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08/30/2023
Emergence of Segmentation with Minimalistic White-Box Transformers
Transformer-like models for vision tasks have recently proven effective ...
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07/25/2023
Scaff-PD: Communication Efficient Fair and Robust Federated Learning
We present Scaff-PD, a fast and communication-efficient algorithm for di...
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06/15/2023
ViP: A Differentially Private Foundation Model for Computer Vision
Artificial intelligence (AI) has seen a tremendous surge in capabilities...
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06/01/2023
White-Box Transformers via Sparse Rate Reduction
In this paper, we contend that the objective of representation learning ...
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05/27/2023
Federated Conformal Predictors for Distributed Uncertainty Quantification
Conformal prediction is emerging as a popular paradigm for providing rig...
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07/13/2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
State-of-the-art federated learning methods can perform far worse than t...
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05/23/2022
Conditional Supervised Contrastive Learning for Fair Text Classification
Contrastive representation learning has gained much attention due to its...
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05/15/2022
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Motivated by applications to online learning in sparse estimation and Ba...
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04/07/2022
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning
We investigate and leverage a connection between Differential Privacy (D...
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02/11/2022
Predicting Out-of-Distribution Error with the Projection Norm
We propose a metric – Projection Norm – to predict a model's performance...
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12/08/2021
The Effect of Model Size on Worst-Group Generalization
Overparameterization is shown to result in poor test accuracy on rare su...
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11/12/2021
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
This work proposes a new computational framework for learning an explici...
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06/30/2021
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging
We study the stochastic bilinear minimax optimization problem, presentin...
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05/21/2021
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
This work attempts to provide a plausible theoretical framework that aim...
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04/27/2021
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
Distributionally robust supervised learning (DRSL) is emerging as a key ...
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03/17/2021
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Adversarially trained models exhibit a large generalization gap: they ca...
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10/27/2020
Deep Networks from the Principle of Rate Reduction
This work attempts to interpret modern deep (convolutional) networks fro...
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07/09/2020
Boundary thickness and robustness in learning models
Robustness of machine learning models to various adversarial and non-adv...
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06/15/2020
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
To learn intrinsic low-dimensional structures from high-dimensional data...
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02/26/2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
The classical bias-variance trade-off predicts that bias decreases and v...
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01/24/2019
Theoretically Principled Trade-off between Robustness and Accuracy
We identify a trade-off between robustness and accuracy that serves as a...
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06/20/2018
Learning One-hidden-layer ReLU Networks via Gradient Descent
We study the problem of learning one-hidden-layer neural networks with R...
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12/18/2017
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
We propose stochastic optimization algorithms that can find local minima...
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12/11/2017
