Explanipedia

How to Explore the Potential of Green Bonds?—Based on Propensity Score Matching Method Open

H. J. Lu, Michael R. Lyu, Yiming Ying · 2025

Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis Open

Yiming Ying, Robert J. Ju, Jieyi Wang, Wenkai Li, Yuan Ji , et al. · 2024

ML has demonstrated optimal performance in detecting MSI in GC and could serve as a prospective early adjunctive detection tool for MSI in GC. Future research should contemplate minimally invasive or non-invasive, readily collectible, and …

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning Open

Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang · 2024

We study the discriminative probabilistic modeling on a continuous domain for the data prediction task of (multimodal) self-supervised representation learning. To address the challenge of computing the integral in the partition function fo…

Differentially private stochastic gradient descent with low-noise Open

Puyu Wang, Yunwen Lei, Yiming Ying, Ding‐Xuan Zhou · 2024

Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. This paper addresses the practical and theoretical importance …

Differentially Private Non-convex Learning for Multi-layer Neural Networks Open

Hanpu Shen, Chenglong Wang, Zihang Xiang, Yiming Ying, Di Wang · 2023

This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part, we examine cases with no hidden nodes, specifically focusin…

Outlier Robust Adversarial Training Open

Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu · 2023

Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with adversarial samples. While traditional robust learning met…

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms Open

Y. Ming, Xiyuan Wei, Tianbao Yang, Yiming Ying · 2023

Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associat…

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence Open

Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying · 2023

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AU…

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance Open

Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen · 2023

Multi-objective learning (MOL) problems often arise in emerging machine learning problems when there are multiple learning criteria, data modalities, or learning tasks. Different from single-objective learning, one of the critical challeng…

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks Open

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding‐Xuan Zhou · 2023

Recently, significant progress has been made in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) using the algorithmic stability approach. However, most of the existing research has focused on one-…

Fairness-aware Differentially Private Collaborative Filtering Open

Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao , et al. · 2023

Recently, there has been an increasing adoption of differential privacy\nguided algorithms for privacy-preserving machine learning tasks. However, the\nuse of such algorithms comes with trade-offs in terms of algorithmic fairness,\nwhich h…

Unmixing biological fluorescence image data with sparse and low-rank Poisson regression Open

Ruogu Wang, Alex A. Lemus, Colin M. Henneberry, Yiming Ying, Yunlong Feng , et al. · 2023

Motivation Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increa…

Generalization Analysis for Contrastive Representation Learning Open

Yunwen Lei, Tianbao Yang, Yiming Ying, Ding‐Xuan Zhou · 2023

Recently, contrastive learning has found impressive success in advancing the state of the art in solving various machine learning tasks. However, the existing generalization analysis is very limited or even not meaningful. In particular, t…

Unmixing Biological Fluorescence Image Data with Sparse and Low-Rank Poisson Regression Open

Ruogu Wang, Alex A. Lemus, Colin M. Henneberry, Yiming Ying, Yunlong Feng , et al. · 2023

Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2)…

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks Open

Yunwen Lei, Rong Jin, Yiming Ying · 2022

While significant theoretical progress has been achieved, unveiling the generalization mystery of overparameterized neural networks still remains largely elusive. In this paper, we study the generalization behavior of shallow neural networ…

Stability and Generalization for Markov Chain Stochastic Gradient Methods Open

Puyu Wang, Yunwen Lei, Yiming Ying, Ding‐Xuan Zhou · 2022

Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving minimization problems. In this paper, we provide a comprehensi…

Differentially Private Stochastic Gradient Descent with Low-Noise Open

Puyu Wang, Yunwen Lei, Yiming Ying, Ding‐Xuan Zhou · 2022

Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. This paper addresses the practical and theoretical importance …

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence Open

Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying · 2022

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AU…

AUC Maximization in the Era of Big Data and AI: A Survey Open

Tianbao Yang, Yiming Ying · 2022

Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC sc…

AUC Maximization in the Era of Big Data and AI: A Survey Open

Tianbao Yang, Yiming Ying · 2022

Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC sc…

Differentially Private SGDA for Minimax Problems Open

Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu , et al. · 2022

Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descent (SGD) with differential privacy (DP) constraints, there i…

Message from the IUCC 2021 General Chairs IUCC/CIT/DSCI/SmartCNS 2021 Open

Prosanta Gope, Rubem Lin, Zhengxin Yu, Is-Haka Mkwawa, Xu Jia , et al. · 2021

Welcome to the

Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise Learning. Open

Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying · 2021

Pairwise learning refers to learning tasks where the loss function depends on a pair of instances. It instantiates many important machine learning tasks such as bipartite ranking and metric learning. A popular approach to handle streaming …

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Open

Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying · 2021

Pairwise learning refers to learning tasks where the loss function depends on a pair of instances. It instantiates many important machine learning tasks such as bipartite ranking and metric learning. A popular approach to handle streaming …

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems Open

Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying · 2021

Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the …

Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning Open

Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang · 2021

In recent years, model-agnostic meta-learning (MAML) has become a popular research area. However, the stochastic optimization of MAML is still underdeveloped. Existing MAML algorithms rely on the ``episode'' idea by sampling a few tasks an…

Sum of Ranked Range Loss for Supervised Learning Open

Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu · 2021

In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each training sample, and in…

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems Open

Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying · 2021

Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the …

Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss Open

Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying · 2021

Pairwise learning has recently received increasing attention since it subsumes many important machine learning tasks (e.g. AUC maximization and metric learning) into a unifying framework. In this paper, we give the first-ever-known stabili…

Patterns of mega-forest fires in east Siberia will become less predictable with climate warming Open

Michael Natole, Yiming Ying, Alexander Buyantuev, Michael Stessin, V.A. Buyantuev , et al. · 2021

Very large fires covering tens to hundreds of hectares, termed mega-fires, have become a prominent feature of fire regime in taiga forests worldwide, and in Siberia in particular. Here, we applied an array of machine learning algorithms an…

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