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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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Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
We study infinite-horizon discounted two-player zero-sum Markov games, a...
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The Relevance of Classic Fuzz Testing: Have We Solved This One?
As fuzz testing has passed its 30th anniversary, and in the face of the ...
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RANDOM MASK: Towards Robust Convolutional Neural Networks
Robustness of neural networks has recently been highlighted by the adver...
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Linear Last-iterate Convergence for Matrix Games and Stochastic Games
Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...
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Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
We develop a new approach to obtaining high probability regret bounds fo...
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Selling Data at an Auction under Privacy Constraints
Private data query combines mechanism design with privacy protection to ...
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A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
We study small-loss bounds for the adversarial multi-armed bandits probl...
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Defective Convolutional Layers Learn Robust CNNs
Robustness of convolutional neural networks has recently been highlighte...
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The Local Dimension of Deep Manifold
Based on our observation that there exists a dramatic drop for the singu...
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