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09/18/2023
Multi-dimensional domain generalization with low-rank structures
In conventional statistical and machine learning methods, it is typicall...
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07/06/2023
What Should Data Science Education Do with Large Language Models?
The rapid advances of large language models (LLMs), such as ChatGPT, are...
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06/13/2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
The surge in multimodal AI's success has sparked concerns over data priv...
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05/29/2023
Beyond Confidence: Reliable Models Should Also Consider Atypicality
While most machine learning models can provide confidence in their predi...
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05/01/2023
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Deep neural networks often rely on spurious correlations to make predict...
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03/13/2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
Achieving optimal statistical performance while ensuring the privacy of ...
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03/08/2023
HappyMap: A Generalized Multi-calibration Method
Multi-calibration is a powerful and evolving concept originating in the ...
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02/13/2023
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
Language-supervised vision models have recently attracted great attentio...
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11/28/2022
FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
Algorithmic fairness plays an increasingly critical role in machine lear...
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11/08/2022
Reinforcement Learning with Stepwise Fairness Constraints
AI methods are used in societally important settings, ranging from credi...
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10/20/2022
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise
The existence of spurious correlations such as image backgrounds in the ...
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10/11/2022
C-Mixup: Improving Generalization in Regression
Improving the generalization of deep networks is an important open chall...
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09/19/2022
Finite- and Large- Sample Inference for Model and Coefficients in High-dimensional Linear Regression with Repro Samples
In this paper, we present a new and effective simulation-based approach ...
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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...
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01/02/2022
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test exam...
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11/04/2021
Scaffolding Sets
Predictors map individual instances in a population to the interval [0,1...
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10/06/2021
The Power of Contrast for Feature Learning: A Theoretical Analysis
Contrastive learning has achieved state-of-the-art performance in variou...
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06/28/2021
Understanding Dynamics of Nonlinear Representation Learning and Its Application
Representations of the world environment play a crucial role in machine ...
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06/18/2021
Adversarial Training Helps Transfer Learning via Better Representations
Transfer learning aims to leverage models pre-trained on source data to ...
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06/04/2021
Meta-Learning with Fewer Tasks through Task Interpolation
Meta-learning enables algorithms to quickly learn a newly encountered ta...
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03/15/2021
A Central Limit Theorem for Differentially Private Query Answering
Perhaps the single most important use case for differential privacy is t...
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02/11/2021
When and How Mixup Improves Calibration
In many machine learning applications, it is important for the model to ...
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11/08/2020
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds
We propose differentially private algorithms for parameter estimation in...
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11/06/2020
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression
This paper studies the high-dimensional mixed linear regression (MLR) wh...
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10/09/2020
How Does Mixup Help With Robustness and Generalization?
Mixup is a popular data augmentation technique based on taking convex co...
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10/03/2020
Interpreting Robust Optimization via Adversarial Influence Functions
Robust optimization has been widely used in nowadays data science, espec...
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09/07/2020
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables
Many data we collect today are in tabular form, with rows as records and...
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06/15/2020
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
Data augmentation by incorporating cheap unlabeled data from multiple do...
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12/05/2019
A Convex Optimization Approach to High-Dimensional Sparse Quadratic Discriminant Analysis
In this paper, we study high-dimensional sparse Quadratic Discriminant A...
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07/02/2019
Double Cross Validation for the Number of Factors in Approximate Factor Models
Determining the number of factors is essential to factor analysis. In th...
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02/12/2019
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
Privacy-preserving data analysis is a rising challenge in contemporary s...
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04/09/2018
High-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data
This paper aims to develop an optimality theory for linear discriminant ...
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02/16/2016