
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
Adversarial robustness has become a fundamental requirement in modern ma...
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ClassWeighted Classification: Tradeoffs and Robust Approaches
We address imbalanced classification, the problem in which a label may h...
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Learning Complexity of Simulated Annealing
Simulated annealing is an effective and general means of optimization. I...
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MACER: Attackfree and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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Optimal Analysis of SubsetSelection Based L_p Low Rank Approximation
We study the low rank approximation problem of any given matrix A over R...
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Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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Adversarially Robust Generalization Just Requires More Unlabeled Data
Neural network robustness has recently been highlighted by the existence...
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BiluLinial stability, certified algorithms and the Independent Set problem
We study the notion of BiluLinial stability in the context of Independe...
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Sample Complexity of Nonparametric SemiSupervised Learning
We study the sample complexity of semisupervised learning (SSL) and int...
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Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Motivated by problems in data clustering, we establish general condition...
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Chen Dan
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