Search Results for author: Cong Shen

Found 20 papers, 5 papers with code

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.

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

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