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09/10/2023
Outlier Robust Adversarial Training
Supervised learning models are challenged by the intrinsic complexities ...
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07/07/2023
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Many machine learning tasks can be formulated as a stochastic compositio...
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05/26/2023
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
Recently, significant progress has been made in understanding the genera...
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03/16/2023
Fairness-aware Differentially Private Collaborative Filtering
Recently, there has been an increasing adoption of differential privacy ...
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02/24/2023
Generalization Analysis for Contrastive Representation Learning
Recently, contrastive learning has found impressive success in advancing...
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09/16/2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Recently there is a large amount of work devoted to the study of Markov ...
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09/09/2022
Differentially Private Stochastic Gradient Descent with Low-Noise
In this paper, by introducing a low-noise condition, we study privacy an...
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08/22/2022
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
The use of machine learning models in consequential decision making ofte...
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03/28/2022
AUC Maximization in the Era of Big Data and AI: A Survey
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessi...
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01/22/2022
Differentially Private SGDA for Minimax Problems
Stochastic gradient descent ascent (SGDA) and its variants have been the...
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11/23/2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
Pairwise learning refers to learning tasks where the loss function depen...
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06/09/2021
Memory-based Optimization Methods for Model-Agnostic Meta-Learning
Recently, model-agnostic meta-learning (MAML) has garnered tremendous at...
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06/07/2021
Sum of Ranked Range Loss for Supervised Learning
In forming learning objectives, one oftentimes needs to aggregate a set ...
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05/08/2021
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Many machine learning problems can be formulated as minimax problems suc...
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02/09/2021
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
eep UC (area under the ROC curve) aximization (DAM) has attracted much a...
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01/22/2021
Differentially Private SGD with Non-Smooth Loss
In this paper, we are concerned with differentially private SGD algorith...
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11/04/2020
Stochastic Hard Thresholding Algorithms for AUC Maximization
In this paper, we aim to develop stochastic hard thresholding algorithms...
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10/05/2020
Learning by Minimizing the Sum of Ranked Range
In forming learning objectives, one oftentimes needs to aggregate a set ...
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09/23/2020
Online AUC Optimization for Sparse High-Dimensional Datasets
The Area Under the ROC Curve (AUC) is a widely used performance measure ...
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06/15/2020
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Recently there are a considerable amount of work devoted to the study of...
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08/28/2019
Stochastic AUC Maximization with Deep Neural Networks
Stochastic AUC maximization has garnered an increasing interest due to b...
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06/14/2019
Stochastic Proximal AUC Maximization
In this paper we consider the problem of maximizing the Area under the R...
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05/26/2019
Dual Averaging Method for Online Graph-structured Sparsity
Online learning algorithms update models via one sample per iteration, t...
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05/09/2019
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Stochastic optimization algorithms update models with cheap per-iteratio...
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04/25/2019
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning
In this paper we study the stability and its trade-off with optimization...
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03/01/2018
Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise
In recent years, correntropy and its applications in machine learning ha...
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03/01/2018
Learning with Correntripy-induced Losses for Regression with Mixture of Symmetric Stable Noise
In recent years, correntropy and its applications in machine learning ha...
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05/24/2017
Learning with Average Top-k Loss
In this work, we introduce the average top-k (AT_k) loss as a new ensemb...
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03/02/2015
Unregularized Online Learning Algorithms with General Loss Functions
In this paper, we consider unregularized online learning algorithms in a...
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02/25/2015
Online Pairwise Learning Algorithms with Kernels
Pairwise learning usually refers to a learning task which involves a los...
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07/23/2012
