137 citations · 656 across the 54 of their papers we have counts for
6 papers · 1 filter
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms
Donghwan Lee, Niao He
In this paper, we introduce a unified framework for analyzing a large family of Q-learning algorithms, based on switching system perspectives and ODE-based stochastic approximation…
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Donghwan Lee, Niao He, Parameswaran Kamalaruban +1
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and coo…
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
Yifan Hu, Xin Chen, Niao He
In this paper, we study a class of stochastic optimization problems, referred to as the \emph{Conditional Stochastic Optimization} (CSO), in the form of $\min_{x \in \mathcal{X}} \…
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai +4
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural n…
Target-Based Temporal Difference Learning
Donghwan Lee, Niao He
The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this w…
Quadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs)
Pan Li, Niao He, Olgica Milenkovic
We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs a…