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07/11/2023
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Different distribution shifts require different algorithmic and operatio...
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05/18/2023
Modeling Interference Using Experiment Roll-out
Experiments on online marketplaces and social networks suffer from inter...
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03/21/2023
Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches
Standard bandit algorithms that assume continual reallocation of measure...
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03/03/2023
Diagnosing Model Performance Under Distribution Shift
Prediction models can perform poorly when deployed to target distributio...
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02/03/2023
An Operational Perspective to Fairness Interventions: Where and How to Intervene
As AI-based decision systems proliferate, their successful operationaliz...
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12/13/2022
Minimax Optimal Estimation of Stability Under Distribution Shift
The performance of decision policies and prediction models often deterio...
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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...
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09/04/2021
Robust fine-tuning of zero-shot models
Large pre-trained models such as CLIP offer consistent accuracy across a...
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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 ...
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07/28/2020
Distributionally Robust Losses for Latent Covariate Mixtures
While modern large-scale datasets often consist of heterogeneous subpopu...
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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...
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03/12/2020
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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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...
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10/31/2018
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
While recent developments in autonomous vehicle (AV) technology highligh...
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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...
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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...
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06/20/2018
Fairness Without Demographics in Repeated Loss Minimization
Machine learning models (e.g., speech recognizers) are usually trained t...
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05/30/2018
Generalizing to Unseen Domains via Adversarial Data Augmentation
We are concerned with learning models that generalize well to different ...
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10/29/2017
Certifiable Distributional Robustness with Principled Adversarial Training
Neural networks are vulnerable to adversarial examples and researchers h...
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10/11/2016
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
We study statistical inference and robust solution methods for stochasti...
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10/08/2016