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ORCIDNIPS 2016: Barcelona, Spain
- Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett:
Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 2016 - Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. 1-9 - Yan Yang, Jian Sun, Huibin Li, Zongben Xu:
Deep ADMM-Net for Compressive Sensing MRI. 10-18 - Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. 19-27 - Saurabh Singh, Derek Hoiem, David A. Forsyth:
Swapout: Learning an ensemble of deep architectures. 28-36 - Richard Nock:
On Regularizing Rademacher Observation Losses. 37-45 - Ohad Shamir:
Without-Replacement Sampling for Stochastic Gradient Methods. 46-54 - Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause:
Fast and Provably Good Seedings for k-Means. 55-63 - Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. 64-72 - Ehsan Elhamifar:
High-Rank Matrix Completion and Clustering under Self-Expressive Models. 73-81 - Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum:
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. 82-90 - Tianfan Xue, Jiajun Wu, Katherine L. Bouman, Bill Freeman:
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks. 91-99 - Pedro A. Ortega, Alan A. Stocker:
Human Decision-Making under Limited Time. 100-108 - Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong:
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition. 109-117 - Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Natural-Parameter Networks: A Class of Probabilistic Neural Networks. 118-126 - Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Feng Lu, Shuicheng Yan:
Tree-Structured Reinforcement Learning for Sequential Object Localization. 127-135 - Mingsheng Long
, Han Zhu, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. 136-144 - Zohar S. Karnin:
Verification Based Solution for Structured MAB Problems. 145-153 - Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen:
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games. 154-162 - Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham:
Linear dynamical neural population models through nonlinear embeddings. 163-171 - Peng Wang, Xiaohui Shen, Bryan C. Russell, Scott Cohen, Brian L. Price, Alan L. Yuille:
SURGE: Surface Regularized Geometry Estimation from a Single Image. 172-180 - Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. 181-189 - Edouard Pauwels, Jean B. Lasserre:
Sorting out typicality with the inverse moment matrix SOS polynomial. 190-198 - Zohar S. Karnin, Oren Anava:
Multi-armed Bandits: Competing with Optimal Sequences. 199-207 - Ruth Heller, Yair Heller:
Multivariate tests of association based on univariate tests. 208-216 - Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee:
Learning What and Where to Draw. 217-225 - Damek Davis, Brent Edmunds, Madeleine Udell:
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. 226-234 - Hakan Bilen, Andrea Vedaldi:
Integrated perception with recurrent multi-task neural networks. 235-243 - Yu-Xiong Wang, Martial Hebert:
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs. 244-252 - Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu:
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain. 253-261 - Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause:
Cooperative Graphical Models. 262-270 - Sebastian Nowozin, Botond Cseke, Ryota Tomioka:
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. 271-279 - Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. 280-288 - Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh:
Hierarchical Question-Image Co-Attention for Visual Question Answering. 289-297 - Malik Magdon-Ismail, Christos Boutsidis:
Optimal Sparse Linear Encoders and Sparse PCA. 298-306 - Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas:
FPNN: Field Probing Neural Networks for 3D Data. 307-315 - Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang:
CRF-CNN: Modeling Structured Information in Human Pose Estimation. 316-324 - Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. 325-333 - Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. 334-342 - Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. 343-351 - Diane Bouchacourt, Pawan Kumar Mudigonda, Sebastian Nowozin:
DISCO Nets : DISsimilarity COefficients Networks. 352-360 - Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, JungWoo Ha, Byoung-Tak Zhang:
Multimodal Residual Learning for Visual QA. 361-369 - Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel:
CMA-ES with Optimal Covariance Update and Storage Complexity. 370-378 - Jifeng Dai, Yi Li, Kaiming He, Jian Sun:
R-FCN: Object Detection via Region-based Fully Convolutional Networks. 379-387 - Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe Screening Rules for Sparse-Group Lasso. 388-396 - Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez-Rodriguez:
Learning and Forecasting Opinion Dynamics in Social Networks. 397-405 - Rong Zhu:
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares. 406-414 - Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks. 415-423 - Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová:
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula. 424-432 - Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. 433-441 - Junhua Mao, Jiajing Xu, Yushi Jing, Alan L. Yuille:
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. 442-450 - Yiming Ying, Longyin Wen, Siwei Lyu:
Stochastic Online AUC Maximization. 451-459 - Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
The Generalized Reparameterization Gradient. 460-468 - Ming-Yu Liu, Oncel Tuzel:
Coupled Generative Adversarial Networks. 469-477 - Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. 478-486 - Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Variational Information Maximization for Feature Selection. 487-495 - Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. 496-504 - Vu C. Dinh, Lam Si Tung Ho, Binh T. Nguyen, Duy M. H. Nguyen:
Fast learning rates with heavy-tailed losses. 505-513 - Kaito Fujii, Hisashi Kashima:
Budgeted stream-based active learning via adaptive submodular maximization. 514-522 - Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi:
Learning feed-forward one-shot learners. 523-531 - Ting-Yu Cheng, Guiguan Lin, Xinyang Gong, Kang-Jun Liu, Shan-Hung Wu:
Learning User Perceived Clusters with Feature-Level Supervision. 532-540 - Pan Zhang:
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization. 541-549 - Andreas Veit, Michael J. Wilber, Serge J. Belongie:
Residual Networks Behave Like Ensembles of Relatively Shallow Networks. 550-558 - Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart:
Adversarial Multiclass Classification: A Risk Minimization Perspective. 559-567 - Gang Wang, Georgios B. Giannakis:
Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow. 568-576 - Francesco Orabona, Dávid Pál:
Coin Betting and Parameter-Free Online Learning. 577-585 - Kenji Kawaguchi:
Deep Learning without Poor Local Minima. 586-594 - Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko:
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. 595-603 - Dennis Wei:
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++. 604-612 - Carl Vondrick, Hamed Pirsiavash, Antonio Torralba:
Generating Videos with Scene Dynamics. 613-621 - Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman:
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks. 622-630 - Kun He, Yan Wang, John E. Hopcroft:
A Powerful Generative Model Using Random Weights for the Deep Image Representation. 631-639 - Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán:
Optimizing affinity-based binary hashing using auxiliary coordinates. 640-648 - Huasen Wu, Xin Liu:
Double Thompson Sampling for Dueling Bandits. 649-657 - Alexey Dosovitskiy, Thomas Brox:
Generating Images with Perceptual Similarity Metrics based on Deep Networks. 658-666 - Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc Van Gool:
Dynamic Filter Networks. 667-675 - Aaron Defazio:
A Simple Practical Accelerated Method for Finite Sums. 676-684 - Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian:
Barzilai-Borwein Step Size for Stochastic Gradient Descent. 685-693 - Guillaume Papa, Aurélien Bellet, Stéphan Clémençon:
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. 694-702 - Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya:
Optimal spectral transportation with application to music transcription. 703-711 - Damien Scieur, Alexandre d'Aspremont, Francis R. Bach:
Regularized Nonlinear Acceleration. 712-720 - Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros:
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. 721-729 - Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng:
Single-Image Depth Perception in the Wild. 730-738 - Ashish Khetan, Sewoong Oh:
Computational and Statistical Tradeoffs in Learning to Rank. 739-747 - Ashok Cutkosky, Kwabena Boahen:
Online Convex Optimization with Unconstrained Domains and Losses. 748-756 - Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei:
An ensemble diversity approach to supervised binary hashing. 757-765 - Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro:
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. 766-774 - Kevin G. Jamieson, Daniel Haas, Benjamin Recht:
The Power of Adaptivity in Identifying Statistical Alternatives. 775-783 - Aurélien Garivier, Tor Lattimore, Emilie Kaufmann:
On Explore-Then-Commit strategies. 784-792 - Zhao Song, David P. Woodruff, Huan Zhang:
Sublinear Time Orthogonal Tensor Decomposition. 793-801 - Xiangyu Wang, David B. Dunson, Chenlei Leng:
DECOrrelated feature space partitioning for distributed sparse regression. 802-810 - Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao:
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition. 811-819 - Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma:
Dual Learning for Machine Translation. 820-828 - Jason Weston:
Dialog-based Language Learning. 829-837 - Théodore Bluche:
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition. 838-846 - Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon:
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction. 847-855 - Aryeh Kontorovich, Sivan Sabato, Ruth Urner:
Active Nearest-Neighbor Learning in Metric Spaces. 856-864 - Shenlong Wang, Sanja Fidler, Raquel Urtasun:
Proximal Deep Structured Models. 865-873 - Dan Garber:
Faster Projection-free Convex Optimization over the Spectrahedron. 874-882 - Rémi Lam, Karen Willcox, David H. Wolpert:
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. 883-891 - Yusuf Aytar, Carl Vondrick, Antonio Torralba:
SoundNet: Learning Sound Representations from Unlabeled Video. 892-900 - Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. 901 - Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning by Sketching. 902-910 - Yoshinobu Kawahara:
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis. 911-919 - Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani:
Distributed Flexible Nonlinear Tensor Factorization. 920-928 - Pingfan Tang, Jeff M. Phillips:
The Robustness of Estimator Composition. 929-937 - Bipin Rajendran, Pulkit Tandon, Yash H. Malviya:
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats. 938-946 - Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli:
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. 947-955 - Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa:
Differential Privacy without Sensitivity. 956-964 - Se-Young Yun, Alexandre Proutière:
Optimal Cluster Recovery in the Labeled Stochastic Block Model. 965-973 - Zeyuan Allen Zhu, Yuanzhi Li:
Even Faster SVD Decomposition Yet Without Agonizing Pain. 974-982 - Xinan Wang, Sanjoy Dasgupta:
An algorithm for L1 nearest neighbor search via monotonic embedding. 983-991 - Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. 992-1000 - Dan Garber, Ofer Meshi:
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. 1001-1009 - Shashank Singh, Simon S. Du, Barnabás Póczos:
Efficient Nonparametric Smoothness Estimation. 1010-1018 - Yarin Gal, Zoubin Ghahramani:
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. 1019-1027 - George Papamakarios, Iain Murray:
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation. 1028-1036 - Arild Nøkland:
Direct Feedback Alignment Provides Learning in Deep Neural Networks. 1037-1045 - Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare:
Safe and Efficient Off-Policy Reinforcement Learning. 1046-1054 - Albert S. Berahas, Jorge Nocedal, Martin Takác:
A Multi-Batch L-BFGS Method for Machine Learning. 1055-1063 - Pan Xu, Quanquan Gu:
Semiparametric Differential Graph Models. 1064-1072 - Yingzhen Li, Richard E. Turner:
Rényi Divergence Variational Inference. 1073-1081 - Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang, Weining Lu:
Doubly Convolutional Neural Networks. 1082-1090 - Dangna Li, Kun Yang, Wing Hung Wong:
Density Estimation via Discrepancy Based Adaptive Sequential Partition. 1091-1099 - Sven Eberhardt, Jonah G. Cader, Thomas Serre:
How Deep is the Feature Analysis underlying Rapid Visual Categorization? 1100-1108 - Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
VIME: Variational Information Maximizing Exploration. 1109-1117 - Timothy N. Rubin, Oluwasanmi Koyejo, Michael N. Jones, Tal Yarkoni:
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain. 1118-1126 - Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman:
Solving Marginal MAP Problems with NP Oracles and Parity Constraints. 1127-1135 - Tomoharu Iwata, Makoto Yamada:
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models. 1136-1144 - Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. 1145-1153 - Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. 1154-1162 - Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen:
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. 1163-1171 - Dmitry Krotov, John J. Hopfield:
Dense Associative Memory for Pattern Recognition. 1172-1180 - Finnian Lattimore, Tor Lattimore, Mark D. Reid:
Causal Bandits: Learning Good Interventions via Causal Inference. 1181-1189 - Sébastien Gerchinovitz, Tor Lattimore:
Refined Lower Bounds for Adversarial Bandits. 1190-1198 - Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama:
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. 1199-1207 - Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang:
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). 1208-1216 - Shashank Singh, Barnabás Póczos:
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators. 1217-1225 - Ying Yang, Elissa Aminoff, Michael J. Tarr, Robert E. Kass:
A state-space model of cross-region dynamic connectivity in MEG/EEG. 1226-1234 - Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou:
What Makes Objects Similar: A Unified Multi-Metric Learning Approach. 1235-1243 - Nguyen Cuong, Huan Xu:
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint. 1244-1252 - Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal:
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions. 1253-1261 - Chen Huang, Chen Change Loy, Xiaoou Tang:
Local Similarity-Aware Deep Feature Embedding. 1262-1270 - Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu:
A Communication-Efficient Parallel Algorithm for Decision Tree. 1271-1279 - Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen:
Convex Two-Layer Modeling with Latent Structure. 1280-1288 - Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum:
Sampling for Bayesian Program Learning. 1289-1297 - Aman Sinha, John C. Duchi:
Learning Kernels with Random Features. 1298-1306 - Nir Rosenfeld, Amir Globerson:
Optimal Tagging with Markov Chain Optimization. 1307-1315 - Ramya Korlakai Vinayak, Babak Hassibi:
Crowdsourced Clustering: Querying Edges vs Triangles. 1316-1324 - Arno Onken, Stefano Panzeri:
Mixed vine copulas as joint models of spike counts and local field potentials. 1325-1333 - Emmanuel Abbe, Colin Sandon:
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation. 1334-1342 - Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon:
Adaptive Concentration Inequalities for Sequential Decision Problems. 1343-1351 - James Newling, François Fleuret:
Nested Mini-Batch K-Means. 1352-1360 - Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus:
Deep Learning Models of the Retinal Response to Natural Scenes. 1361-1369 - Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh:
Preference Completion from Partial Rankings. 1370-1378 - Yiwen Guo, Anbang Yao, Yurong Chen:
Dynamic Network Surgery for Efficient DNNs. 1379-1387 - Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua:
Learning a Metric Embedding for Face Recognition using the Multibatch Method. 1388-1389 - Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf:
A Pseudo-Bayesian Algorithm for Robust PCA. 1390-1398 - Julien Mairal:
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks. 1399-1407 - P. Balamurugan, Francis R. Bach:
Stochastic Variance Reduction Methods for Saddle-Point Problems. 1408-1416 - Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna M. Wallach, Abbas Zaidi, Beka Steorts:
Flexible Models for Microclustering with Application to Entity Resolution. 1417-1425 - Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan Peters:
Catching heuristics are optimal control policies. 1426-1434 - Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sébastien Le Digabel:
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian. 1435-1443 - Rudy Bunel, Alban Desmaison, Pawan Kumar Mudigonda, Pushmeet Kohli, Philip H. S. Torr:
Adaptive Neural Compilation. 1444-1452 - Sungsoo Ahn, Michael Chertkov, Jinwoo Shin:
Synthesis of MCMC and Belief Propagation. 1453-1461 - Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon:
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables. 1462-1470 - Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos:
Unifying Count-Based Exploration and Intrinsic Motivation. 1471-1479 - Mohammad J. Saberian, José Costa Pereira, Nuno Vasconcelos, Can Xu:
Large Margin Discriminant Dimensionality Reduction in Prediction Space. 1480-1488 - Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo:
Stochastic Structured Prediction under Bandit Feedback. 1489-1497 - Anshumali Shrivastava:
Simple and Efficient Weighted Minwise Hashing. 1498-1506 - Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher:
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation. 1507-1515 - Prateek Jain, Nikhil Rao, Inderjit S. Dhillon:
Structured Sparse Regression via Greedy Hard Thresholding. 1516-1524 - Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen:
Understanding Probabilistic Sparse Gaussian Process Approximations. 1525-1533 - Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky:
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques. 1534-1542 - Stephan Zheng, Yisong Yue, Jennifer A. Hobbs:
Generating Long-term Trajectories Using Deep Hierarchical Networks. 1543-1551 - Vikas K. Garg, Tommi S. Jaakkola:
Learning Tree Structured Potential Games. 1552-1560 - Ricardo Silva:
Observational-Interventional Priors for Dose-Response Learning. 1561-1569 - Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. 1570-1578 - Bryant Chen:
Identification and Overidentification of Linear Structural Equation Models. 1579-1587 - Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Adaptive Skills Adaptive Partitions (ASAP). 1588-1596 - Paul Lagrée, Claire Vernade, Olivier Cappé:
Multiple-Play Bandits in the Position-Based Model. 1597-1605 - Zeyuan Allen Zhu, Elad Hazan:
Optimal Black-Box Reductions Between Optimization Objectives. 1606-1614 - Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson:
On Valid Optimal Assignment Kernels and Applications to Graph Classification. 1615-1623 - Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard:
Robustness of classifiers: from adversarial to random noise. 1624-1632 - Ming Lin, Jieping Ye:
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing. 1633-1641 - Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan:
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. 1642-1650 - Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu:
Combinatorial Multi-Armed Bandit with General Reward Functions. 1651-1659 - Corinna Cortes, Giulia DeSalvo, Mehryar Mohri:
Boosting with Abstention. 1660-1668 - Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai:
Regret of Queueing Bandits. 1669-1677 - Dale Schuurmans, Martin Zinkevich:
Deep Learning Games. 1678-1686 - Antoine Gautier, Quynh Nguyen, Matthias Hein:
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. 1687-1695 - Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee:
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision. 1696-1704 - Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford:
A Credit Assignment Compiler for Joint Prediction. 1705-1713 - Mengdi Wang, Ji Liu, Ethan X. Fang:
Accelerating Stochastic Composition Optimization. 1714-1722 - Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans:
Reward Augmented Maximum Likelihood for Neural Structured Prediction. 1723-1731 - Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf:
Consistent Kernel Mean Estimation for Functions of Random Variables. 1732-1740 - Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin:
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling. 1741-1749 - Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen:
Scalable Adaptive Stochastic Optimization Using Random Projections. 1750-1758 - Josip Djolonga, Sebastian Tschiatschek, Andreas Krause:
Variational Inference in Mixed Probabilistic Submodular Models. 1759-1767 - Namrata Vaswani, Han Guo:
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated. 1768-1776 - Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider:
The Multi-fidelity Multi-armed Bandit. 1777-1785 - Kejun Huang, Xiao Fu, Nikos D. Sidiropoulos:
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm. 1786-1794 - Jun Han, Qiang Liu:
Bootstrap Model Aggregation for Distributed Statistical Learning. 1795-1803 - Steven Cheng-Xian Li, Benjamin M. Marlin:
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification. 1804-1812 - Yang Liu, Yiling Chen:
A Bandit Framework for Strategic Regression. 1813-1821 - Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio:
Architectural Complexity Measures of Recurrent Neural Networks. 1822-1830 - Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman:
Statistical Inference for Cluster Trees. 1831-1839 - Akshay Krishnamurthy, Alekh Agarwal, John Langford:
PAC Reinforcement Learning with Rich Observations. 1840-1848 - Kihyuk Sohn:
Improved Deep Metric Learning with Multi-class N-pair Loss Objective. 1849-1857 - David F. Harwath, Antonio Torralba, James R. Glass:
Unsupervised Learning of Spoken Language with Visual Context. 1858-1866 - Guillaume Rabusseau, Hachem Kadri:
Low-Rank Regression with Tensor Responses. 1867-1875 - Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. 1876-1884 - Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik:
Data Poisoning Attacks on Factorization-Based Collaborative Filtering. 1885-1893 - Zijun Wei, Hossein Adeli, Minh Hoai, Gregory J. Zelinsky, Dimitris Samaras:
Learned Region Sparsity and Diversity Also Predicts Visual Attention. 1894-1902 - Rodrigo Nogueira, Kyunghyun Cho:
End-to-End Goal-Driven Web Navigation. 1903-1911 - Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski:
Automated scalable segmentation of neurons from multispectral images. 1912-1920 - Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman:
Privacy Odometers and Filters: Pay-as-you-Go Composition. 1921-1929 - Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. 1930-1938 - Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow:
Adaptive optimal training of animal behavior. 1939-1947 - Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes:
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition. 1948-1956 - Matthew Chalk, Olivier Marre, Gasper Tkacik:
Relevant sparse codes with variational information bottleneck. 1957-1965 - Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel:
Combinatorial Energy Learning for Image Segmentation. 1966-1974 - Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar:
Orthogonal Random Features. 1975-1983 - Johannes Friedrich, Liam Paninski:
Fast Active Set Methods for Online Spike Inference from Calcium Imaging. 1984-1992 - James Atwood, Don Towsley:
Diffusion-Convolutional Neural Networks. 1993-2001 - Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow:
Bayesian latent structure discovery from multi-neuron recordings. 2002-2010 - Feras Saad, Vikash K. Mansinghka:
A Probabilistic Programming Approach To Probabilistic Data Analysis. 2011-2019 - William Hoiles, Mihaela van der Schaar:
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics. 2020-2028 - Rajkumar Vasudeva Raju, Xaq Pitkow:
Inference by Reparameterization in Neural Population Codes. 2029-2037 - Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox:
Tensor Switching Networks. 2038-2046 - Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard:
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. 2047-2055 - Qi Lei, Kai Zhong, Inderjit S. Dhillon:
Coordinate-wise Power Method. 2056-2064 - Xinran He, Ke Xu, David Kempe, Yan Liu:
Learning Influence Functions from Incomplete Observations. 2065-2073 - Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li:
Learning Structured Sparsity in Deep Neural Networks. 2074-2082 - Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik:
Sample Complexity of Automated Mechanism Design. 2083-2091 - Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. 2092-2100 - Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven, Marcel A. J. van Gerven:
Brains on Beats. 2101-2109 - Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese:
Learning Transferrable Representations for Unsupervised Domain Adaptation. 2110-2118 - Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David J. Crandall, Dhruv Batra:
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles. 2119-2127 - Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active Learning from Imperfect Labelers. 2128-2136 - Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning. 2137-2145 - Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, Garrett Thomas:
Value Iteration Networks. 2146-2154 - Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah:
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering. 2155-2163 - Xi Chen, Yu Cheng, Bo Tang:
On the Recursive Teaching Dimension of VC Classes. 2164-2171 - Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. 2172-2180 - Satyen Kale, Chansoo Lee, Dávid Pál:
Hardness of Online Sleeping Combinatorial Optimization Problems. 2181-2189 - Kai Zhong, Prateek Jain, Inderjit S. Dhillon:
Mixed Linear Regression with Multiple Components. 2190-2198 - Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther:
Sequential Neural Models with Stochastic Layers. 2199-2207 - Hongseok Namkoong, John C. Duchi:
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. 2208-2216 - Kohei Hayashi, Yuichi Yoshida:
Minimizing Quadratic Functions in Constant Time. 2217-2225 - Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen:
Improved Techniques for Training GANs. 2226-2234 - Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Eén, François Chollet, Josef Urban:
DeepMath - Deep Sequence Models for Premise Selection. 2235-2243 - Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus:
Learning Multiagent Communication with Backpropagation. 2244-2252 - Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. 2253-2261 - José M. Álvarez, Mathieu Salzmann:
Learning the Number of Neurons in Deep Networks. 2262-2270 - Laetitia Papaxanthos, Felipe Llinares-López, Dean A. Bodenham, Karsten M. Borgwardt:
Finding significant combinations of features in the presence of categorical covariates. 2271-2279 - Been Kim, Oluwasanmi Koyejo, Rajiv Khanna:
Examples are not enough, learn to criticize! Criticism for Interpretability. 2280-2288 - Scott Yang, Mehryar Mohri:
Optimistic Bandit Convex Optimization. 2289-2297 - Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow:
Safe Policy Improvement by Minimizing Robust Baseline Regret. 2298-2306 - Justin Eldridge, Mikhail Belkin, Yusu Wang:
Graphons, mergeons, and so on! 2307-2315 - Aurko Roy, Sebastian Pokutta:
Hierarchical Clustering via Spreading Metrics. 2316-2324 - Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche:
Learning Bayesian networks with ancestral constraints. 2325-2333 - Feng Nan, Joseph Wang, Venkatesh Saligrama:
Pruning Random Forests for Prediction on a Budget. 2334-2342 - Chaoyue Liu, Mikhail Belkin:
Clustering with Bregman Divergences: an Asymptotic Analysis. 2343-2351 - Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin:
Variational Autoencoder for Deep Learning of Images, Labels and Captions. 2352-2360 - Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan Salakhutdinov:
Review Networks for Caption Generation. 2361-2369 - Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. 2370-2378 - Anh Tuan Nguyen, Jian Xu, Zhi Yang:
A Bio-inspired Redundant Sensing Architecture. 2379-2387 - Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Contextual semibandits via supervised learning oracles. 2388-2396 - Alex Beatson, Zhaoran Wang, Han Liu:
Blind Attacks on Machine Learners. 2397-2405 - Christopher B. Choy, JunYoung Gwak, Silvio Savarese, Manmohan Krishna Chandraker:
Universal Correspondence Network. 2406-2414 - Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander:
Satisfying Real-world Goals with Dataset Constraints. 2415-2423 - Jason S. Hartford, James R. Wright, Kevin Leyton-Brown:
Deep Learning for Predicting Human Strategic Behavior. 2424-2432 - Sougata Chaudhuri, Ambuj Tewari:
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games. 2433-2441 - Chien-Ju Ho, Rafael M. Frongillo, Yiling Chen:
Eliciting Categorical Data for Optimal Aggregation. 2442-2450 - Roger B. Grosse, Siddharth Ancha, Daniel M. Roy:
Measuring the reliability of MCMC inference with bidirectional Monte Carlo. 2451-2459 - Weihao Gao, Sewoong Oh, Pramod Viswanath:
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation. 2460-2468 - Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty:
Selective inference for group-sparse linear models. 2469-2477 - Yali Wan, Marina Meila:
Graph Clustering: Block-models and model free results. 2478-2486 - Christopher Lynn, Daniel D. Lee:
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution. 2487-2495 - Hao Henry Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson:
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease. 2496-2504 - Mikhail Yurochkin, XuanLong Nguyen:
Geometric Dirichlet Means Algorithm for topic inference. 2505-2513 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang:
Structured Prediction Theory Based on Factor Graph Complexity. 2514-2522 - Zhe Li, Boqing Gong, Tianbao Yang:
Improved Dropout for Shallow and Deep Learning. 2523-2531 - Yaniv Tenzer, Alexander G. Schwing, Kevin Gimpel, Tamir Hazan:
Constraints Based Convex Belief Propagation. 2532-2540 - Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai:
Error Analysis of Generalized Nyström Kernel Regression. 2541-2549 - Ankit B. Patel, Minh Tan Nguyen, Richard G. Baraniuk:
A Probabilistic Framework for Deep Learning. 2550-2558 - Tao Wu, Austin R. Benson, David F. Gleich:
General Tensor Spectral Co-clustering for Higher-Order Data. 2559-2567 - Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. 2577-2585 - Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Stochastic Variational Deep Kernel Learning. 2586-2594 - Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov:
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models. 2595-2603 - Panagiotis Toulis, David C. Parkes:
Long-term Causal Effects via Behavioral Game Theory. 2604-2612 - Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya V. Nori, Antonio Criminisi:
Measuring Neural Net Robustness with Constraints. 2613-2621 - Huishuai Zhang, Yingbin Liang:
Reshaped Wirtinger Flow for Solving Quadratic System of Equations. 2622-2630 - James McQueen, Marina Meila, Dominique Joncas:
Nearly Isometric Embedding by Relaxation. 2631-2639 - Kevin Winner, Daniel Sheldon:
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models. 2640-2648 - Yuxun Zhou, Costas J. Spanos:
Causal meets Submodular: Subset Selection with Directed Information. 2649-2657 - Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich:
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions. 2658-2666 - Arulkumar Subramaniam, Moitreya Chatterjee, Anurag Mittal:
Deep Neural Networks with Inexact Matching for Person Re-Identification. 2667-2675 - Ji Xu, Daniel J. Hsu, Arian Maleki:
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians. 2676-2684 - Shantanu Jain, Martha White, Predrag Radivojac:
Estimating the class prior and posterior from noisy positives and unlabeled data. 2685-2693 - Zelda E. Mariet, Suvrit Sra:
Kronecker Determinantal Point Processes. 2694-2702 - Lalit Jain, Kevin G. Jamieson, Robert D. Nowak:
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. 2703-2711 - Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun:
Feature-distributed sparse regression: a screen-and-clean approach. 2712-2720 - Wataru Kumagai:
Learning Bound for Parameter Transfer Learning. 2721-2729 - He Huang, Martin P. Paulus:
Learning under uncertainty: a comparison between R-W and Bayesian approach. 2730-2738 - Hossein Esfandiari, Nitish Korula, Vahab S. Mirrokni:
Bi-Objective Online Matching and Submodular Allocations. 2739-2747 - Ping Li, Michael Mitzenmacher, Martin Slawski:
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity. 2748-2756 - Nicolas Boumal, Vladislav Voroninski, Afonso S. Bandeira:
The non-convex Burer-Monteiro approach works on smooth semidefinite programs. 2757-2765 - Dan Feldman, Mikhail Volkov, Daniela Rus:
Dimensionality Reduction of Massive Sparse Datasets Using Coresets. 2766-2774 - Zhen Xu, Wen Dong, Sargur N. Srihari:
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model. 2775-2783 - Ofir David, Shay Moran, Amir Yehudayoff:
Supervised learning through the lens of compression. 2784-2792 - Xinghua Lou, Ken Kansky, Wolfgang Lehrach, C. C. Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George:
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data. 2793-2801 - Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang:
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections. 2802-2810 - Mandar Dixit, Nuno Vasconcelos:
Object based Scene Representations using Fisher Scores of Local Subspace Projections. 2811-2819 - Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu:
Active Learning with Oracle Epiphany. 2820-2828 - Ming Yu, Mladen Kolar, Varun Gupta:
Statistical Inference for Pairwise Graphical Models Using Score Matching. 2829-2837 - Samir Chowdhury, Facundo Mémoli, Zane T. Smith:
Improved Error Bounds for Tree Representations of Metric Spaces. 2838-2846 - Arturo Deza, Miguel P. Eckstein:
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? 2847-2855 - Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov:
On Multiplicative Integration with Recurrent Neural Networks. 2856-2864 - Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing:
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. 2865-2873 - Nagarajan Natarajan, Prateek Jain:
Regret Bounds for Non-decomposable Metrics with Missing Labels. 2874-2882 - Alexandros Georgogiannis:
Robust k-means: a Theoretical Revisit. 2883-2891 - Gustavo Malkomes, Chip Schaff, Roman Garnett:
Bayesian optimization for automated model selection. 2892-2900 - Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao:
A Probabilistic Model of Social Decision Making based on Reward Maximization. 2901-2909 - Ahmed M. Alaa, Mihaela van der Schaar:
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition. 2910-2918 - Mahdi Milani Fard, Kevin Robert Canini, Andrew Cotter, Jan Pfeifer, Maya R. Gupta:
Fast and Flexible Monotonic Functions with Ensembles of Lattices. 2919-2927 - Yong Ren, Jun Zhu, Jialian Li, Yucen Luo:
Conditional Generative Moment-Matching Networks. 2928-2936 - Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin:
Stochastic Gradient MCMC with Stale Gradients. 2937-2945 - Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta:
Composing graphical models with neural networks for structured representations and fast inference. 2946-2954 - Maria-Florina Balcan, Hongyang Zhang:
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling. 2955-2963 - Rémy Degenne, Vianney Perchet:
Combinatorial semi-bandit with known covariance. 2964-2972 - Rong Ge, Jason D. Lee, Tengyu Ma:
Matrix Completion has No Spurious Local Minimum. 2973-2981 - Risi Kondor, Horace Pan:
The Multiscale Laplacian Graph Kernel. 2982-2990 - Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett:
Adaptive Averaging in Accelerated Descent Dynamics. 2991-2999 - Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney:
Sub-sampled Newton Methods with Non-uniform Sampling. 3000-3008 - Chang Liu, Jun Zhu, Yang Song:
Stochastic Gradient Geodesic MCMC Methods. 3009-3017 - Aditya Grover, Stefano Ermon:
Variational Bayes on Monte Carlo Steroids. 3018-3026 - Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil:
Showing versus doing: Teaching by demonstration. 3027-3035 - Jianxu Chen, Lin Yang, Yizhe Zhang, Mark S. Alber, Danny Ziyi Chen:
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation. 3036-3044 - Thibaut Horel, Yaron Singer:
Maximization of Approximately Submodular Functions. 3045-3053 - Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu:
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order. 3054-3062 - Wei Ping, Qiang Liu, Alexander Ihler:
Learning Infinite RBMs with Frank-Wolfe. 3063-3071 - Lin Chen, Amin Karbasi, Forrest W. Crawford:
Estimating the Size of a Large Network and its Communities from a Random Sample. 3072-3080 - Ayan Chakrabarti:
Learning Sensor Multiplexing Design through Back-propagation. 3081-3089 - Bowei Yan, Purnamrita Sarkar:
On Robustness of Kernel Clustering. 3090-3098 - Kameron Decker Harris, Stefan Mihalas, Eric Shea-Brown:
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression. 3099-3107 - Grégory Rogez, Cordelia Schmid:
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild. 3108-3116 - Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole:
New Liftable Classes for First-Order Probabilistic Inference. 3117-3125 - Jian Wu, Peter I. Frazier:
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization. 3126-3134 - Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. 3135-3143 - Ilya Shpitser:
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random. 3144-3152 - Eli Gutin, Vivek F. Farias:
Optimistic Gittins Indices. 3153-3161 - Juho Lee, Lancelot F. James, Seungjin Choi:
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models. 3162-3170 - Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta:
Launch and Iterate: Reducing Prediction Churn. 3171-3179 - Wenhao Zhang, He Wang, K. Y. Michael Wong, Si Wu:
"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation. 3180-3188 - Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein:
Learning shape correspondence with anisotropic convolutional neural networks. 3189-3197 - Stephen Ragain, Johan Ugander:
Pairwise Choice Markov Chains. 3198-3206 - Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang:
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization. 3207-3215 - Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David:
Clustering with Same-Cluster Queries. 3216-3224 - S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton:
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. 3225-3233 - Tatiana Shpakova, Francis R. Bach:
Parameter Learning for Log-supermodular Distributions. 3234-3242 - Ayan Sinha, David F. Gleich, Karthik Ramani:
Deconvolving Feedback Loops in Recommender Systems. 3243-3251 - Sheng Chen, Arindam Banerjee:
Structured Matrix Recovery via the Generalized Dantzig Selector. 3252-3260 - Himabindu Lakkaraju, Jure Leskovec:
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making. 3261-3269 - Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung:
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks. 3270-3278 - Reza Eghbali, Maryam Fazel:
Designing smoothing functions for improved worst-case competitive ratio in online optimization. 3279-3287 - Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence guarantees for kernel-based quadrature rules in misspecified settings. 3288-3296 - Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje:
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data. 3297-3305 - Elad Hazan, Tengyu Ma:
A Non-generative Framework and Convex Relaxations for Unsupervised Learning. 3306-3314 - Moritz Hardt, Eric Price, Nati Srebro:
Equality of Opportunity in Supervised Learning. 3315-3323 - Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati:
Scaled Least Squares Estimator for GLMs in Large-Scale Problems. 3324-3332 - Yuan Zhao, Il Memming Park:
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories. 3333-3341 - Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang:
Search Improves Label for Active Learning. 3342-3350 - Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata:
Higher-Order Factorization Machines. 3351-3359 - Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli:
Exponential expressivity in deep neural networks through transient chaos. 3360-3368 - Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao:
Split LBI: An Iterative Regularization Path with Structural Sparsity. 3369-3377 - Madhu Advani, Surya Ganguli:
An equivalence between high dimensional Bayes optimal inference and M-estimation. 3378-3386 - Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune:
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. 3387-3395 - Brian W. Dolhansky, Jeff A. Bilmes:
Deep Submodular Functions: Definitions and Learning. 3396-3404 - Mathias Niepert:
Discriminative Gaifman Models. 3405-3413 - Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis:
Leveraging Sparsity for Efficient Submodular Data Summarization. 3414-3422 - Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu:
Local Minimax Complexity of Stochastic Convex Optimization. 3423-3431 - Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach:
Stochastic Optimization for Large-scale Optimal Transport. 3432-3440 - Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii:
On Mixtures of Markov Chains. 3441-3449 - Shipra Agrawal, Nikhil R. Devanur:
Linear Contextual Bandits with Knapsacks. 3450-3458 - Andrey Y. Lokhov:
Reconstructing Parameters of Spreading Models from Partial Observations. 3459-3467 - Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes:
Spatiotemporal Residual Networks for Video Action Recognition. 3468-3476 - Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro:
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. 3477-3485 - Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John P. Agapiou, Koray Kavukcuoglu:
Strategic Attentive Writer for Learning Macro-Actions. 3486-3494 - Brian Bullins, Elad Hazan, Tomer Koren:
The Limits of Learning with Missing Data. 3495-3503 - Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter F. Stewart:
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. 3504-3512 - Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. 3513-3521 - Aris Anagnostopoulos, Jakub Lacki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian:
Community Detection on Evolving Graphs. 3522-3530 - Yining Wang, Anima Anandkumar:
Online and Differentially-Private Tensor Decomposition. 3531-3539 - Yossi Arjevani, Ohad Shamir:
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems. 3540-3548 - Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra:
Towards Conceptual Compression. 3549-3557 - Xiao-Tong Yuan, Ping Li, Tong Zhang:
Exact Recovery of Hard Thresholding Pursuit. 3558-3566 - Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré:
Data Programming: Creating Large Training Sets, Quickly. 3567-3575 - Vitaly Feldman:
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back. 3576-3584 - Liangbei Xu, Mark A. Davenport:
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint. 3585-3593 - Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi:
Fast Distributed Submodular Cover: Public-Private Data Summarization. 3594-3602 - Cristina Savin, Gasper Tkacik:
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods. 3603-3611 - Anastasia Pentina, Ruth Urner:
Lifelong Learning with Weighted Majority Votes. 3612-3620 - Jack W. Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap:
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. 3621-3629 - Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra:
Matching Networks for One Shot Learning. 3630-3638 - Blake E. Woodworth, Nati Srebro:
Tight Complexity Bounds for Optimizing Composite Objectives. 3639-3647 - Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu:
Graphical Time Warping for Joint Alignment of Multiple Curves. 3648-3656 - Jacob Steinhardt, Percy Liang:
Unsupervised Risk Estimation Using Only Conditional Independence Structure. 3657-3665 - Tim van Erven, Wouter M. Koolen:
MetaGrad: Multiple Learning Rates in Online Learning. 3666-3674 - Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum:
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. 3675-3683 - Qilong Gu, Arindam Banerjee:
High Dimensional Structured Superposition Models. 3684-3692 - Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc:
Joint quantile regression in vector-valued RKHSs. 3693-3701 - Kieran Milan, Joel Veness, James Kirkpatrick, Michael H. Bowling, Anna Koop, Demis Hassabis:
The Forget-me-not Process. 3702-3710 - Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi:
Wasserstein Training of Restricted Boltzmann Machines. 3711-3719 - Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang:
Communication-Optimal Distributed Clustering. 3720-3728 - Eric Schulz, Josh Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman:
Probing the Compositionality of Intuitive Functions. 3729-3737 - Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther:
Ladder Variational Autoencoders. 3738-3746 - Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani:
The Multiple Quantile Graphical Model. 3747-3755 - Nathan F. Lepora:
Threshold Learning for Optimal Decision Making. 3756-3764 - Aapo Hyvärinen, Hiroshi Morioka:
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. 3765-3773 - Lukasz Kaiser, Samy Bengio:
Can Active Memory Replace Attention? 3774-3782 - Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami:
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. 3783-3791 - Thomas Laurent, James H. von Brecht, Xavier Bresson, Arthur Szlam:
The Product Cut. 3792-3800 - Mohammad Javad Hosseini, Su-In Lee:
Learning Sparse Gaussian Graphical Models with Overlapping Blocks. 3801-3809 - Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet Talwalkar:
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. 3810-3818 - Tengyao Wang, Quentin Berthet, Yaniv Plan:
Average-case hardness of RIP certification. 3819-3827 - Ivan Herreros, Xerxes D. Arsiwalla, Paul F. M. J. Verschure:
A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control. 3828-3836 - Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst:
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. 3837-3845 - Miguel Ángel Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Björn Ommer:
CliqueCNN: Deep Unsupervised Exemplar Learning. 3846-3854 - Shinji Ito, Ryohei Fujimaki:
Large-Scale Price Optimization via Network Flow. 3855-3863 - Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar:
Online Pricing with Strategic and Patient Buyers. 3864-3872 - Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. 3873-3881 - Daniel Neil, Michael Pfeiffer, Shih-Chii Liu:
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. 3882-3890 - Jason Pazis, Ronald Parr, Jonathan P. How:
Improving PAC Exploration Using the Median Of Means. 3891-3899 - Peng Lin, Bang Zhang, Ting Guo, Yang Wang, Fang Chen:
Infinite Hidden Semi-Markov Modulated Interaction Point Process. 3900-3908 - Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel, Anca D. Dragan:
Cooperative Inverse Reinforcement Learning. 3909-3917 - Ransalu Senanayake, Lionel Ott, Simon Timothy O'Callaghan, Fabio Tozeto Ramos:
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments. 3918-3926 - Abdul-Saboor Sheikh, Jörg Lücke:
Select-and-Sample for Spike-and-Slab Sparse Coding. 3927-3935 - Yujia Shen, Arthur Choi, Adnan Darwiche:
Tractable Operations for Arithmetic Circuits of Probabilistic Models. 3936-3944 - Dino Oglic, Thomas Gärtner:
Greedy Feature Construction. 3945-3953 - Mark Herbster, Stephen Pasteris, Massimiliano Pontil:
Mistake Bounds for Binary Matrix Completion. 3954-3962 - Frédéric Chazal, Ilaria Giulini, Bertrand Michel:
Data driven estimation of Laplace-Beltrami operator. 3963-3971 - Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu:
Tracking the Best Expert in Non-stationary Stochastic Environments. 3972-3980 - Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas:
Learning to learn by gradient descent by gradient descent. 3981-3989 - Hassan A. Kingravi, Harshal R. Maske, Girish Chowdhary:
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes. 3990-3998 - Ashish Kapoor, Nathan Wiebe, Krysta M. Svore:
Quantum Perceptron Models. 3999-4007 - William H. Montgomery, Sergey Levine:
Guided Policy Search via Approximate Mirror Descent. 4008-4016 - Eric Balkanski, Aviad Rubinstein, Yaron Singer:
The Power of Optimization from Samples. 4017-4025 - Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy:
Deep Exploration via Bootstrapped DQN. 4026-4034 - Jingwei Liang, Jalal Fadili, Gabriel Peyré:
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization. 4035-4043 - Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva:
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. 4044-4052 - Shreyas Saxena, Jakob Verbeek:
Convolutional Neural Fabrics. 4053-4061 - Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro:
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. 4062-4070 - Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi:
A Sparse Interactive Model for Matrix Completion with Side Information. 4071-4079 - Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. 4080-4088 - Matey Neykov, Zhaoran Wang, Han Liu:
Agnostic Estimation for Misspecified Phase Retrieval Models. 4089-4097 - Aditya Bhaskara, Mehrdad Ghadiri, Vahab S. Mirrokni, Ola Svensson:
Linear Relaxations for Finding Diverse Elements in Metric Spaces. 4098-4106 - Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Binarized Neural Networks. 4107-4115 - Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan:
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. 4116-4124 - Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves:
Memory-Efficient Backpropagation Through Time. 4125-4133 - Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter:
Bayesian Optimization with Robust Bayesian Neural Networks. 4134-4142 - Oleg Grinchuk, Vadim Lebedev, Victor S. Lempitsky:
Learnable Visual Markers. 4143-4151 - Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis:
Fast Algorithms for Robust PCA via Gradient Descent. 4152-4160 - Michalis K. Titsias:
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities. 4161-4169 - Evgeniya Ustinova, Victor S. Lempitsky:
Learning Deep Embeddings with Histogram Loss. 4170-4178 - Hao Wu, Frank Noé:
Spectral Learning of Dynamic Systems from Nonequilibrium Data. 4179-4187 - Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. 4188-4196 - Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard:
Mapping Estimation for Discrete Optimal Transport. 4197-4205 - Tarun Kathuria, Amit Deshpande, Pushmeet Kohli:
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes. 4206-4214 - Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers:
Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images. 4215-4223 - Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin:
Linear Feature Encoding for Reinforcement Learning. 4224-4232 - Farzan Farnia, David Tse:
A Minimax Approach to Supervised Learning. 4233-4241 - Diana Cai, Trevor Campbell, Tamara Broderick:
Edge-exchangeable graphs and sparsity. 4242-4250 - Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg:
A Locally Adaptive Normal Distribution. 4251-4259 - Tue Herlau, Mikkel N. Schmidt, Morten Mørup:
Completely random measures for modelling block-structured sparse networks. 4260-4268 - Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques:
Sparse Support Recovery with Non-smooth Loss Functions. 4269-4277 - Travis Monk, Cristina Savin, Jörg Lücke:
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. 4278-4286 - Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver:
Learning values across many orders of magnitude. 4287-4295 - Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang:
Adaptive Smoothed Online Multi-Task Learning. 4296-4304 - Matteo Turchetta, Felix Berkenkamp, Andreas Krause:
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. 4305-4313 - Onur Teymur, Konstantinos Zygalakis, Ben Calderhead:
Probabilistic Linear Multistep Methods. 4314-4321 - Alp Yurtsever, Bang Công Vu, Volkan Cevher:
Stochastic Three-Composite Convex Minimization. 4322-4330 - Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu:
Using Fast Weights to Attend to the Recent Past. 4331-4339 - Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang:
Maximal Sparsity with Deep Networks? 4340-4348 - Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Tauman Kalai:
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. 4349-4357 - Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. 4358-4366 - Xiao-Tong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu:
Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation. 4367-4375 - Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel:
Backprop KF: Learning Discriminative Deterministic State Estimators. 4376-4384 - Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu:
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks. 4385-4393 - Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Fast recovery from a union of subspaces. 4394-4402 - Ching-An Cheng, Byron Boots:
Incremental Variational Sparse Gaussian Process Regression. 4403-4411 - Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi:
A Consistent Regularization Approach for Structured Prediction. 4412-4420 - Pedro Mercado, Francesco Tudisco, Matthias Hein:
Clustering Signed Networks with the Geometric Mean of Laplacians. 4421-4429 - Víctor Soto, Alberto Suárez, Gonzalo Martínez-Muñoz:
An urn model for majority voting in classification ensembles. 4430-4438 - Jacob Steinhardt, Gregory Valiant, Moses Charikar:
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction. 4439-4447 - Marius Pachitariu, Nicholas A. Steinmetz, Shabnam N. Kadir, Matteo Carandini, Kenneth D. Harris:
Fast and accurate spike sorting of high-channel count probes with KiloSort. 4448-4456 - Wouter M. Koolen, Peter Grünwald, Tim van Erven:
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. 4457-4465 - Sara Magliacane, Tom Claassen, Joris M. Mooij:
Ancestral Causal Inference. 4466-4474 - Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu:
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. 4475-4483 - Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Harri Valpola, Jürgen Schmidhuber:
Tagger: Deep Unsupervised Perceptual Grouping. 4484-4492 - Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher:
An Efficient Streaming Algorithm for the Submodular Cover Problem. 4493-4501 - Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu:
Interaction Networks for Learning about Objects, Relations and Physics. 4502-4510 - Daniel C. McNamee, Daniel M. Wolpert, Máté Lengyel:
Efficient state-space modularization for planning: theory, behavioral and neural signatures. 4511-4519 - Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. 4520-4528 - Wei-Shou Hsu, Pascal Poupart:
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics. 4529-4537 - Justin T. Khim, Varun S. Jog, Po-Ling Loh:
Computing and maximizing influence in linear threshold and triggering models. 4538-4546 - Yichen Wang, Nan Du, Rakshit Trivedi, Le Song:
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions. 4547-4555 - Junhong Lin, Lorenzo Rosasco:
Optimal Learning for Multi-pass Stochastic Gradient Methods. 4556-4564 - Jonathan Ho, Stefano Ermon:
Generative Adversarial Imitation Learning. 4565-4573 - Chang Liu, Xinyun Chen, Eui Chul Richard Shin, Mingcheng Chen, Dawn Xiaodong Song:
Latent Attention For If-Then Program Synthesis. 4574-4582 - Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Dual Space Gradient Descent for Online Learning. 4583-4591 - Hongyi Zhang, Sashank J. Reddi, Suvrit Sra:
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. 4592-4600 - Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio:
Professor Forcing: A New Algorithm for Training Recurrent Networks. 4601-4609 - Elvis Dohmatob, Arthur Mensch, Gaël Varoquaux, Bertrand Thirion:
Learning brain regions via large-scale online structured sparse dictionary learning. 4610-4618 - Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee:
Efficient Neural Codes under Metabolic Constraints. 4619-4627 - Andrej Risteski, Yuanzhi Li:
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods. 4628-4636 - Alexander Shishkin, Anastasia A. Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov:
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information. 4637-4645 - Matthias W. Seeger, David Salinas, Valentin Flunkert:
Bayesian Intermittent Demand Forecasting for Large Inventories. 4646-4654 - Ruiyu Li, Jiaya Jia:
Visual Question Answering with Question Representation Update (QRU). 4655-4663 - Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu:
Learning Parametric Sparse Models for Image Super-Resolution. 4664-4672 - Jean-Bastien Grill, Michal Valko, Rémi Munos:
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. 4673-4681 - Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh:
Asynchronous Parallel Greedy Coordinate Descent. 4682-4690 - R. Devon Hjelm, Russ Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince D. Calhoun, Junyoung Chung:
Iterative Refinement of the Approximate Posterior for Directed Belief Networks. 4691-4699 - Antoine Désir, Vineet Goyal, Srikanth Jagabathula, Danny Segev:
Assortment Optimization Under the Mallows model. 4700-4708 - Peter Schulam, Raman Arora:
Disease Trajectory Maps. 4709-4717 - Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha:
Multistage Campaigning in Social Networks. 4718-4726 - Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos:
Learning in Games: Robustness of Fast Convergence. 4727-4735 - Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. 4736-4744 - Andrej Risteski, Yuanzhi Li:
Algorithms and matching lower bounds for approximately-convex optimization. 4745-4753 - Tyler B. Johnson, Carlos Guestrin:
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization. 4754-4762 - Yang Song, Jun Zhu, Yong Ren:
Kernel Bayesian Inference with Posterior Regularization. 4763-4771 - Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon:
Neural Universal Discrete Denoiser. 4772-4780 - Jonathan Kadmon, Haim Sompolinsky:
Optimal Architectures in a Solvable Model of Deep Networks. 4781-4789 - Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, Alex Graves:
Conditional Image Generation with PixelCNN Decoders. 4790-4798 - Edwin Miles Stoudenmire, David J. Schwab:
Supervised Learning with Tensor Networks. 4799-4807 - Maia Fraser:
Multi-step learning and underlying structure in statistical models. 4808-4816 - Dmitry Ostrovsky, Zaïd Harchaoui, Anatoli B. Juditsky, Arkadi Nemirovski:
Structure-Blind Signal Recovery. 4817-4825 - Philip Bachman:
An Architecture for Deep, Hierarchical Generative Models. 4826-4834 - José L. Torrecilla, Alberto Suárez:
Feature selection in functional data classification with recursive maxima hunting. 4835-4843 - Ashish Khetan, Sewoong Oh:
Achieving budget-optimality with adaptive schemes in crowdsourcing. 4844-4852 - Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky:
Near-Optimal Smoothing of Structured Conditional Probability Matrices. 4853-4861 - Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger:
Supervised Word Mover's Distance. 4862-4870 - Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel:
Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models. 4871-4879 - Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les E. Atlas:
Full-Capacity Unitary Recurrent Neural Networks. 4880-4888 - Jacob D. Abernethy, Kareem Amin, Ruihao Zhu:
Threshold Bandits, With and Without Censored Feedback. 4889-4897 - Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel:
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. 4898-4906 - Lev Bogolubsky, Pavel E. Dvurechensky, Alexander V. Gasnikov, Gleb Gusev, Yurii E. Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii:
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods. 4907-4915 - Oren Anava, Kfir Y. Levy:
k*-Nearest Neighbors: From Global to Local. 4916-4924 - Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi:
Normalized Spectral Map Synchronization. 4925-4933 - Moontae Lee, Seok Hyun Jin, David M. Mimno:
Beyond Exchangeability: The Chinese Voting Process. 4934-4942 - Nicolò Colombo, Nikos Vlassis:
A posteriori error bounds for joint matrix decomposition problems. 4943-4950 - Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv:
A Bayesian method for reducing bias in neural representational similarity analysis. 4952-4960 - Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. 4961-4969 - Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. 4970-4978 - Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu:
SDP Relaxation with Randomized Rounding for Energy Disaggregation. 4979-4987 - Yuanzhi Li, Yingyu Liang, Andrej Risteski:
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates. 4988-4996 - Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess:
Unsupervised Learning of 3D Structure from Images. 4997-5005 - Aaron Schein, Hanna M. Wallach, Mingyuan Zhou:
Poisson-Gamma dynamical systems. 5006-5014 - Tamara Fernandez, Nicolas Rivera, Yee Whye Teh:
Gaussian Processes for Survival Analysis. 5015-5023 - Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon:
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. 5024-5032 - Akshay Balsubramani, Yoav Freund:
Optimal Binary Classifier Aggregation for General Losses. 5033-5040 - Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun:
Disentangling factors of variation in deep representation using adversarial training. 5041-5049 - Necdet Serhat Aybat, Erfan Yazdandoost Hamedani:
A primal-dual method for conic constrained distributed optimization problems. 5050-5058 - Farshad Lahouti, Babak Hassibi:
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing. 5059-5067 - Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. 5067-5075 - Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Learning Deep Parsimonious Representations. 5076-5084 - Cyclades: Conflict-free Asynchronous Machine Learning.
- Learning to Poke by Poking: Experiential Learning of Intuitive Physics.
- Only H is left: Near-tight Episodic PAC RL.
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