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Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
Thompson sampling (TS) has emerged as a robust technique for contextual ...
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Distributionally Robust Losses for Latent Covariate Mixtures
While modern large-scale datasets often consist of heterogeneous subpopu...
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Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects
We propose the worst-case treatment effect (WTE) across all subpopulatio...
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Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
When observed decisions depend only on observed features, off-policy pol...
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In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation
Modern treatments for Type 1 diabetes (T1D) use devices known as artific...
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Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
While recent developments in autonomous vehicle (AV) technology highligh...
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Learning Models with Uniform Performance via Distributionally Robust Optimization
A common goal in statistics and machine learning is to learn models that...
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Bounds on the conditional and average treatment effect in the presence of unobserved confounders
The causal effect of an intervention can not be consistently estimated w...
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Fairness Without Demographics in Repeated Loss Minimization
Machine learning models (e.g., speech recognizers) are usually trained t...
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Generalizing to Unseen Domains via Adversarial Data Augmentation
We are concerned with learning models that generalize well to different ...
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Certifiable Distributional Robustness with Principled Adversarial Training
Neural networks are vulnerable to adversarial examples and researchers h...
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Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
We study statistical inference and robust solution methods for stochasti...
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Variance-based regularization with convex objectives
We develop an approach to risk minimization and stochastic optimization ...
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