16 citations · 58 across the 27 of their papers we have counts for
27 papers
Cost-Aware Optimal Pairwise Pure Exploration
Di Wu, Chengshuai Shi, Ruida Zhou +1
Pure exploration is one of the fundamental problems in multi-armed bandits (MAB). However, existing works mostly focus on specific pure exploration tasks, without a holistic view o…
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…
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang +2
Large Language Models (LLMs) rely on the contextual information embedded in examples/demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially l…
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…
Staggered Quantizers for Perfect Perceptual Quality: A Connection between Quantizers with Common Randomness and Without
Ruida Zhou, Chao Tian
The rate-distortion-perception (RDP) framework has attracted significant recent attention due to its application in neural compression. It is important to understand the underlying…
Weakly Private Information Retrieval from Heterogeneously Trusted Servers
Wenyuan Zhao, Yu-Shin Huang, Ruida Zhou +1
We study the problem of weakly private information retrieval (PIR) when there is heterogeneity in servers' trustworthiness under the maximal leakage (Max-L) metric and mutual infor…
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Chengshuai Shi, Ruida Zhou, Kun Yang +1
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original foc…
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…
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
Ruida Zhou, Chao Tian, Tie Liu
We provide a new information-theoretic generalization error bound that is exactly tight (i.e., matching even the constant) for the canonical quadratic Gaussian (location) problem.…
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…
Improved Weakly Private Information Retrieval Codes
Chengyuan Qian, Ruida Zhou, Chao Tian +1
We study the problem of weakly private information retrieval (W-PIR), where a user wishes to retrieve a desired message from non-colluding servers in a way that the privacy lea…
On Top- Selection from -wise Partial Rankings via Borda Counting
Wenjing Chen, Ruida Zhou, Chao Tian +1
We analyze the performance of the Borda counting algorithm in a non-parametric model. The algorithm needs to utilize probabilistic rankings of the items within -sized subsets to…
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…
Stochastic Chaining and Strengthened Information-Theoretic Generalization Bounds
Ruida Zhou, Chao Tian, Tie Liu
We propose a new approach to apply the chaining technique in conjunction with information-theoretic measures to bound the generalization error of machine learning algorithms. Diffe…
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…
Two-Level Private Information Retrieval
Ruida Zhou, Chao Tian, Hua Sun +1
In the conventional robust -colluding private information retrieval (PIR) system, the user needs to retrieve one of the possible messages while keeping the identity of the reque…
Individually Conditional Individual Mutual Information Bound on Generalization Error
Ruida Zhou, Chao Tian, Tie Liu
We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information…
New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems
Tao Guo, Ruida Zhou, Chao Tian
In a private information retrieval (PIR) system, the user needs to retrieve one of the possible messages from a set of storage servers, but wishes to keep the identity of requested…
On the Information Leakage in Private Information Retrieval Systems
Tao Guo, Ruida Zhou, Chao Tian
We consider information leakage to the user in private information retrieval (PIR) systems. Information leakage can be measured in terms of individual message leakage or total leak…
Capacity-Achieving Private Information Retrieval Codes from MDS-Coded Databases with Minimum Message Size
Ruida Zhou, Chao Tian, Hua Sun +1
We consider constructing capacity-achieving linear codes with minimum message size for private information retrieval (PIR) from non-colluding databases, where each message is c…
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…