Arushi Jain

I am fifth year Computer Science (Artificial Intelligence) Ph.D. candidate with Doina Precup at McGill University and Mila, Montreal , Canada.

Last year, I was hosted by Alessandro Lazaric for internship at Meta AI Research Lab (FAIR) in Paris. I worked on learning a self-supervised framework that ties exploration and representation together in reward-free setting.

I did my Masters at McGill University and Mila, with Doina Precup. Before starting Masters, I was a Research Fellow at Microsoft Research (MSR), India in ML team, where I was advised by Sundararajan Sellamanickam . I did my Bachelors in Computer Science at IIIT-Delhi, India. There, I worked in Image Analysis and Biometrics (IAB) lab, where I was advised by Mayank Vatsa and Richa Singh.

Broadly my research interests span reinforcement learning, safety in AI and exploration.

Email / CV / Google Scholar / Twitter / Github

Highlights and News

03/10/2022 Interning with Alessandro Lazaric in Reinforcement Learning team at FAIR, Paris. I am working on learning better exploration and representations in unsupervised reward-free setting to produce optimal policy in zero-shot manner.

04/07/2022 Interning with Personalization team at Amazon, working on RL for Recommender Systems starting July 2022.

01/07/2022 Thrilled to be among the 10 recipients who are awarded "Borealis AI 2022 Fellowship". Press coverage by McGill University.

20/05/2022 "Towards Painless Policy Optimization for Constrained MDPs" paper accepted at UAI, 2022.

18/03/2022 "Robust Constrained MDPs" paper accepted at RLDM, 2022.

24/01/2022 Accepted to AGI Safety Fundamentals course by Effective Altruism Cambridge (EAC). I am taking part in Tech Alignment track.

Research

My research focuses on learning safe reinforcement learning (RL) algorithms grounded in theory which can be extended to the real-world applications. I am also interested in off-policy RL and Constrained MDPs (CMDPs).

	GVFExplorer: Adaptive Exploration for Data-Efficient General Value Function Evaluations Arushi Jain, Josiah P. Hanna, Doina Precup Under Submission, 2024 Paper
	Towards Painless Policy Optimization for Constrained MDPs Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvari, Doina Precup UAI and RLDM, 2022 paper / short RLDM paper / code / RLDM poster
	Variance Penalized On-Policy and Off-Policy Actor-Critic Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup AAAI, 2021 paper / code / talk / slides / poster
	Safe Option-Critic: Learning Safety in the Option-Critic Architecture Arushi Jain, Khimya Khetarpal, Doina Precup Knowledge Engineering Review (KER) Journal, 2021. (Cambridge University Press Journal) Adaptive Learning Agents (ALA) Workshop, ICML, 2018. paper / code / slides / poster
	Safety using Constraint Variance in Policy-Gradient Methods Arushi Jain Master's thesis, McGill University, March 2020. paper
	Safe Actor-Critic Arushi Jain, Ayush Jain, Doina Precup Safety, Risk and Uncertainty in RL Workshop, UAI, 2018. Women in ML (WiML) Workshop, NeurIPS* , 2018. paper / / slides / poster
	Safe Hierarchical Policy Optimization using Constrained Return Variance in Options Arushi Jain, Doina Precup RLDM*, 2019. paper
	Learning Options using Constrained Return Variance Arushi Jain, Doina Precup Safety and Robustness in Decision Making Workshop , NeurIPS*, 2019. paper / poster
	Safe Policy Learning with Constrained Return Variance Arushi Jain* Graduate Student AI Symposium, Canadian AI Conference, 2019. Proceeding published in LNAI Series by Springer Verlag. paper / talk

Reviewer

Reviewer, DARL, ICML Workshop ('22)

Reviewer, AISTATS ('22)

Reviewer, ICLR ('22), ML Evaluation Standards Workshop

Reviewer, NeurIPS ('18), WiML Workshop

Source code and style from Jon Barron's website.