7 citations · 23 across the 11 of their papers we have counts for
12 papers
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Neeraj Gangwar, Anshuka Rangi, Rishabh Deshmukh +3
Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While these methods perform well in single…
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…
Multi-Player Bandits Robust to Adversarial Collisions
Shivakumar Mahesh, Anshuka Rangi, Haifeng Xu +1
Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits has been extensively studied in recent years. In this setting, each player pulls an arm, and receives a r…
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning
Anshuka Rangi, Haifeng Xu, Long Tran-Thanh +1
To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate \emph{any} order-optimal learning algorithm towards…
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
Anshuka Rangi, Long Tran-Thanh, Haifeng Xu +1
We study bandit algorithms under data poisoning attacks in a bounded reward setting. We consider a strong attacker model in which the attacker can observe both the selected actions…