Hongseok Namkoong

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Stanford University

Research Assistant at Stanford University

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∙ 03/10/2022

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

The conventional recipe for maximizing model accuracy is to (1) train mu...

Mitchell Wortsman, et al. ∙

∙ 09/04/2021

Robust fine-tuning of zero-shot models

Large pre-trained models such as CLIP offer consistent accuracy across a...

Mitchell Wortsman, et al. ∙

∙ 11/29/2020

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

Thompson sampling (TS) has emerged as a robust technique for contextual ...

Hongseok Namkoong, et al. ∙

∙ 07/28/2020

Distributionally Robust Losses for Latent Covariate Mixtures

While modern large-scale datasets often consist of heterogeneous subpopu...

John Duchi, et al. ∙

∙ 07/05/2020

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

We propose the worst-case treatment effect (WTE) across all subpopulatio...

Sookyo Jeong, et al. ∙

∙ 03/12/2020

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

When observed decisions depend only on observed features, off-policy pol...

Hongseok Namkoong, et al. ∙

∙ 12/02/2018

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...

Matthew O'Kelly, et al. ∙

∙ 10/31/2018

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

While recent developments in autonomous vehicle (AV) technology highligh...

Matthew O'Kelly, et al. ∙

∙ 10/20/2018

Learning Models with Uniform Performance via Distributionally Robust Optimization

A common goal in statistics and machine learning is to learn models that...

John Duchi, et al. ∙

∙ 08/28/2018

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...

Steve Yadlowsky, et al. ∙

∙ 06/20/2018

Fairness Without Demographics in Repeated Loss Minimization

Machine learning models (e.g., speech recognizers) are usually trained t...

Tatsunori B. Hashimoto, et al. ∙

∙ 05/30/2018

Generalizing to Unseen Domains via Adversarial Data Augmentation

We are concerned with learning models that generalize well to different ...

Riccardo Volpi, et al. ∙

∙ 10/29/2017

Certifiable Distributional Robustness with Principled Adversarial Training

Neural networks are vulnerable to adversarial examples and researchers h...

Aman Sinha, et al. ∙

∙ 10/11/2016

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach

We study statistical inference and robust solution methods for stochasti...

John Duchi, et al. ∙

∙ 10/08/2016

Variance-based regularization with convex objectives

We develop an approach to risk minimization and stochastic optimization ...

John Duchi, et al. ∙

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