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06/04/2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
We study the problem of learning causal representations from unknown, la...
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06/01/2023
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
We derive an (almost) guaranteed upper bound on the error of deep neural...
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10/08/2022
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
Recent advances in learning aligned multimodal representations have been...
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02/14/2022
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
A common explanation for the failure of deep networks to generalize out-...
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10/21/2021
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Noise-contrastive estimation (NCE) is a statistically consistent method ...
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06/18/2021
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
Domain generalization aims at performing well on unseen test environment...
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02/25/2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
A popular assumption for out-of-distribution generalization is that the ...
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10/12/2020
The Risks of Invariant Risk Minimization
Invariant Causal Prediction (Peters et al., 2016) is a technique for out...
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07/10/2020
Self-Reflective Variational Autoencoder
The Variational Autoencoder (VAE) is a powerful framework for learning p...
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02/07/2020
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Machine learning algorithms are known to be susceptible to data poisonin...
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05/31/2019
Human-Usable Password Schemas: Beyond Information-Theoretic Security
Password users frequently employ passwords that are too simple, or they ...
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02/08/2019