Arushi Jain

I am second year Computer Science (Artificial Intelligence) Ph.D. candidate with Doina Precup and Pierre-Luc Bacon in Reasoning and Learning Lab (RLLab) at McGill University and Mila, Montreal , Canada.

I am currently hosted by Alessandro Lazaric for internship at Meta AI Research Lab (FAIR) in Paris. I am working 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

profile photo
Highlights and News

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).

3DSP 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
3DSP 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
3DSP 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
3DSP Safety using Constraint Variance in Policy-Gradient Methods
Arushi Jain
Master's thesis, McGill University, March 2020.
3DSP 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
3DSP Safe Hierarchical Policy Optimization using Constrained Return Variance in Options
Arushi Jain*, Doina Precup
RLDM, 2019.
3DSP Learning Options using Constrained Return Variance
Arushi Jain*, Doina Precup
Safety and Robustness in Decision Making Workshop , NeurIPS, 2019.
paper / poster
3DSP 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
sym 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.