b'Tianbao Yang'

research

∙ 08/29/2023

Everything Perturbed All at Once: Enabling Differentiable Graph Attacks

As powerful tools for representation learning on graphs, graph neural ne...

0 Haoran Liu, 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

∙ 06/13/2023

Learning Unnormalized Statistical Models via Compositional Optimization

Learning unnormalized statistical models (e.g., energy-based models) is ...

0 Wei Jiang, et al. ∙

research

∙ 06/05/2023

LibAUC: A Deep Learning Library for X-Risk Optimization

This paper introduces the award-winning deep learning (DL) library calle...

0 Zhuoning Yuan, et al. ∙

research

∙ 05/30/2023

Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization

In this paper, we consider non-convex multi-block bilevel optimization (...

0 Quanqi Hu, et al. ∙

research

∙ 05/19/2023

Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization

In this paper, we aim to optimize a contrastive loss with individualized...

0 Zi-Hao Qiu, et al. ∙

research

∙ 05/14/2023

Provable Multi-instance Deep AUC Maximization with Stochastic Pooling

This paper considers a novel application of deep AUC maximization (DAM) ...

0 Dixain Zhu, et al. ∙

research

∙ 04/20/2023

Federated Compositional Deep AUC Maximization

Federated learning has attracted increasing attention due to the promise...

0 Xinwen Zhang, 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

∙ 02/18/2023

Stochastic Approximation Approaches to Group Distributionally Robust Optimization

This paper investigates group distributionally robust optimization (GDRO...

0 Moshe Y. Vardi, et al. ∙

research

∙ 12/23/2022

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints

As machine learning being used increasingly in making high-stakes decisi...

0 Yao Yao, et al. ∙

research

∙ 10/12/2022

Fairness via Adversarial Attribute Neighbourhood Robust Learning

Improving fairness between privileged and less-privileged sensitive attr...

0 Qi Qi, et al. ∙

research

∙ 10/11/2022

Stochastic Constrained DRO with a Complexity Independent of Sample Size

Distributionally Robust Optimization (DRO), as a popular method to train...

0 Qi Qi, et al. ∙

research

∙ 07/18/2022

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization

Variance reduction techniques such as SPIDER/SARAH/STORM have been exten...

0 Wei Jiang, et al. ∙

research

∙ 06/01/2022

Algorithmic Foundation of Deep X-Risk Optimization

X-risk is a term introduced to represent a family of compositional measu...

0 Tianbao Yang, et al. ∙

research

∙ 06/01/2022

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

In this paper, we study multi-block min-max bilevel optimization problem...

0 Quanqi Hu, et al. ∙

research

∙ 05/02/2022

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

In this paper, we investigate an online prediction strategy named as Dis...

0 Moshe Y. Vardi, 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

∙ 03/27/2022

Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices

The area under the ROC curve (AUROC) has been vigorously applied for imb...

0 Dixian Zhu, et al. ∙

research

∙ 03/03/2022

Large-scale Optimization of Partial AUC in a Range of False Positive Rates

The area under the ROC curve (AUC) is one of the most widely used perfor...

0 Yao Yao, et al. ∙

research

∙ 03/01/2022

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

In this paper, we propose systematic and efficient gradient-based method...

0 Dixian Zhu, et al. ∙

research

∙ 02/24/2022

Finite-Sum Compositional Stochastic Optimization: Theory and Applications

This paper studies stochastic optimization for a sum of compositional fu...

0 Bokun Wang, et al. ∙

research

∙ 02/24/2022

Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance

In this paper, we study contrastive learning from an optimization perspe...

0 Zhuoning Yuan, et al. ∙

research

∙ 02/24/2022

Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence

NDCG, namely Normalized Discounted Cumulative Gain, is a widely used ran...

0 Zi-Hao Qiu, et al. ∙

research

∙ 02/15/2022

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

In this paper, we investigate the problem of stochastic multi-level comp...

0 Wei Jiang, et al. ∙

research

∙ 12/30/2021

A Unified DRO View of Multi-class Loss Functions with top-N Consistency

Multi-class classification is one of the most common tasks in machine le...

0 Dixian Zhu, 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

∙ 11/01/2021

Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

In this extended abstract, we will present and discuss opportunities and...

0 Tianbao Yang, et al. ∙

research

∙ 07/02/2021

Momentum Accelerates the Convergence of Stochastic AUPRC Maximization

In this paper, we study stochastic optimization of areas under precision...

0 Guanghui Wang, 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

∙ 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

∙ 05/08/2021

A Simple yet Universal Strategy for Online Convex Optimization

Recently, several universal methods have been proposed for online convex...

0 Moshe Y. Vardi, et al. ∙

research

∙ 05/05/2021

Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization

In this paper, we consider non-convex stochastic bilevel optimization (S...

0 Zhishuai Guo, et al. ∙

research

∙ 04/18/2021

Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence

Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common m...

14 Qi Qi, et al. ∙

research

∙ 03/21/2021

Online Convex Optimization with Continuous Switching Constraint

In many sequential decision making applications, the change of decision ...

0 Guanghui Wang, et al. ∙

research

∙ 02/13/2021

Revisiting Smoothed Online Learning

In this paper, we revisit the problem of smoothed online learning, in wh...

0 Moshe Y. Vardi, 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

∙ 12/06/2020

Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

Deep AUC Maximization (DAM) is a paradigm for learning a deep neural net...

0 Zhuoning Yuan, et al. ∙

research

∙ 09/14/2020

Variance-Reduced Off-Policy Memory-Efficient Policy Search

Off-policy policy optimization is a challenging problem in reinforcement...

0 Daoming Lyu, et al. ∙

research

∙ 06/17/2020

Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise

In this paper, we propose robust stochastic algorithms for solving conve...

0 Yan Yan, et al. ∙

research

∙ 06/12/2020

Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems

This paper focuses on stochastic methods for solving smooth non-convex s...

0 Zhishuai Guo, et al. ∙

research

∙ 05/05/2020

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

In this paper, we study distributed algorithms for large-scale AUC maxim...

0 Zhishuai Guo, et al. ∙

research

∙ 03/09/2020

Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives

Stochastic gradient descent (SGD) has been widely studied in the literat...

0 Zhishuai Guo, et al. ∙

research

∙ 02/13/2020

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

Epoch gradient descent method (a.k.a. Epoch-GD) proposed by (Hazan and K...

0 Yan Yan, et al. ∙

research

∙ 02/06/2020

Minimizing Dynamic Regret and Adaptive Regret Simultaneously

Regret minimization is treated as the golden rule in the traditional stu...

0 Moshe Y. Vardi, et al. ∙

research

∙ 12/26/2019

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

Adaptive gradient algorithms perform gradient-based updates using the hi...

0 Mingrui Liu, et al. ∙

research

∙ 12/24/2019

a simple and effective framework for pairwise deep metric learning

Deep metric learning (DML) has received much attention in deep learning ...

29 Qi Qi, et al. ∙

research

∙ 10/28/2019

Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Generative Adversarial Networks (GANs) are powerful class of generative ...

0 Mingrui Liu, et al. ∙

research

∙ 09/25/2019

Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient

Current deep neural networks can achieve remarkable performance on a sin...

0 Yunhui Guo, 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. ∙

Tianbao Yang

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