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Rishabh Singh

Dr. Bhim Rao Ambadker University Agra, Computer Science, Graduate Student

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I am currently a final year B.E student with Majoring in Computer Science And Engineering at Dr. Bhimrao Ambedkar University, Agra. I am living to write and code. I am a person who works hard and pays attention to detail. I'm flexible, quick to pick up new skills and eager to learn from others.I am Enthusiastic And Determinant Men. And Some I Interest Include Fitness, Cricket and Basketball, Web crawling.

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Papers by Rishabh Singh

TF-Coder: Program Synthesis for Tensor Manipulations

ACM Transactions on Programming Languages and Systems

The success and popularity of deep learning is on the rise, partially due to powerful deep learni... more The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch, which make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding sim...

MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning

Neural Networks, 2022

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning mo... more Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to expose the inner workings of the model in a human understandable form. We present MoËT, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. Thanks to such gating function the model is more expressive than the standard decision tree. To support non-differentiable decision trees as experts, we formulate a novel training procedure. In addition, we introduce a hard thresholding version, MoËT h , in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, MoËT h allows each prediction to be easily decomposed into a set of logical rules in a form which can be easily verified. While MoËT is a general use model, we illustrate its power in the reinforcement learning setting. By training MoËT models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models. Moreover, we show that MoËT can also be used in real-world supervised problems on which it outperforms other verifiable machine learning models.

Scaling Symbolic Methods using Gradients for Neural Model Explanation

ArXiv, 2021

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for an... more Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation. In particular, we apply this technique to identify minimal regions in an input that are most relevant for a neural network's prediction. Our approach uses gradient information (based on Integrated Gradients) to focus on a subset of neurons in the first layer, which allows our technique to scale to large networks. The corresponding SMT constraints encode the minimal input mask discovery problem such that after masking the input, the activations of the selected neurons are still above a threshold. After solving for the minimal masks, our approach scores the mask regions t...

MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees

ArXiv, 2019

Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, ... more Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go. However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings. Viper, a recently proposed technique, constructs a decision tree policy by mimicking the DRL agent. Decision trees are interpretable as each action made can be traced back to the decision rule path that lead to it. However, one global decision tree approximating the DRL policy has significant limitations with respect to the geometry of decision boundaries. We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions. We propose a training procedure to support non-differentiable decision tree experts and integrate it into imitation learning pr...

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

ArXiv, 2018

Program synthesis is the task of automatically generating a program consistent with a specificati... more Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are trained to maximize the likelihood of known reference programs. While achieving impressive results, this strategy has two key limitations. First, it ignores Program Aliasing: the fact that many different programs may satisfy a given specification (especially with incomplete specifications such as a few input-output examples). By maximizing the likelihood of only a single reference program, it penalizes many semantically correct programs, which can adversely affect the synthesizer performance. Second, this strategy overlooks the fact that programs have a strict syntax that can be efficiently checked. To address the first limitation, we perform reinforcement learning on ...

Quantifying Model Predictive Uncertainty with Perturbation Theory

ArXiv, 2021

We propose a framework for predictive uncertainty quantification of a neural network that replace... more We propose a framework for predictive uncertainty quantification of a neural network that replaces the conventional Bayesian notion of weight probability density function (PDF) with a physics based potential field representation of the model weights in a Gaussian reproducing kernel Hilbert space (RKHS) embedding. This allows us to use perturbation theory from quantum physics to formulate a moment decomposition problem over the model weight-output relationship. The extracted moments reveal successive degrees of regularization of the weight potential field around the local neighborhood of the model output. Such localized moments represent well the PDF tails and provide significantly greater accuracy of the model’s predictive uncertainty than the central moments characterized by Bayesian and ensemble methods or their variants. We show that this consequently leads to a better ability to detect false model predictions of test data that has undergone a covariate shift away from the traini...

Search, align, and repair: data-driven feedback generation for introductory programming exercises

ACM SIGPLAN Notices, 2018

This paper introduces the “Search, Align, and Repair” data-driven program repair framework to aut... more This paper introduces the “Search, Align, and Repair” data-driven program repair framework to automate feedback generation for introductory programming exercises. Distinct from existing techniques, our goal is to develop an efficient, fully automated, and problem-agnostic technique for large or MOOC-scale introductory programming courses. We leverage the large amount of available student submissions in such settings and develop new algorithms for identifying similar programs, aligning correct and incorrect programs, and repairing incorrect programs by finding minimal fixes. We have implemented our technique in the Sarfgen system and evaluated it on thousands of real student attempts from the Microsoft-DEV204.1x edX course and the Microsoft CodeHunt platform. Our results show that Sarfgen can, within two seconds on average, generate concise, useful feedback for 89.7% of the incorrect student submissions. It has been integrated with the Microsoft-DEV204.1X edX class and deployed for p...

Acute Kidney Injury Biomarkers: What Do They Tell Us?

Contributions to nephrology, 2018

The definition of acute kidney injury (AKI) is now well established and encompasses changes in bo... more The definition of acute kidney injury (AKI) is now well established and encompasses changes in both the urine output and the serum creatinine (SCr) over time. Many studies to date have concentrated solely on the SCr criteria, as this is relatively easy to monitor, given that accurate urine output is rarely measured outside critical care areas. However, many studies have emphasised the inadequacies of SCr in highlighting potential renal injury in a timely fashion. These limitations reflect not only acute changes in creatinine metabolism in the critically ill but also the kinetics of creatinine generation that may hinder early recognition of AKI. In turn, this may prevent judicious intervention promoting the misconception that little can be done for patients with this devastating condition except treating the consequences. Such observations have led to much research focused on identifying early indicators of AKI that may enable early treatment and hopefully lead to improved outcomes. ...

SyGuS-Comp 2016: Results and Analysis

Electronic Proceedings in Theoretical Computer Science, 2016

Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation f that ... more Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula ϕ in a background theory T , and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in the SyGuS input format (SyGuS-IF), a language that is built on top of SMT-LIB. The syntax-guided synthesis competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In the 5 th SyGuS-Comp, five solvers competed on over 1600 benchmarks across various tracks. This paper presents and analyses the results of this year's (2018) SyGuS competition.

SyncWorld: A cloud storage/synchronization service using Java and Php

2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN), 2016

Cloud Computing is a major field of study. Cloud Storage is regarded as an infrastructure to clou... more Cloud Computing is a major field of study. Cloud Storage is regarded as an infrastructure to cloud computing and personal cloud storage are gaining popularity. This paper presents the development of a user friendly and efficient personal cloud synchronization and storage service using java and php. The application follows a new algorithm and its architecture is beyond the conventional client/server or peer to peer.

Proceedings of the VLDB Endowment, 2016

The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for... more The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for enabling end-users to perform data transformation tasks using input-output examples. Since examples are inherently an under-specification, there are typically a large number of hypotheses conforming to the examples, and the PBE techniques suffer from scalability issues for finding the intended program amongst the large space. We present a semi-supervised learning technique to significantly reduce this ambiguity by using the logical information present in the input data to guide the synthesis algorithm. We develop a data structure InputDataGraph to succinctly represent a large set of logical patterns that are shared across the input data, and use this graph to efficiently learn substring expressions in a new PBE system B link F ill . We evaluate B link F ill on 207 real-world benchmarks and show that B link F ill is significantly faster (on average 41x) and requires fewer input-output ex...

Transforming spreadsheet data types using examples

Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 2016

Cleaning spreadsheet data types is a common problem faced by millions of spreadsheet users. Data ... more Cleaning spreadsheet data types is a common problem faced by millions of spreadsheet users. Data types such as date, time, name, and units are ubiquitous in spreadsheets, and cleaning transformations on these data types involve parsing and pretty printing their string representations. This presents many challenges to users because cleaning such data requires some background knowledge about the data itself and moreover this data is typically nonuniform, unstructured, and ambiguous. Spreadsheet systems and Programming Languages provide some UI-based and programmatic solutions for this problem but they are either insufficient for the user's needs or are beyond their expertise. In this paper, we present a programming by example methodology of cleaning data types that learns the desired transformation from a few input-output examples. We propose a domain specific language with probabilistic semantics that is parameterized with declarative data type definitions. The probabilistic semantics is based on three key aspects: (i) approximate predicate matching, (ii) joint learning of data type interpretation, and (iii) weighted branches. This probabilistic semantics enables the language to handle non-uniform, unstructured, and ambiguous data. We then present a synthesis algorithm that learns the desired program in this language from a set of input-output examples. We have implemented our algorithm as an Excel add-in and present its successful evaluation on 55 benchmark problems obtained from online help forums and Excel product team.

Method of Managing Household Product Inventory

Dynamic braking of induction motor - Analysis of conventional methods and an efficient multistage braking model

2013 International Conference on Energy Efficient Technologies for Sustainability, 2013

In this paper, a detailed analysis is made on capacitor self excitation and DC injection methods ... more In this paper, a detailed analysis is made on capacitor self excitation and DC injection methods of dynamic braking of induction motor through MATLAB/SIMULINK simulations. The effectiveness of these conventional dynamic braking methods is carefully analyzed while changing various parameters. The speed range and time duration for which these methods are the most effective is carefully observed during changing load conditions. The effects of changing capacitor values and DC injection voltage levels on the effective speed range have been studied and the time durations in which these methods are most effective are also analyzed. Using the analysis data obtained, the parameters of a reliable fast multistage dynamic braking model are arrived upon for its best possible performance. The performance of this model, which efficiently dissipates the kinetic energy of the motor in a very short duration of time, is checked through simulations.

Equality and hashing for (almost) free: Generating implementations from abstraction functions

2009 IEEE 31st International Conference on Software Engineering, 2009

In an object-oriented language such as Java, every class requires implementations of two special ... more In an object-oriented language such as Java, every class requires implementations of two special methods, one for determining equality and one for computing hash codes. Although the specification of these methods is usually straightforward, they can be hard to code (due to subclassing, delegation, cyclic references, and other factors) and often harbor subtle faults. A technique is presented that simplifies this task. Instead of writing code for the methods, the programmer gives, as a brief annotation, an abstraction function that defines an abstract view of an object's representation, and sometimes an additional observer in the form of an iterator method. Equality and hash codes are then computed in library code that uses reflection to read the annotations. Experiments on a variety of programs suggest that, in comparison to writing the methods by hand, our technique requires less text from the programmer and results in methods that are more often correct.

Synthesizing Number Transformations from Input-Output Examples

Lecture Notes in Computer Science, 2012

Numbers are one of the most widely used data type in programming languages. Number transformation... more Numbers are one of the most widely used data type in programming languages. Number transformations like formatting and rounding present a challenge even for experienced programmers as they find it difficult to remember different number format strings supported by different programming languages. These transformations present an even bigger challenge for end-users of spreadsheet systems like Microsoft Excel where providing such custom format strings is beyond their expertise. In our extensive case study of help forums of many programming languages and Excel, we found that both programmers and end-users struggle with these number transformations, but are able to easily express their intent using input-output examples. In this paper, we present a framework that can learn such number transformations from very few input-output examples. We first describe an expressive number transformation language that can model these transformations, and then present an inductive synthesis algorithm that can learn all expressions in this language that are consistent with a given set of examples. We also present a ranking scheme of these expressions that enables efficient learning of the desired transformation from very few examples. By combining our inductive synthesis algorithm for number transformations with an inductive synthesis algorithm for syntactic string transformations, we are able to obtain an inductive synthesis algorithm for manipulating data types that have numbers as a constituent sub-type such as date, unit, and time. We have implemented our algorithms as an Excel add-in and have evaluated it successfully over several benchmarks obtained from the help forums and the Excel product team.

Subsumer-First: Steering Symbolic Reachability Analysis

Model Checking Software, 2009

Symbolic reachability analysis provides a basis for the verification of software systems by offer... more Symbolic reachability analysis provides a basis for the verification of software systems by offering algorithmic support for the exploration of the program state space when searching for proofs or counterexamples. The choice of exploration strategy employed by the analysis has direct impact on its success, whereas the ability to find short counterexamples quickly and-as a complementary task-to efficiently perform the exhaustive state space traversal are of utmost importance for the majority of verification efforts. Existing exploration strategies can optimize only one of these objectives which leads to a sub-optimal reachability analysis, e.g., breadth-first search may sacrifice the exploration efficiency and chaotic iteration can miss minimal counterexamples. In this paper we present subsumer-first, a new approach for steering symbolic reachability analysis that targets both minimal counterexample discovery and efficiency of exhaustive exploration. Our approach leverages the result of fixpoint checks performed during symbolic reachability analysis to bias the exploration strategy towards its objectives, and does not require any additional computation. We demonstrate how the subsumer-first approach can be applied to improve efficiency of software verification tools based on predicate abstraction. Our experimental evaluation indicates the practical usefulness of the approach: we observe significant efficiency improvements (median value 40%) on difficult verification benchmarks from the transportation domain.

Automated feedback generation for introductory programming assignments

ACM SIGPLAN Notices, 2013

We present a new method for automatically providing feedback for introductory programming problem... more We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from the Introduction to Programming course at MIT (6.00) and MITx (6.00x). Our results show that relatively simple er...

Spreadsheet data manipulation using examples

Communications of the ACM, 2012

Millions of computer end users need to perform tasks over large spreadsheet data, yet lack the pr... more Millions of computer end users need to perform tasks over large spreadsheet data, yet lack the programming knowledge to do such tasks automatically. We present a programming by example methodology that allows end users to automate such repetitive tasks. Our methodology involves designing a domain-specific language and developing a synthesis algorithm that can learn programs in that language from user-provided examples. We present instantiations of this methodology for particular domains of tasks: (a) syntactic transformations of strings using restricted forms of regular expressions, conditionals, and loops, (b) semantic transformations of strings involving lookup in relational tables, and (c) layout transformations on spreadsheet tables. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help forums.

Problems of techniques in anthropometric studies in India

American Journal of Physical Anthropology, 1974

KEY WORDS Anthropometry. Techniques. Variations. Apex. Vertex. Cranial height.