research
∙
07/19/2023
Contextual Reliability: When Different Features Matter in Different Contexts
Deep neural networks often fail catastrophically by relying on spurious ...
share
research
∙
06/19/2023
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts
Effective machine learning models learn both robust features that direct...
research
∙
02/10/2023
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features
Conventional approaches to robustness try to learn a model based on caus...
research
∙
02/06/2023
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts
Training machine learning models robust to distribution shifts is critic...
research
∙
06/03/2022
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Supervised learning methods trained with maximum likelihood objectives o...
research
∙
02/23/2021
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods
In this work we introduce a simple baseline for meta-learning. Our uncon...
research
∙
11/28/2020
Is Support Set Diversity Necessary for Meta-Learning?
Meta-learning is a popular framework for learning with limited data in w...
research
∙
08/18/2020
Robust Handwriting Recognition with Limited and Noisy Data
Despite the advent of deep learning in computer vision, the general hand...
research
∙
07/25/2020
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations
This paper extends recent work on nonlinear Independent Component Analys...
research
∙
07/06/2020
Covariate Distribution Aware Meta-learning
Meta-learning has proven to be successful at few-shot learning across th...
research
∙
04/29/2020
Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves c...
research
∙
10/22/2019
