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05/24/2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility
We propose a general learning framework for the protection mechanisms th...
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05/07/2023
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
Federated learning (FL) enables participating parties to collaboratively...
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04/11/2023
A Game-theoretic Framework for Federated Learning
In federated learning, benign participants aim to optimize a global mode...
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04/10/2023
Probably Approximately Correct Federated Learning
Federated learning (FL) is a new distributed learning paradigm, with pri...
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03/11/2022
No free lunch theorem for security and utility in federated learning
In a federated learning scenario where multiple parties jointly learn a ...
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06/19/2021
Variance-Dependent Best Arm Identification
We study the problem of identifying the best arm in a stochastic multi-a...
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02/11/2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
In this work, we develop linear bandit algorithms that automatically ada...
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06/15/2020
Improved algorithm for permutation testing
We study the problem of testing forbidden patterns. The patterns that ar...
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05/13/2020
Adaptive Double-Exploration Tradeoff for Outlier Detection
We study a variant of the thresholding bandit problem (TBP) in the conte...
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12/02/2019
Improved Algorithm for Tolerant Junta Testing
In this paper, we consider the problem of tolerant junta testing for boo...
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11/26/2019
Contextual Combinatorial Conservative Bandits
The problem of multi-armed bandits (MAB) asks to make sequential decisio...
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11/25/2019
Near-Optimal Algorithm for Distribution-Free Junta Testing
In this paper, We firstly present an algorithm for the problem of distri...
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11/25/2019