Address: Microsoft Research,
300 Lafayette Street,
New York, NY 10012
I am a Senior Researcher at Microsoft Research, New York.
I received my PhD in computer science from Cornell University (2019) and my
bachelors in computer science from Indian Institute of Technology Kanpur (2013).
Research Interests:
My main interest is in developing efficient machine learning algorithms with
applications to real-world problems. The word efficient here includes provable,
sample and computationally efficient, interpretable, scalable, and ethical.
My empirical focus is on problems in natural language understanding and allied fields.
I am currently active in reinforcement learning theory, interactive learning,
representation learning, and language and vision problems. I also have interest
in computational social science, and data and society.
News: We have a new paper on interactive learning that only uses language feedback (i.e., no reward, actions, etc.).
Our algorithm uses language descriptions of trajectories and solves a sequence of supervised learning problems. We evaluate on
grounded language understanding tasks and provide convergence guarantees.
[arXiv paper]
Provable RL: We have three new provable reinforcement learning algorithms for rich-observation problems.
These algorithms are computationally efficient, and their sample complexity is either independent of the size of observation space, or
only weakly depends on it.
Quick Links: MSR Reinforcement Learning, Cereb-RL Code Base, CIFF Code Base, My Blog, A Bandit Game, RL Formulas
Interactive Learning from Activity Description
[arXiv Version] [Code to Come Soon]
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
In Proceedings of the 9th International Conference on Learning Representations (ICLR), 2021.
[Paper and code to come soon]
Learning the Linear Quadratic Regulator from Nonlinear Observations
In Proceedings of the 34th Conference on Neural Information Processing Systems (NeuRIPS), 2020.
[arXiv Version] [Code]
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
[arXiv Version] [ICML Version] [Code]
Early Fusion for Goal Directed Robotic Vision
In International Conference on Intelligent Robots and Systems (IROS), 2019.
[Paper] [Best paper nomination]
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[Paper] [Dataset and SDR Code] [Navigation Code]
Mapping Navigation Instructions to Continuous Control Actions with Position Visitation Prediction
In Proceedings of the Conference on Robot Learning (CoRL), 2018.
[Paper] [Code] [Demo Video]
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
[Paper] [Code, Data and Simulators]
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.
[Paper] [Code] [Arxiv Preprint]
Neural Shift-Reduce CCG Semantic Parsing
In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
[Paper] [Supplementary] [Code]
Environment-driven lexicon induction for high-level instructions
In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2015.
[Paper]
[Supplementary]
[Code]
[Data]
[Simulator]
[Bibtex]
Tell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions
In Proceedings of the Robotics: Science and systems (RSS), 2015.
[Paper] [Website]
[Simulator] [Bibtex]
Combating the Compounding-Error Problem with a Multi-step Model
arXiv, 2019.
[Paper]
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
Deep Reinforcement Learning Workshop at the Conference on Neural Information Processing Systems (NeurIPS), 2018.
[Paper]
The Third Workshop on Representation Learning for NLP (Rep4NLP)
Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL), 2018.
[Workshop Proceedings]
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning
Workshop on Prediction and Generative Modeling in Reinforcement Learning (PGMRL) at the International Conference on Machine Learning (ICML), 2018.
[ArXiv Preprint]
Coronavirus and Hosting Conferences Remotely [Post]
Academia and Compute-Intensive AI Research [Post]
PAC with Hoeffding-Bernstein [Post]
Growing Bifurcation of AI Scholarship [Post]
Are Synthetic Datasets in AI Useful? [Post]
Are we doing NLP the right way? [Post]
Writing and Proof Reading Research Code [Post]
Writing Strong PhD Applications [Post]
Mathematical Analysis of Policy Gradient Methods [Post]
Tutorial on Markov Decision Process Theory and Reinforcement Learning. [Slides Part 1] [Slides Part 2] [Post]