40 citations · 46 across the 8 of their papers we have counts for
4 papers · 1 filter
On Limitation of Transformer for Learning HMMs
Jiachen Hu, Qinghua Liu, Chi Jin
Despite the remarkable success of Transformer-based architectures in various sequential modeling tasks, such as natural language processing, computer vision, and robotics, their ab…
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
Yuanhao Wang, Qinghua Liu, Yu Bai +1
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing lea…
Efficient displacement convex optimization with particle gradient descent
Hadi Daneshmand, Jason D. Lee, Chi Jin
Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of p…
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári +1
This paper introduces a simple efficient learning algorithms for general sequential decision making. The algorithm combines Optimism for exploration with Maximum Likelihood Estimat…