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Linjun Zhang
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∙ 05/30/2023

How Does Information Bottleneck Help Deep Learning?

Numerous deep learning algorithms have been inspired by and understood v...

0 Kenji Kawaguchi, et al. ∙

∙ 04/08/2023

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks

As machine learning has been deployed ubiquitously across applications i...

0 Yuzhen Mao, et al. ∙

∙ 03/08/2023

HappyMap: A Generalized Multi-calibration Method

Multi-calibration is a powerful and evolving concept originating in the ...

0 Zhun Deng, et al. ∙

∙ 02/13/2023

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data

Language-supervised vision models have recently attracted great attentio...

0 Ryumei Nakada, et al. ∙

∙ 12/27/2022

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions

Rigorous guarantees about the performance of predictive algorithms are n...

13 Jake C. Snell, et al. ∙

∙ 11/08/2022

Reinforcement Learning with Stepwise Fairness Constraints

AI methods are used in societally important settings, ranging from credi...

4 Zhun Deng, et al. ∙

∙ 11/07/2022

Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach

Double-blind peer review mechanism has become the skeleton of academic r...

0 Jiayao Zhang, et al. ∙

∙ 06/27/2022

Robustness Implies Generalization via Data-Dependent Generalization Bounds

This paper proves that robustness implies generalization via data-depend...

0 Kenji Kawaguchi, et al. ∙

∙ 06/06/2022

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

Algorithmic fairness plays an important role in machine learning and imp...

0 Zhun Deng, et al. ∙

∙ 11/04/2021

Scaffolding Sets

Predictors map individual instances in a population to the interval [0,1...

9 Maya Burhanpurkar, et al. ∙

∙ 10/06/2021

An Unconstrained Layer-Peeled Perspective on Neural Collapse

Neural collapse is a highly symmetric geometric pattern of neural networ...

0 Wenlong Ji, et al. ∙

∙ 10/06/2021

The Power of Contrast for Feature Learning: A Theoretical Analysis

Contrastive learning has achieved state-of-the-art performance in variou...

0 Wenlong Ji, et al. ∙

∙ 06/28/2021

Understanding Dynamics of Nonlinear Representation Learning and Its Application

Representations of the world environment play a crucial role in machine ...

0 Kenji Kawaguchi, et al. ∙

∙ 06/18/2021

Adversarial Training Helps Transfer Learning via Better Representations

Transfer learning aims to leverage models pre-trained on source data to ...

0 Zhun Deng, et al. ∙

∙ 02/11/2021

When and How Mixup Improves Calibration

In many machine learning applications, it is important for the model to ...

0 Linjun Zhang, et al. ∙

∙ 10/27/2020

Toward Better Generalization Bounds with Locally Elastic Stability

Classical approaches in learning theory are often seen to yield very loo...

0 Zhun Deng, et al. ∙

∙ 10/20/2020

Towards Understanding the Dynamics of the First-Order Adversaries

An acknowledged weakness of neural networks is their vulnerability to ad...

0 Zhun Deng, et al. ∙

∙ 10/09/2020

How Does Mixup Help With Robustness and Generalization?

Mixup is a popular data augmentation technique based on taking convex co...

7 Linjun Zhang, et al. ∙

∙ 10/03/2020

Interpreting Robust Optimization via Adversarial Influence Functions

Robust optimization has been widely used in nowadays data science, espec...

0 Zhun Deng, et al. ∙

∙ 09/26/2020

Decision-Aware Conditional GANs for Time Series Data

We introduce the decision-aware time-series conditional generative adver...

7 He Sun, et al. ∙

∙ 06/20/2020

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

We investigate the power of censoring techniques, first developed for le...

0 Zhun Deng, et al. ∙

∙ 06/15/2020

Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

Data augmentation by incorporating cheap unlabeled data from multiple do...

0 Zhun Deng, et al. ∙

∙ 06/04/2019

Architecture Selection via the Trade-off Between Accuracy and Robustness

We provide a general framework for characterizing the trade-off between ...

0 Zhun Deng, et al. ∙

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