Search Results for author: Cong Shen

Found 44 papers, 10 papers with code

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets

no code implementations19 Nov 2023 Kun Yang, Cong Shen, Jing Yang, Shu-ping Yeh, Jerry Sydir

We observe that the performance of offline RL for the RRM problem depends critically on the behavior policy used for data collection, and further propose a novel offline RL solution that leverages heterogeneous datasets collected by different behavior policies.

Management Offline RL +4

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization

no code implementations2 Nov 2023 Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen

In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.

Federated Learning

Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction

1 code implementation23 Jun 2023 Cong Shen, Pingjian Ding, JunJie Wee, Jialin Bi, Jiawei Luo, Kelin Xia

The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negativecurvature ratios, network densities, and network sizes (when larger than 500).

Torsion Graph Neural Networks

1 code implementation23 Jun 2023 Cong Shen, Xiang Liu, Jiawei Luo, Kelin Xia

This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs.

Link Prediction Node Classification

Molecular geometric deep learning

1 code implementation22 Jun 2023 Cong Shen, Jiawei Luo, Kelin Xia

This demonstrates the great potential of novel molecular representations beyond the de facto standard of covalent-bond-based molecular graphs.

Molecular Property Prediction molecular representation +1

Differentially Private Wireless Federated Learning Using Orthogonal Sequences

no code implementations14 Jun 2023 Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor

A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.

Federated Learning Privacy Preserving

Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources

no code implementations14 Jun 2023 Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

Then, a novel HetPEVI algorithm is proposed, which simultaneously considers the sample uncertainties from a finite number of data samples per data source and the source uncertainties due to a finite number of available data sources.

Offline RL reinforcement-learning +1

Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints

no code implementations9 Jun 2023 Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang

This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the learning process.


On High-dimensional and Low-rank Tensor Bandits

no code implementations6 May 2023 Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos

To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank.

Recommendation Systems Vocal Bursts Intensity Prediction

Reward Teaching for Federated Multi-armed Bandits

no code implementations3 May 2023 Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

Rigorous analyses demonstrate that when facing clients with UCB1, TWL outperforms TAL in terms of the dependencies on sub-optimality gaps thanks to its adaptive design.

Multi-Armed Bandits

Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning

no code implementations21 Apr 2023 Jieming Bian, Cong Shen, Jie Xu

The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection.

Federated Learning

Accelerating Hybrid Federated Learning Convergence under Partial Participation

no code implementations10 Apr 2023 Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu

In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.

Federated Learning

Federated Learning via Indirect Server-Client Communications

no code implementations14 Feb 2023 Jieming Bian, Cong Shen, Jie Xu

In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e. g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients.

Federated Learning Privacy Preserving

Communication and Storage Efficient Federated Split Learning

no code implementations11 Feb 2023 Yujia Mu, Cong Shen

Federated Split Learning (FSL) preserves the parallel model training principle of FL, with a reduced device computation requirement thanks to splitting the ML model between the server and clients.

Federated Learning

Cross-Layer Federated Learning Optimization in MIMO Networks

no code implementations4 Feb 2023 Sihua Wang, Mingzhe Chen, Cong Shen, Changchuan Yin, Christopher G. Brinton

The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices.

Federated Learning

Random Orthogonalization for Federated Learning in Massive MIMO Systems

no code implementations18 Oct 2022 Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor

We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.

Federated Learning

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games

no code implementations4 Oct 2022 Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang

Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle.

Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game

no code implementations31 May 2022 Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, LiWei Wang, Tong Zhang

We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm.

Offline RL Reinforcement Learning (RL)

On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting

no code implementations11 Apr 2022 Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen

In the special case of $m=2$, i. e., pairwise comparison, the resultant bound is tighter than that given by Shah et al., leading to a reduced gap between the error probability upper and lower bounds.

On Federated Learning with Energy Harvesting Clients

no code implementations12 Feb 2022 Cong Shen, Jing Yang, Jie Xu

Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper.

Federated Learning Scheduling

Learning for Robust Combinatorial Optimization: Algorithm and Application

no code implementations20 Dec 2021 Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren

Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity than conventional solvers.

Combinatorial Optimization Edge-computing

(Almost) Free Incentivized Exploration from Decentralized Learning Agents

1 code implementation NeurIPS 2021 Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen

In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications.

Multi-Armed Bandits

Federated Linear Contextual Bandits

no code implementations NeurIPS 2021 Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen

This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters.

Multi-Armed Bandits

Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions

no code implementations25 Jun 2021 Chengshuai Shi, Cong Shen

We study a new stochastic multi-player multi-armed bandits (MP-MAB) problem, where the reward distribution changes if a collision occurs on the arm.

Multi-Armed Bandits

Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

no code implementations14 Apr 2021 Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen

We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).

Federated Learning

Federated Multi-armed Bandits with Personalization

1 code implementation25 Feb 2021 Chengshuai Shi, Cong Shen, Jing Yang

A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of FL with personalization.

Federated Learning Multi-Armed Bandits

Federated Multi-Armed Bandits

1 code implementation28 Jan 2021 Chengshuai Shi, Cong Shen

We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution.

Federated Learning Multi-Armed Bandits +1

SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups

no code implementations26 Jan 2021 Hyun-Suk Lee, Cong Shen, William Zame, Jang-Won Lee, Mihaela van der Schaar

Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).

Federated Learning over Noisy Channels: Convergence Analysis and Design Examples

no code implementations6 Jan 2021 Xizixiang Wei, Cong Shen

Does Federated Learning (FL) work when both uplink and downlink communications have errors?

Federated Learning

Design and Analysis of Uplink and Downlink Communications for Federated Learning

no code implementations7 Dec 2020 Sihui Zheng, Cong Shen, Xiang Chen

Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous - a carefully designed quantization and transmission achieves more than 98% of the floating-point baseline accuracy with fewer than 10% of the baseline bandwidth, for majority of the experiments on both i. i. d.

Federated Learning Quantization

On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications

no code implementations2 Nov 2020 Chengshuai Shi, Cong Shen

Instead of focusing on the hardness of multiple players, we introduce a new dimension of hardness, called attackability.

Multi-Armed Bandits

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

1 code implementation NeurIPS 2020 Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar

Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.

Recommendation Systems

Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

no code implementations ICML 2020 Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar

Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex.

Stochastic Linear Contextual Bandits with Diverse Contexts

no code implementations5 Mar 2020 Weiqiang Wu, Jing Yang, Cong Shen

In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits.

Multi-Armed Bandits

Decentralized Multi-player Multi-armed Bandits with No Collision Information

no code implementations29 Feb 2020 Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper.

Multi-Armed Bandits

Contextual Constrained Learning for Dose-Finding Clinical Trials

1 code implementation8 Jan 2020 Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar

In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.

A Regression Approach to Certain Information Transmission Problems

no code implementations10 Jun 2019 Wenyi Zhang, Yizhu Wang, Cong Shen, Ning Liang

A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information.

BIG-bench Machine Learning regression

Online Learning with Diverse User Preferences

no code implementations23 Jan 2019 Chao Gan, Jing Yang, Ruida Zhou, Cong Shen

We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant.

Towards Optimal Power Control via Ensembling Deep Neural Networks

1 code implementation26 Jul 2018 Fei Liang, Cong Shen, Wei Yu, Feng Wu

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel.

Cost-aware Cascading Bandits

no code implementations22 May 2018 Ruida Zhou, Chao Gan, Jing Yan, Cong Shen

For the online setting, we propose a Cost-aware Cas- cading Upper Confidence Bound (CC-UCB) algo- rithm, and show that the cumulative regret scales in O(log T ).

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

no code implementations11 Apr 2018 Chao Gan, Ruida Zhou, Jing Yang, Cong Shen

Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i. e., reward-minus-cost).

Regional Multi-Armed Bandits

no code implementations22 Feb 2018 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.

Multi-Armed Bandits

An Iterative BP-CNN Architecture for Channel Decoding

no code implementations18 Jul 2017 Fei Liang, Cong Shen, Feng Wu

The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise.

Noise Estimation Test

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