23 citations · 72 across the 15 of their papers we have counts for
15 papers
RewardRank: Optimizing True Learning-to-Rank Utility
Gaurav Bhatt, Kiran Koshy Thekumparampil, Tanmay Gangwani +2
Traditional ranking systems optimize offline proxy objectives that rely on oversimplified assumptions about user behavior, often neglecting factors such as position bias and item d…
Knowledge Distillation with Training Wheels
Guanlin Liu, Anand Ramachandran, Tanmay Gangwani +2
Knowledge distillation is used, in generative language modeling, to train a smaller student model using the help of a larger teacher model, resulting in improved capabilities for t…
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Yinuo Ren, Tesi Xiao, Tanmay Gangwani +4
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO…
Selective Uncertainty Propagation in Offline RL
Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya +3
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algo…
Imitation Learning from Observations under Transition Model Disparity
Tanmay Gangwani, Yuan Zhou, Jian Peng
Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills wit…
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan, Jian Peng, Tanmay Gangwani
Meta-reinforcement learning (meta-RL) algorithms allow for agents to learn new behaviors from small amounts of experience, mitigating the sample inefficiency problem in RL. However…
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
Tanmay Gangwani, Jian Peng, Yuan Zhou
Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning. It facilitates learning a population of agents where each membe…
Learning Guidance Rewards with Trajectory-space Smoothing
Tanmay Gangwani, Yuan Zhou, Jian Peng
Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that…
Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch
Michael Wan, Tanmay Gangwani, Jian Peng
Deep reinforcement learning (RL) algorithms have achieved great success on a wide variety of sequential decision-making tasks. However, many of these algorithms suffer from high sa…
State-only Imitation with Transition Dynamics Mismatch
Tanmay Gangwani, Jian Peng
Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a…
Learning Belief Representations for Imitation Learning in POMDPs
Tanmay Gangwani, Joel Lehman, Qiang Liu +1
We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the…
Partial Redundancy Elimination using Lazy Code Motion
Sandeep Dasgupta, Tanmay Gangwani
Partial Redundancy Elimination (PRE) is a compiler optimization that eliminates expressions that are redundant on some but not necessarily all paths through a program. In this proj…
Distributed and Secure ML with Self-tallying Multi-party Aggregation
Yunhui Long, Tanmay Gangwani, Haris Mughees +1
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure m…
Learning Self-Imitating Diverse Policies
Tanmay Gangwani, Qiang Liu, Jian Peng
The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at ea…
Policy Optimization by Genetic Distillation
Tanmay Gangwani, Jian Peng
Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide eff…