219 citations · 258 across the 8 of their papers we have counts for
21 papers
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie, Shagun Sodhani, Chelsea Finn +2
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods provide a way to infer the variation from on…
Block Contextual MDPs for Continual Learning
Shagun Sodhani, Franziska Meier, Joelle Pineau +1
In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationary. This assumption of stationarity, whi…
Multi-Task Reinforcement Learning with Context-based Representations
Shagun Sodhani, Amy Zhang, Joelle Pineau
The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to improve performance on any single task. While sharing representa…
IIRC: Incremental Implicitly-Refined Classification
Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani +1
We introduce the "Incremental Implicitly-Refined Classi-fication (IIRC)" setup, an extension to the class incremental learning setup where the incoming batches of classes have two…
A Closer Look at Codistillation for Distributed Training
Shagun Sodhani, Olivier Delalleau, Mahmoud Assran +3
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. T…
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
Shagun Sodhani, Mayoore S. Jaiswal, Lauren Baker +5
This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019. The goal of the report is t…