b'Yiming Ying'

research

∙ 09/10/2023

Outlier Robust Adversarial Training

Supervised learning models are challenged by the intrinsic complexities ...

0 Shu Hu, et al. ∙

research

∙ 07/07/2023

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

Many machine learning tasks can be formulated as a stochastic compositio...

0 Ming Yang, et al. ∙

research

∙ 05/26/2023

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks

Recently, significant progress has been made in understanding the genera...

0 Puyu Wang, et al. ∙

research

∙ 03/16/2023

Fairness-aware Differentially Private Collaborative Filtering

Recently, there has been an increasing adoption of differential privacy ...

0 Zhenhuan Yang, et al. ∙

research

∙ 02/24/2023

Generalization Analysis for Contrastive Representation Learning

Recently, contrastive learning has found impressive success in advancing...

0 Yunwen Lei, et al. ∙

research

∙ 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 ...

0 Puyu Wang, et al. ∙

research

∙ 09/09/2022

Differentially Private Stochastic Gradient Descent with Low-Noise

In this paper, by introducing a low-noise condition, we study privacy an...

0 Puyu Wang, et al. ∙

research

∙ 08/22/2022

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence

The use of machine learning models in consequential decision making ofte...

0 Zhenhuan Yang, et al. ∙

research

∙ 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...

0 Tianbao Yang, et al. ∙

research

∙ 01/22/2022

Differentially Private SGDA for Minimax Problems

Stochastic gradient descent ascent (SGDA) and its variants have been the...

0 Zhenhuan Yang, et al. ∙

research

∙ 11/23/2021

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning

Pairwise learning refers to learning tasks where the loss function depen...

0 Zhenhuan Yang, et al. ∙

research

∙ 06/09/2021

Memory-based Optimization Methods for Model-Agnostic Meta-Learning

Recently, model-agnostic meta-learning (MAML) has garnered tremendous at...

0 Bokun Wang, et al. ∙

research

∙ 06/07/2021

Sum of Ranked Range Loss for Supervised Learning

In forming learning objectives, one oftentimes needs to aggregate a set ...

0 Shu Hu, et al. ∙

research

∙ 05/08/2021

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems

Many machine learning problems can be formulated as minimax problems suc...

0 Yunwen Lei, et al. ∙

research

∙ 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...

0 Zhuoning Yuan, et al. ∙

research

∙ 01/22/2021

Differentially Private SGD with Non-Smooth Loss

In this paper, we are concerned with differentially private SGD algorith...

0 Puyu Wang, et al. ∙

research

∙ 11/04/2020

Stochastic Hard Thresholding Algorithms for AUC Maximization

In this paper, we aim to develop stochastic hard thresholding algorithms...

0 Zhenhuan Yang, et al. ∙

research

∙ 10/05/2020

Learning by Minimizing the Sum of Ranked Range

In forming learning objectives, one oftentimes needs to aggregate a set ...

18 Shu Hu, et al. ∙

research

∙ 09/23/2020

Online AUC Optimization for Sparse High-Dimensional Datasets

The Area Under the ROC Curve (AUC) is a widely used performance measure ...

14 Baojian Zhou, et al. ∙

research

∙ 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...

0 Yunwen Lei, et al. ∙

research

∙ 08/28/2019

Stochastic AUC Maximization with Deep Neural Networks

Stochastic AUC maximization has garnered an increasing interest due to b...

0 Mingrui Liu, et al. ∙

research

∙ 06/14/2019

Stochastic Proximal AUC Maximization

In this paper we consider the problem of maximizing the Area under the R...

0 Yunwen Lei, et al. ∙

research

∙ 05/26/2019

Dual Averaging Method for Online Graph-structured Sparsity

Online learning algorithms update models via one sample per iteration, t...

0 Baojian Zhou, et al. ∙

research

∙ 05/09/2019

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

Stochastic optimization algorithms update models with cheap per-iteratio...

0 Baojian Zhou, et al. ∙

research

∙ 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...

0 Wei Shen, et al. ∙

research

∙ 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...

0 Yunlong Feng, et al. ∙

research

∙ 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...

0 Yunlong Feng, et al. ∙

research

∙ 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...

0 Yanbo Fan, et al. ∙

research

∙ 03/02/2015

Unregularized Online Learning Algorithms with General Loss Functions

In this paper, we consider unregularized online learning algorithms in a...

0 Yiming Ying, et al. ∙

research

∙ 02/25/2015

Online Pairwise Learning Algorithms with Kernels

Pairwise learning usually refers to a learning task which involves a los...

0 Yiming Ying, et al. ∙

research

∙ 07/23/2012

Generalization Bounds for Metric and Similarity Learning

Recently, metric learning and similarity learning have attracted a large...

0 Qiong Cao, et al. ∙

Yiming Ying

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