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ORCIDRishabh Singh
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2020 – today
- 2024
[c64]Rishabh Singh, Yaxin Ma, José C. Príncipe:
Finding Local Dependent Regions in PDFs using RKHS Uncertainty Moments and Optimal Transport. IJCNN 2024: 1-6- 2023
- [c63]Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta:
Measuring the Impact of Programming Language Distribution. ICML 2023: 26619-26645 - [i51]Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta:
Measuring The Impact Of Programming Language Distribution. CoRR abs/2302.01973 (2023) - 2022
- [j21]Kavita Pabreja
, Anubhuti Singh, Rishabh Singh, Rishita Agnihotri, Shriam Kaushik, Tanvi Malhotra:
Prediction of Stress Level on Indian Working Professionals Using Machine Learning. Int. J. Hum. Cap. Inf. Technol. Prof. 13(1): 1-26 (2022) - [j20]Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid:
MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning. Neural Networks 151: 34-47 (2022) - [j19]Qinheping Hu, Rishabh Singh, Loris D'Antoni:
Solving Program Sketches with Large Integer Values. ACM Trans. Program. Lang. Syst. 44(2): 9:1-9:28 (2022) - [j18]Kensen Shi, David Bieber, Rishabh Singh:
TF-Coder: Program Synthesis for Tensor Manipulations. ACM Trans. Program. Lang. Syst. 44(2): 10:1-10:36 (2022) - [c62]Rishabh Singh, Shirin Goshtasbpour:
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers. ACL (Findings) 2022: 3673-3684 - [c61]Murtadha D. Hssayeni, Arash Andalib, Rishabh Singh, Diego Pava, Kan Li, Steven Borzak, Robert Chait, Kaustubh Kale:
ECG Fiducial Point Localization Using a Deep Learning Model. ICMLA 2022: 321-328 - [i50]Rishabh Singh, José C. Príncipe:
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS. CoRR abs/2211.01999 (2022) - [i49]Rishabh Singh, José C. Príncipe:
Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport. CoRR abs/2211.02005 (2022) - 2021
- [j17]Rishabh Singh, Devansh Timbadia, Vidhi Kapoor, Rishabh Reddy, Prathamesh P. Churi, Omkar Pimple:
Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application. Educ. Inf. Technol. 26(4): 4151-4179 (2021) - [j16]Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
Special Issue on Syntax-Guided Synthesis Preface. Formal Methods Syst. Des. 58(3): 469-470 (2021) - [j15]Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue:
Neurosymbolic Programming. Found. Trends Program. Lang. 7(3): 158-243 (2021) - [j14]Rishabh Singh, José C. Príncipe:
Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models. Neural Comput. 33(5): 1164-1198 (2021) - [c60]Rishabh Singh:
Bias: Bijective Input And Surjectivity In Zero Shot Learning. ICIP 2021: 1249-1253 - [c59]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai:
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. ICLR 2021 - [c58]Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley:
Scaling Symbolic Methods using Gradients for Neural Model Explanation. ICLR 2021 - [c57]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML 2021: 1661-1672 - [c56]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. ICML 2021: 4308-4318 - [c55]Digvijay Singh, Rishabh Singh, Rahul Ajmeria, Manik Gupta, R. N. Ponnalagu:
DAWSSM: A plug-and-play Drone Assisted Water Sampling and Sensing Module. IECON 2021: 1-6 - [c54]Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, Richard Ho, Charles Sutton:
Learning Semantic Representations to Verify Hardware Designs. NeurIPS 2021: 23491-23504 - [i48]Rishabh Singh, José C. Príncipe:
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts. CoRR abs/2103.01374 (2021) - [i47]Vaishali V. Ingale, Rishabh Singh, Pragati Patwal:
Image to Image Translation : Generating maps from satellite images. CoRR abs/2105.09253 (2021) - [i46]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. CoRR abs/2106.15339 (2021) - [i45]Sumit Kumar Varshney, Jeetu Kumar, Aditya Tiwari, Rishabh Singh, Venkata M. V. Gunturi, Narayanan C. Krishnan:
Deep Geospatial Interpolation Networks. CoRR abs/2108.06670 (2021) - [i44]Rishabh Singh, José C. Príncipe:
Quantifying Model Predictive Uncertainty with Perturbation Theory. CoRR abs/2109.10888 (2021) - 2020
- [j13]Ajay Vikram Singh, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux, Andreas Luch:
Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Adv. Intell. Syst. 2(12): 2000084 (2020) - [j12]Ajay Vikram Singh, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux, Andreas Luch:
Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Adv. Intell. Syst. 2(12): 2070125 (2020) - [j11]Rishabh Reddy, Rishabh Singh, Vidhi Kapoor, Prathamesh P. Churi:
Joy of Learning Through Internet Memes. Int. J. Eng. Pedagog. 10(5): 116-133 (2020) - [j10]Shengwei An, Rishabh Singh, Sasa Misailovic, Roopsha Samanta:
Augmented example-based synthesis using relational perturbation properties. Proc. ACM Program. Lang. 4(POPL): 56:1-56:24 (2020) - [c53]Rong Pan, Qinheping Hu, Rishabh Singh, Loris D'Antoni:
Solving Program Sketches with Large Integer Values. ESOP 2020: 572-598 - [c52]Rishabh Singh, Shujian Yu, José C. Príncipe:
Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System. ICASSP 2020: 2757-2761 - [c51]Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber:
Global Relational Models of Source Code. ICLR 2020 - [c50]Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik:
Generating Programmatic Referring Expressions via Program Synthesis. ICML 2020: 4495-4506 - [c49]Rishabh Reddy, Rishabh Singh, Vidhi Kapoor, Prathamesh P. Churi:
Cyberbullying and Indian Society: Outcomes from Social Conclave Conference. ISTAS 2020: 314-321 - [c48]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [c47]Rishabh Singh, José C. Príncipe:
Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework. UAI 2020: 1368-1377 - [i43]Rishabh Singh, José C. Príncipe:
Towards a Kernel based Physical Interpretation of Model Uncertainty. CoRR abs/2001.11495 (2020) - [i42]Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer, Charles Sutton:
Towards Modular Algorithm Induction. CoRR abs/2003.04227 (2020) - [i41]Kensen Shi, David Bieber, Rishabh Singh:
TF-Coder: Program Synthesis for Tensor Manipulations. CoRR abs/2003.09040 (2020) - [i40]Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton:
Neural Program Synthesis with a Differentiable Fixer. CoRR abs/2006.10924 (2020) - [i39]Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick F. Riley:
Scaling Symbolic Methods using Gradients for Neural Model Explanation. CoRR abs/2006.16322 (2020) - [i38]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton:
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration. CoRR abs/2007.14381 (2020) - [i37]Prem Devanbu, Matthew B. Dwyer, Sebastian G. Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang:
Deep Learning & Software Engineering: State of Research and Future Directions. CoRR abs/2009.08525 (2020) - [i36]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR abs/2011.05363 (2020) - [i35]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. CoRR abs/2012.00377 (2020)
2010 – 2019
- 2019
- [c46]Abhinav Kumar, Aishwarya Gupta, Bishal Santra, K. S. Lalitha, Manasa Kolla, Mayank Gupta, Rishabh Singh:
VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System. AAAI 2019: 9498-9503 - [c45]Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song:
Synthetic Datasets for Neural Program Synthesis. ICLR (Poster) 2019 - [c44]Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh:
Neural Program Repair by Jointly Learning to Localize and Repair. ICLR (Poster) 2019 - [c43]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. NeurIPS 2019: 2514-2525 - [c42]Qinheping Hu, Roopsha Samanta, Rishabh Singh, Loris D'Antoni:
Direct Manipulation for Imperative Programs. SAS 2019: 347-367 - [i34]Li Li, Minjie Fan, Rishabh Singh, Patrick Riley:
Neural-Guided Symbolic Regression with Semantic Prior. CoRR abs/1901.07714 (2019) - [i33]Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh:
Neural Program Repair by Jointly Learning to Localize and Repair. CoRR abs/1904.01720 (2019) - [i32]Rajeev Alur, Dana Fisman, Saswat Padhi, Rishabh Singh, Abhishek Udupa:
SyGuS-Comp 2018: Results and Analysis. CoRR abs/1904.07146 (2019) - [i31]Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid:
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees. CoRR abs/1906.06717 (2019) - [i30]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. CoRR abs/1910.12980 (2019) - [i29]Richard Shin, Neel Kant, Kavi Gupta, Christopher Bender, Brandon Trabucco, Rishabh Singh, Dawn Song:
Synthetic Datasets for Neural Program Synthesis. CoRR abs/1912.12345 (2019) - 2018
- [j9]Rajeev Alur, Rishabh Singh, Dana Fisman, Armando Solar-Lezama:
Search-based program synthesis. Commun. ACM 61(12): 84-93 (2018) - [j8]Jeevana Priya Inala, Rishabh Singh:
WebRelate: integrating web data with spreadsheets using examples. Proc. ACM Program. Lang. 2(POPL): 2:1-2:28 (2018) - [j7]Xinyu Wang, Isil Dillig, Rishabh Singh:
Program synthesis using abstraction refinement. Proc. ACM Program. Lang. 2(POPL): 63:1-63:30 (2018) - [c41]Sitara Shah, Snigdha Petluru, Rishabh Singh, Saurabh Srivastava:
gAR-age: A Feedback-Enabled Blended Ecosystem for Vehicle Health Monitoring. AutomotiveUI 2018: 268-277 - [c40]Rishabh Singh, Kan Li, José C. Príncipe:
Nearest-Instance-Centroid-Estimation Linear Discriminant Analysis (Nice Lda). ICASSP 2018: 2846-2850 - [c39]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. ICLR (Poster) 2018 - [c38]Roland Fernandez, Asli Celikyilmaz, Paul Smolensky, Rishabh Singh:
Learning and Analyzing Vector Encoding of Symbolic Representation. ICLR (Workshop) 2018 - [c37]Ke Wang, Rishabh Singh, Zhendong Su:
Dynamic Neural Program Embeddings for Program Repair. ICLR (Poster) 2018 - [c36]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. ICML 2018: 5052-5061 - [c35]Sahil Bhatia, Pushmeet Kohli, Rishabh Singh:
Neuro-symbolic program corrector for introductory programming assignments. ICSE 2018: 60-70 - [c34]Rishabh Singh, José C. Príncipe:
Correntropy Based Hierarchical Linear Dynamical System For Speech Recognition. IJCNN 2018: 1-7 - [c33]Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He:
Natural Language to Structured Query Generation via Meta-Learning. NAACL-HLT (2) 2018: 732-738 - [c32]Xin Zhang, Armando Solar-Lezama, Rishabh Singh:
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. NeurIPS 2018: 4879-4890 - [c31]Ke Wang, Rishabh Singh, Zhendong Su:
Search, align, and repair: data-driven feedback generation for introductory programming exercises. PLDI 2018: 481-495 - [c30]Konstantin Böttinger, Patrice Godefroid, Rishabh Singh:
Deep Reinforcement Fuzzing. IEEE Symposium on Security and Privacy Workshops 2018: 116-122 - [i28]Konstantin Böttinger, Patrice Godefroid, Rishabh Singh:
Deep Reinforcement Fuzzing. CoRR abs/1801.04589 (2018) - [i27]Xin Zhang, Armando Solar-Lezama, Rishabh Singh:
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. CoRR abs/1802.07384 (2018) - [i26]Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He:
Natural Language to Structured Query Generation via Meta-Learning. CoRR abs/1803.02400 (2018) - [i25]Roland Fernandez, Asli Celikyilmaz, Rishabh Singh, Paul Smolensky:
Learning and analyzing vector encoding of symbolic representations. CoRR abs/1803.03834 (2018) - [i24]Qinheping Hu, Isaac Evavold, Roopsha Samanta, Rishabh Singh, Loris D'Antoni:
Program Repair via Direct State Manipulation. CoRR abs/1803.07522 (2018) - [i23]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. CoRR abs/1804.02477 (2018) - [i22]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. CoRR abs/1805.04276 (2018) - [i21]Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh:
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis. CoRR abs/1807.00403 (2018) - [i20]Chenglong Wang, Po-Sen Huang, Alex Polozov, Marc Brockschmidt, Rishabh Singh:
Execution-Guided Neural Program Decoding. CoRR abs/1807.03100 (2018) - 2017
- [j6]Sumit Gulwani, Oleksandr Polozov, Rishabh Singh:
Program Synthesis. Found. Trends Program. Lang. 4(1-2): 1-119 (2017) - [j5]Xinyu Wang, Isil Dillig, Rishabh Singh:
Synthesis of data completion scripts using finite tree automata. Proc. ACM Program. Lang. 1(OOPSLA): 62:1-62:26 (2017) - [c29]Vivek Venugopal, Abhishek Sharma, Rishabh Singh, Abhinav Sharma, Suresh Sundaram:
A Vector Quantization Based Feature Descriptor for Online Signature Verification. ICDAR 2017: 1210-1215 - [c28]Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. ICLR (Poster) 2017 - [c27]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. ICML 2017: 990-998 - [c26]Patrice Godefroid, Hila Peleg, Rishabh Singh:
Learn&Fuzz: machine learning for input fuzzing. ASE 2017: 50-59 - [c25]Ke Wang, Benjamin Lin, Bjorn Rettig, Paul Pardi, Rishabh Singh:
Data-Driven Feedback Generator for Online Programing Courses. L@S 2017: 257-260 - [c24]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. NIPS 2017: 2080-2088 - [c23]Loris D'Antoni, Rishabh Singh, Michael Vaughn:
NoFAQ: synthesizing command repairs from examples. ESEC/SIGSOFT FSE 2017: 582-592 - [c22]Rishabh Singh, Pushmeet Kohli:
AP: Artificial Programming. SNAPL 2017: 16:1-16:12 - [c21]Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
SyGuS-Comp 2017: Results and Analysis. SYNT@CAV 2017: 97-115 - [i19]Patrice Godefroid, Hila Peleg, Rishabh Singh:
Learn&Fuzz: Machine Learning for Input Fuzzing. CoRR abs/1701.07232 (2017) - [i18]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. CoRR abs/1703.07469 (2017) - [i17]Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
Deep API Programmer: Learning to Program with APIs. CoRR abs/1704.04327 (2017) - [i16]Xinyu Wang, Isil Dillig, Rishabh Singh:
Synthesis of Data Completion Scripts using Finite Tree Automata. CoRR abs/1707.01469 (2017) - [i15]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. CoRR abs/1710.04157 (2017) - [i14]Xinyu Wang, Isil Dillig, Rishabh Singh:
Program Synthesis using Abstraction Refinement. CoRR abs/1710.07740 (2017) - [i13]Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli:
Semantic Code Repair using Neuro-Symbolic Transformation Networks. CoRR abs/1710.11054 (2017) - [i12]Mohit Rajpal, William Blum, Rishabh Singh:
Not all bytes are equal: Neural byte sieve for fuzzing. CoRR abs/1711.04596 (2017) - [i11]Jeevana Priya Inala, Rishabh Singh:
WebRelate: Integrating Web Data with Spreadsheets using Examples. CoRR abs/1711.05787 (2017) - [i10]Ke Wang, Rishabh Singh, Zhendong Su:
Data-Driven Feedback Generation for Introductory Programming Exercises. CoRR abs/1711.07148 (2017) - [i9]Ke Wang, Rishabh Singh, Zhendong Su:
Dynamic Neural Program Embedding for Program Repair. CoRR abs/1711.07163 (2017) - [i8]Ute Schmid, Stephen H. Muggleton, Rishabh Singh:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports 7(9): 86-108 (2017) - 2016
- [j4]Rishabh Singh:
BlinkFill: Semi-supervised Programming By Example for Syntactic String Transformations. Proc. VLDB Endow. 9(10): 816-827 (2016) - [c20]Loris D'Antoni, Roopsha Samanta, Rishabh Singh:
Qlose: Program Repair with Quantitative Objectives. CAV (2) 2016: 383-401 - [c19]Parmit K. Chilana, Rishabh Singh, Philip J. Guo:
Understanding Conversational Programmers: A Perspective from the Software Industry. CHI 2016: 1462-1472 - [c18]Xinyu Wang, Sumit Gulwani, Rishabh Singh:
FIDEX: filtering spreadsheet data using examples. OOPSLA 2016: 195-213 - [c17]Rishabh Singh, Sumit Gulwani:
Transforming spreadsheet data types using examples. POPL 2016: 343-356 - [c16]Rishabh Singh, Aman Abidi, Mohammed Abdul Qadeer:
SyncWorld: A cloud storage/synchronization service using Java and Php. WOCN 2016: 1-5 - [c15]Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
SyGuS-Comp 2016: Results and Analysis. SYNT@CAV 2016: 178-202 - [i7]Sahil Bhatia, Rishabh Singh:
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks. CoRR abs/1603.06129 (2016) - [i6]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1608.04428 (2016) - [i5]Loris D'Antoni, Rishabh Singh, Michael Vaughn:
NoFAQ: Synthesizing Command Repairs from Examples. CoRR abs/1608.08219 (2016) - [i4]Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. CoRR abs/1611.01855 (2016) - [i3]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
Summary - TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1612.00817 (2016) - 2015
- [j3]Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller:
OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. ACM Trans. Comput. Hum. Interact. 22(2): 7:1-7:35 (2015) - [c14]Rishabh Singh, Sumit Gulwani:
Predicting a Correct Program in Programming by Example. CAV (1) 2015: 398-414 - [c13]Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Benjamin G. Zorn, Sumit Gulwani:
User Interaction Models for Disambiguation in Programming by Example. UIST 2015: 291-301 - [c12]Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama:
Results and Analysis of SyGuS-Comp'15. SYNT 2015: 3-26 - [p1]Rajeev Alur, Rastislav Bodík, Eric Dallal, Dana Fisman, Pranav Garg, Garvit Juniwal, Hadas Kress-Gazit, P. Madhusudan, Milo M. K. Martin, Mukund Raghothaman, Shambwaditya Saha, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, Abhishek Udupa:
Syntax-Guided Synthesis. Dependable Software Systems Engineering 2015: 1-25 - 2014
- [b1]Rishabh Singh:
Accessible programming using program synthesis. Massachusetts Institute of Technology, Cambridge, MA, USA, 2014 - [c11]Elena L. Glassman, Rishabh Singh, Robert C. Miller:
Feature engineering for clustering student solutions. L@S 2014: 171-172 - [c10]Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller:
OverCode: visualizing variation in student solutions to programming problems at scale. UIST (Adjunct Volume) 2014: 129-130 - [c9]Rohit Singh, Rishabh Singh, Zhilei Xu, Rebecca Krosnick, Armando Solar-Lezama:
Modular Synthesis of Sketches Using Models. VMCAI 2014: 395-414 - 2013
- [c8]Rajeev Alur, Rastislav Bodík, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, Abhishek Udupa:
Syntax-guided synthesis. FMCAD 2013: 1-8 - [c7]Rishabh Singh, Sumit Gulwani, Armando Solar-Lezama:
Automated feedback generation for introductory programming assignments. PLDI 2013: 15-26 - 2012
- [j2]Sumit Gulwani, William R. Harris, Rishabh Singh:
Spreadsheet data manipulation using examples. Commun. ACM 55(8): 97-105 (2012) - [j1]Rishabh Singh, Sumit Gulwani:
Learning Semantic String Transformations from Examples. Proc. VLDB Endow. 5(8): 740-751 (2012) - [c6]Rishabh Singh, Sumit Gulwani:
Synthesizing Number Transformations from Input-Output Examples. CAV 2012: 634-651 - [c5]Rishabh Singh, Armando Solar-Lezama:
SPT: Storyboard Programming Tool. CAV 2012: 738-743 - [i2]Rishabh Singh, Sumit Gulwani, Armando Solar-Lezama:
Automated Semantic Grading of Programs. CoRR abs/1204.1751 (2012) - [i1]Rishabh Singh, Sumit Gulwani:
Learning Semantic String Transformations from Examples. CoRR abs/1204.6079 (2012) - 2011
- [c4]Rishabh Singh, Armando Solar-Lezama:
Synthesizing data structure manipulations from storyboards. SIGSOFT FSE 2011: 289-299 - 2010
- [c3]Rishabh Singh, Dimitra Giannakopoulou, Corina S. Pasareanu:
Learning Component Interfaces with May and Must Abstractions. CAV 2010: 527-542
2000 – 2009
- 2009
- [c2]Derek Rayside, Zev Benjamin, Rishabh Singh, Joseph P. Near, Aleksandar Milicevic, Daniel Jackson:
Equality and hashing for (almost) free: Generating implementations from abstraction functions. ICSE 2009: 342-352 - [c1]Andrey Rybalchenko, Rishabh Singh:
Subsumer-First: Steering Symbolic Reachability Analysis. SPIN 2009: 192-204
Coauthor Index
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:24 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
