11 citations · 39 across the 30 of their papers we have counts for
7 papers · 1 filter
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks
Tao Li, Henger Li, Yunian Pan +3
Federated learning (FL) is susceptible to a range of security threats. Although various defense mechanisms have been proposed, they are typically non-adaptive and tailored to speci…
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning
Tao Li, Juan Guevara, Xinhong Xie +1
Offline reinforcement learning (RL) suffers from the distribution shift between the offline dataset and the online environment. In multi-agent RL (MARL), this distribution shift ma…
A First Order Meta Stackelberg Method for Robust Federated Learning
Yunian Pan, Tao Li, Henger Li +3
Previous research has shown that federated learning (FL) systems are exposed to an array of security risks. Despite the proposal of several defensive strategies, they tend to be no…
Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study
Yunian Pan, Tao Li, Quanyan Zhu
\textit{Intelligent Navigation Systems} (INS) are exposed to an increasing number of informational attack vectors, which often intercept through the communication channels between…
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis
Tao Li, Haozhe Lei, Quanyan Zhu
Meta reinforcement learning (meta RL), as a combination of meta-learning ideas and reinforcement learning (RL), enables the agent to adapt to different tasks using a few samples. H…
Blackwell Online Learning for Markov Decision Processes
Tao Li, Guanze Peng, Quanyan Zhu
This work provides a novel interpretation of Markov Decision Processes (MDP) from the online optimization viewpoint. In such an online optimization context, the policy of the MDP i…
On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning
Tao Li, Quanyan Zhu
In this paper, we propose a generic framework for devising an adaptive approximation scheme for value function approximation in reinforcement learning, which introduces multiscale…