16 citations · 58 across the 27 of their papers we have counts for
12 papers · 1 filter
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen +1
An intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating…
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
Wei Shen, Ruida Zhou, Jing Yang +1
Although transformers have demonstrated impressive capabilities for in-context learning (ICL) in practice, theoretical understanding of the underlying mechanism that allows transfo…
Federated Linear Bandits with Finite Adversarial Actions
Li Fan, Ruida Zhou, Chao Tian +1
We study a federated linear bandits model, where clients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets that…
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
Ruida Zhou, Tao Liu, Min Cheng +3
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testi…
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
Ruida Zhou, Tao Liu, Dileep Kalathil +2
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as propor…
Approximate Top- Arm Identification with Heterogeneous Reward Variances
Ruida Zhou, Chao Tian
We study the effect of reward variance heterogeneity in the approximate top- arm identification setting. In this setting, the reward for the -th arm follows a -sub-Gau…
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
Tao Liu, Ruida Zhou, Dileep Kalathil +2
We address the problem of finding the optimal policy of a constrained Markov decision process (CMDP) using a gradient descent-based algorithm. Previous results have shown that a pr…
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales
Tao Liu, P. R. Kumar, Ruida Zhou +1
Motivated by the problem of learning with small sample sizes, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional…
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
Tao Liu, Ruida Zhou, Dileep Kalathil +2
We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it…
Online Learning with Diverse User Preferences
Chao Gan, Jing Yang, Ruida Zhou +1
In this paper, we investigate the impact of diverse user preference on learning under the stochastic multi-armed bandit (MAB) framework. We aim to show that when the user preferenc…
Cost-aware Cascading Bandits
Ruida Zhou, Chao Gan, Jing Yan +1
In this paper, we propose a cost-aware cascading bandits model, a new variant of multi-armed ban- dits with cascading feedback, by considering the random cost of pulling arms. In e…
Regional Multi-Armed Bandits
Zhiyang Wang, Ruida Zhou, Cong Shen
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter. The arms are divided into different gro…