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05/28/2023
Targeted Data Generation: Finding and Fixing Model Weaknesses
Even when aggregate accuracy is high, state-of-the-art NLP models often ...
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05/20/2023
Collaborative Development of NLP models
Despite substantial advancements, Natural Language Processing (NLP) mode...
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09/30/2022
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
A fundamental challenge of over-parameterized deep learning models is le...
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07/04/2022
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Batch normalization (BN) is a ubiquitous technique for training deep neu...
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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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12/07/2020
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
The presence of spurious features interferes with the goal of obtaining ...
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11/22/2019
Noise Induces Loss Discrepancy Across Groups for Linear Regression
We study the effect of feature noise (measurement error) on the discrepa...
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06/08/2019
Maximum Weighted Loss Discrepancy
Though machine learning algorithms excel at minimizing the average loss ...
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05/30/2018
Planning, Inference and Pragmatics in Sequential Language Games
We study sequential language games in which two players, each with priva...
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06/20/2016
