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08/29/2023
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
As powerful tools for representation learning on graphs, graph neural ne...
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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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06/13/2023
Learning Unnormalized Statistical Models via Compositional Optimization
Learning unnormalized statistical models (e.g., energy-based models) is ...
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06/05/2023
LibAUC: A Deep Learning Library for X-Risk Optimization
This paper introduces the award-winning deep learning (DL) library calle...
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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 (...
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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...
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05/14/2023
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
This paper considers a novel application of deep AUC maximization (DAM) ...
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04/20/2023
Federated Compositional Deep AUC Maximization
Federated learning has attracted increasing attention due to the promise...
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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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02/18/2023
Stochastic Approximation Approaches to Group Distributionally Robust Optimization
This paper investigates group distributionally robust optimization (GDRO...
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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...
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10/12/2022
Fairness via Adversarial Attribute Neighbourhood Robust Learning
Improving fairness between privileged and less-privileged sensitive attr...
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10/11/2022
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Distributionally Robust Optimization (DRO), as a popular method to train...
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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...
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06/01/2022
Algorithmic Foundation of Deep X-Risk Optimization
X-risk is a term introduced to represent a family of compositional measu...
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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...
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05/02/2022
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
In this paper, we investigate an online prediction strategy named as Dis...
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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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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...
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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...
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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...
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02/24/2022
Finite-Sum Compositional Stochastic Optimization: Theory and Applications
This paper studies stochastic optimization for a sum of compositional fu...
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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...
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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...
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02/15/2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
In this paper, we investigate the problem of stochastic multi-level comp...
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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...
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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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11/01/2021
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities
In this extended abstract, we will present and discuss opportunities and...
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07/02/2021
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
In this paper, we study stochastic optimization of areas under precision...
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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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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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05/08/2021
A Simple yet Universal Strategy for Online Convex Optimization
Recently, several universal methods have been proposed for online convex...
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05/05/2021
Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization
In this paper, we consider non-convex stochastic bilevel optimization (S...
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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...
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03/21/2021
Online Convex Optimization with Continuous Switching Constraint
In many sequential decision making applications, the change of decision ...
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02/13/2021
Revisiting Smoothed Online Learning
In this paper, we revisit the problem of smoothed online learning, in wh...
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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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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...
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09/14/2020
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Off-policy policy optimization is a challenging problem in reinforcement...
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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...
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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...
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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...
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03/09/2020
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives
Stochastic gradient descent (SGD) has been widely studied in the literat...
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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...
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02/06/2020
Minimizing Dynamic Regret and Adaptive Regret Simultaneously
Regret minimization is treated as the golden rule in the traditional stu...
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12/26/2019
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Adaptive gradient algorithms perform gradient-based updates using the hi...
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12/24/2019
a simple and effective framework for pairwise deep metric learning
Deep metric learning (DML) has received much attention in deep learning ...
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10/28/2019
Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Generative Adversarial Networks (GANs) are powerful class of generative ...
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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...
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08/28/2019