Rishabh Singh

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Google ∙ MIT ∙ Microsoft

Software Engineer at Microsoft

A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts

Traditional Bayesian approaches for model uncertainty quantification rel...
03/02/2021 ∙ by Rishabh Singh, et al. ∙ 16 ∙ share

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Latent Programmer: Discrete Latent Codes for Program Synthesis

In many sequence learning tasks, such as program synthesis and document ...
12/01/2020 ∙ by Joey Hong, et al. ∙ 1 ∙ share

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Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration

Discrete structures play an important role in applications like program ...
11/10/2020 ∙ by Hanjun Dai, et al. ∙ 14 ∙ share

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Deep Learning Software Engineering: State of Research and Future Directions

Given the current transformative potential of research that sits at the ...
09/17/2020 ∙ by Prem Devanbu, et al. ∙ 23 ∙ share

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BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration

Program synthesis is challenging largely because of the difficulty of se...
07/28/2020 ∙ by Augustus Odena, et al. ∙ 4 ∙ share

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Scaling Symbolic Methods using Gradients for Neural Model Explanation

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...
06/29/2020 ∙ by Subham Sekhar Sahoo, et al. ∙ 1 ∙ share

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Neural Program Synthesis with a Differentiable Fixer

We present a new program synthesis approach that combines an encoder-dec...
06/19/2020 ∙ by Matej Balog, et al. ∙ 0 ∙ share

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TF-Coder: Program Synthesis for Tensor Manipulations

The success and popularity of deep learning is on the rise, partially du...
03/19/2020 ∙ by Kensen Shi, et al. ∙ 3 ∙ share

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Towards Modular Algorithm Induction

We present a modular neural network architecture Main that learns algori...
02/27/2020 ∙ by Daniel A. Abolafia, et al. ∙ 0 ∙ share

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Towards a Kernel based Physical Interpretation of Model Uncertainty

This paper introduces a new information theoretic framework that provide...
01/30/2020 ∙ by Rishabh Singh, et al. ∙ 22 ∙ share

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Synthetic Datasets for Neural Program Synthesis

The goal of program synthesis is to automatically generate programs in a...
12/27/2019 ∙ by Richard Shin, et al. ∙ 21 ∙ share

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Learning Transferable Graph Exploration

This paper considers the problem of efficient exploration of unseen envi...
10/28/2019 ∙ by Hanjun Dai, et al. ∙ 12 ∙ share

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

Deep Reinforcement Learning (DRL) has led to many recent breakthroughs o...
06/16/2019 ∙ by Marko Vasic, et al. ∙ 0 ∙ share

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SyGuS-Comp 2018: Results and Analysis

Syntax-guided synthesis (SyGuS) is the computational problem of finding ...
04/12/2019 ∙ by Rajeev Alur, et al. ∙ 0 ∙ share

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Neural Program Repair by Jointly Learning to Localize and Repair

Due to its potential to improve programmer productivity and software qua...
04/03/2019 ∙ by Marko Vasic, et al. ∙ 0 ∙ share

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Neural-Guided Symbolic Regression with Semantic Prior

Symbolic regression has been shown to be quite useful in many domains fr...
01/23/2019 ∙ by Li Li, et al. ∙ 12 ∙ share

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Robust Text-to-SQL Generation with Execution-Guided Decoding

We consider the problem of neural semantic parsing, which translates nat...
07/09/2018 ∙ by Chenglong Wang, et al. ∙ 0 ∙ share

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Execution-Guided Neural Program Decoding

We present a neural semantic parser that translatesnatural language ques...
07/09/2018 ∙ by Chenglong Wang, et al. ∙ 0 ∙ share

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Towards Mixed Optimization for Reinforcement Learning with Program Synthesis

Deep reinforcement learning has led to several recent breakthroughs, tho...
07/01/2018 ∙ by Surya Bhupatiraju, et al. ∙ 2 ∙ share

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Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

Program synthesis is the task of automatically generating a program cons...
05/11/2018 ∙ by Rudy Bunel, et al. ∙ 0 ∙ share

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Programmatically Interpretable Reinforcement Learning

We study the problem of generating interpretable and verifiable policies...
04/06/2018 ∙ by Abhinav Verma, et al. ∙ 0 ∙ share

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Program Repair via Direct State Manipulation

The goal of program repair is to automatically fix programs to meet a sp...
03/20/2018 ∙ by Qinheping Hu, et al. ∙ 0 ∙ share

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Learning and analyzing vector encoding of symbolic representations

We present a formal language with expressions denoting general symbol st...
03/10/2018 ∙ by Roland Fernandez, et al. ∙ 0 ∙ share

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Natural Language to Structured Query Generation via Meta-Learning

In conventional supervised training, a model is trained to fit all the t...
03/02/2018 ∙ by Po-Sen Huang, et al. ∙ 0 ∙ share

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Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

The paper describes a new algorithm to generate minimal, stable, and sym...
02/21/2018 ∙ by Xin Zhang, et al. ∙ 0 ∙ share

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Deep Reinforcement Fuzzing

Fuzzing is the process of finding security vulnerabilities in input-proc...
01/14/2018 ∙ by Konstantin Böttinger, et al. ∙ 0 ∙ share

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SyGuS-Comp 2017: Results and Analysis

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding ...
11/29/2017 ∙ by Rajeev Alur, et al. ∙ 0 ∙ share

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Dynamic Neural Program Embedding for Program Repair

Neural program embeddings have shown much promise recently for a variety...
11/20/2017 ∙ by Ke Wang, et al. ∙ 0 ∙ share

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Data-Driven Feedback Generation for Introductory Programming Exercises

This paper introduces the "Search, Align, and Repair" data-driven progra...
11/20/2017 ∙ by Ke Wang, et al. ∙ 0 ∙ share

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WebRelate: Integrating Web Data with Spreadsheets using Examples

Data integration between web sources and relational data is a key challe...
11/15/2017 ∙ by Jeevana Priya Inala, et al. ∙ 0 ∙ share

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Not all bytes are equal: Neural byte sieve for fuzzing

Fuzzing is a popular dynamic program analysis technique used to find vul...
11/10/2017 ∙ by Mohit Rajpal, et al. ∙ 0 ∙ share

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Semantic Code Repair using Neuro-Symbolic Transformation Networks

We study the problem of semantic code repair, which can be broadly defin...
10/30/2017 ∙ by Jacob Devlin, et al. ∙ 0 ∙ share

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Program Synthesis using Abstraction Refinement

We present a new approach to example-guided program synthesis based on c...
10/21/2017 ∙ by Xinyu Wang, et al. ∙ 0 ∙ share

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Neural Program Meta-Induction

Most recently proposed methods for Neural Program Induction work under t...
10/11/2017 ∙ by Jacob Devlin, et al. ∙ 0 ∙ share

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Synthesis of Data Completion Scripts using Finite Tree Automata

In application domains that store data in a tabular format, a common tas...
07/05/2017 ∙ by Xinyu Wang, et al. ∙ 0 ∙ share

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Deep API Programmer: Learning to Program with APIs

We present DAPIP, a Programming-By-Example system that learns to program...
04/14/2017 ∙ by Surya Bhupatiraju, et al. ∙ 0 ∙ share

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Learn&Fuzz: Machine Learning for Input Fuzzing

Fuzzing consists of repeatedly testing an application with modified, or ...
01/25/2017 ∙ by Patrice Godefroid, et al. ∙ 0 ∙ share

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Summary - TerpreT: A Probabilistic Programming Language for Program Induction

We study machine learning formulations of inductive program synthesis; t...
12/02/2016 ∙ by Alexander L. Gaunt, et al. ∙ 0 ∙ share

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Neuro-Symbolic Program Synthesis

Recent years have seen the proposal of a number of neural architectures ...
11/06/2016 ∙ by Emilio Parisotto, et al. ∙ 0 ∙ share

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TerpreT: A Probabilistic Programming Language for Program Induction

We study machine learning formulations of inductive program synthesis; g...
08/15/2016 ∙ by Alexander L. Gaunt, et al. ∙ 0 ∙ share

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Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks

We present a method for automatically generating repair feedback for syn...
03/19/2016 ∙ by Sahil Bhatia, et al. ∙ 0 ∙ share

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Automated Feedback Generation for Introductory Programming Assignments

We present a new method for automatically providing feedback for introdu...
04/08/2012 ∙ by Rishabh Singh, et al. ∙ 0 ∙ share

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Rishabh Singh

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