Hongseok Namkoong

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Stanford University

Research Assistant at Stanford University

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning

Thompson sampling (TS) has emerged as a robust technique for contextual ...
11/29/2020 ∙ by Hongseok Namkoong, et al. ∙ 0 ∙ share

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Distributionally Robust Losses for Latent Covariate Mixtures

While modern large-scale datasets often consist of heterogeneous subpopu...
07/28/2020 ∙ by John Duchi, et al. ∙ 0 ∙ share

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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...
07/05/2020 ∙ by Sookyo Jeong, et al. ∙ 0 ∙ share

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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...
03/12/2020 ∙ by Hongseok Namkoong, et al. ∙ 5 ∙ share

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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...
12/02/2018 ∙ by Matthew O'Kelly, et al. ∙ 0 ∙ share

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Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation

While recent developments in autonomous vehicle (AV) technology highligh...
10/31/2018 ∙ by Matthew O'Kelly, et al. ∙ 0 ∙ share

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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...
10/20/2018 ∙ by John Duchi, et al. ∙ 0 ∙ share

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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...
08/28/2018 ∙ by Steve Yadlowsky, et al. ∙ 0 ∙ share

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Fairness Without Demographics in Repeated Loss Minimization

Machine learning models (e.g., speech recognizers) are usually trained t...
06/20/2018 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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Generalizing to Unseen Domains via Adversarial Data Augmentation

We are concerned with learning models that generalize well to different ...
05/30/2018 ∙ by Riccardo Volpi, et al. ∙ 10 ∙ share

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Certifiable Distributional Robustness with Principled Adversarial Training

Neural networks are vulnerable to adversarial examples and researchers h...
10/29/2017 ∙ by Aman Sinha, et al. ∙ 0 ∙ share

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Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach

We study statistical inference and robust solution methods for stochasti...
10/11/2016 ∙ by John Duchi, et al. ∙ 0 ∙ share

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Variance-based regularization with convex objectives

We develop an approach to risk minimization and stochastic optimization ...
10/08/2016 ∙ by John Duchi, et al. ∙ 0 ∙ share

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Hongseok Namkoong

Featured Co-authors

Percy Liang

Silvio Savarese

Emma Brunskill

Vittorio Murino

Russ Tedrake

John Duchi

Riccardo Volpi

Ozan Sener

Eytan Bakshy

Tatsunori B. Hashimoto

Aman Sinha

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning

Distributionally Robust Losses for Latent Covariate Mixtures

Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation

Learning Models with Uniform Performance via Distributionally Robust Optimization

Bounds on the conditional and average treatment effect in the presence of unobserved confounders

Fairness Without Demographics in Repeated Loss Minimization

Generalizing to Unseen Domains via Adversarial Data Augmentation

Certifiable Distributional Robustness with Principled Adversarial Training

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach

Variance-based regularization with convex objectives