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06/08/2023
Conservative Prediction via Data-Driven Confidence Minimization
Errors of machine learning models are costly, especially in safety-criti...
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12/01/2022
Finetune like you pretrain: Improved finetuning of zero-shot vision models
Finetuning image-text models such as CLIP achieves state-of-the-art accu...
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11/25/2022
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?
As the scope of machine learning broadens, we observe a recurring theme ...
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11/17/2022
How to Fine-Tune Vision Models with SGD
SGD (with momentum) and AdamW are the two most used optimizers for fine-...
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11/16/2022
Holistic Evaluation of Language Models
Language models (LMs) are becoming the foundation for almost all major l...
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10/20/2022
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
A common approach to transfer learning under distribution shift is to fi...
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10/12/2022
Are Sample-Efficient NLP Models More Robust?
Recent work has observed that pre-trained models have higher out-of-dist...
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07/18/2022
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
We often see undesirable tradeoffs in robust machine learning where out-...
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04/06/2022
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Contrastive learning is a highly effective method which uses unlabeled d...
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04/01/2022
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
We consider unsupervised domain adaptation (UDA), where labeled data fro...
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02/21/2022
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
When transferring a pretrained model to a downstream task, two popular m...
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12/09/2021
Extending the WILDS Benchmark for Unsupervised Adaptation
Machine learning systems deployed in the wild are often trained on a sou...
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09/12/2021
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Out-of-distribution detection is an important component of reliable ML s...
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12/08/2020
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Consider a prediction setting where a few inputs (e.g., satellite images...
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10/27/2020
Selective Classification Can Magnify Disparities Across Groups
Selective classification, in which models are allowed to abstain on unce...
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06/17/2020
Self-training Avoids Using Spurious Features Under Domain Shift
In unsupervised domain adaptation, existing theory focuses on situations...
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02/26/2020
Understanding Self-Training for Gradual Domain Adaptation
Machine learning systems must adapt to data distributions that evolve ov...
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09/23/2019
Verified Uncertainty Calibration
Applications such as weather forecasting and personalized medicine deman...
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12/04/2018
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
This paper addresses the problem of evaluating learning systems in safet...
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07/05/2018
Consistent Generative Query Networks
Stochastic video prediction is usually framed as an extrapolation proble...
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12/12/2017