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05/17/2023
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
The mixture proportions of pretraining data domains (e.g., Wikipedia, bo...
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02/27/2023
Reward Design with Language Models
Reward design in reinforcement learning (RL) is challenging since specif...
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02/06/2023
Data Selection for Language Models via Importance Resampling
Selecting a suitable training dataset is crucial for both general-domain...
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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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10/25/2022
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Language modeling on large-scale datasets leads to impressive performanc...
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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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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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11/03/2021
An Explanation of In-context Learning as Implicit Bayesian Inference
Large pretrained language models such as GPT-3 have the surprising abili...
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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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06/17/2021
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
Pretrained language models have achieved state-of-the-art performance wh...
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12/14/2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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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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06/29/2020
Simplifying Models with Unlabeled Output Data
We focus on prediction problems with high-dimensional outputs that are s...
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02/25/2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy
Adversarial training augments the training set with perturbations to imp...
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06/14/2019
Adversarial Training Can Hurt Generalization
While adversarial training can improve robust accuracy (against an adver...
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01/29/2019
Differentiable Subset Sampling
Many machine learning tasks require sampling a subset of items from a co...
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05/26/2018
