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Dataset distillation has emerged as a promising approach in deep learning, enabling efficient training with small synthetic datasets derived from larger real ones. Particularly, distribution matching-based distillation methods attract…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Wenxiao Deng , Wenbin Li , Tianyu Ding , Lei Wang , Hongguang Zhang , Kuihua Huang , Jing Huo , Yang Gao

Models incorporating uncertain inputs, such as random forces or material parameters, have been of increasing interest in PDE-constrained optimization. In this paper, we focus on the efficient numerical minimization of a convex and smooth…

Optimization and Control · Mathematics 2021-06-18 Caroline Geiersbach , Winnifried Wollner

Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions. This paper develops a new model-free biclustering…

Methodology · Statistics 2022-08-09 Marcos Matabuena , J. C Vidal , Oscar Hernan Madrid Padilla , Dino Sejdinovic

Classical Mixtures of Experts (MoE) are Machine Learning models that involve partitioning the input space, with a separate "expert" model trained on each partition. Recently, MoE-based model architectures have become popular as a means to…

Machine Learning · Computer Science 2025-10-14 Quentin Fruytier , Aryan Mokhtari , Sujay Sanghavi

Markov chain Monte Carlo (MCMC) methods require a large number of samples to approximate a posterior distribution, which can be costly when the likelihood or prior is expensive to evaluate. The number of samples can be reduced if we can…

Computation · Statistics 2019-08-06 V. Roshan Joseph , Dianpeng Wang , Li Gu , Shiji Lv , Rui Tuo

We consider a discrete optimization formulation for learning sparse classifiers, where the outcome depends upon a linear combination of a small subset of features. Recent work has shown that mixed integer programming (MIP) can be used to…

Machine Learning · Statistics 2021-06-08 Antoine Dedieu , Hussein Hazimeh , Rahul Mazumder

Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predicted and target meshes remains an open problem. A prevalent…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Tung Le , Khai Nguyen , Shanlin Sun , Kun Han , Nhat Ho , Xiaohui Xie

The framework of Partial Information Decomposition (PID) unveils complex nonlinear interactions in network systems by dissecting the mutual information (MI) between a target variable and several source variables. While PID measures have…

Data Analysis, Statistics and Probability · Physics 2024-09-23 Chiara Barà , Yuri Antonacci , Marta Iovino , Ivan Lazic , Luca Faes

In the study of McKean-Vlasov stochastic differential equations (MV-SDEs), numerical approximation plays a crucial role in understanding the behavior of interacting particle systems (IPS). Classical Milstein schemes provide strong…

Numerical Analysis · Mathematics 2025-10-21 Jingtao Zhu , Yuying Zhao , Siqing Gan

Energy-based modeling is a promising approach to unsupervised learning, which yields many downstream applications from a single model. The main difficulty in learning energy-based models with the "contrastive approaches" is the generation…

Machine Learning · Computer Science 2021-11-30 Kirill Neklyudov , Priyank Jaini , Max Welling

The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that…

Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks…

Cryptography and Security · Computer Science 2025-05-20 Zi Liang , Qingqing Ye , Yanyun Wang , Sen Zhang , Yaxin Xiao , Ronghua Li , Jianliang Xu , Haibo Hu

This paper introduces a novel distributed optimization technique for networked systems, which removes the dependency on specific parameter choices, notably the learning rate. Traditional parameter selection strategies in distributed…

Optimization and Control · Mathematics 2024-04-23 Rodrigo Aldana-López , Alessandro Macchelli , Giuseppe Notarstefano , Rosario Aragüés , Carlos Sagüés

This letter investigates the problem of energy efficient collaborative strategy for mobile embodied artificial intelligence network (MEAN) over wireless communication. In the considered model, the agents execute the tasks through…

Signal Processing · Electrical Eng. & Systems 2026-05-15 Chenliang Wu , Zhouxiang Zhao , Jiaxiang Wang , Ruopeng Xu , Chen Zhu , Zhaohui Yang , Zhaoyang Zhang

Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with non-smooth regularization terms. Most existing gradient-based…

Optimization and Control · Mathematics 2019-10-29 Sulaiman A. Alghunaim , Kun Yuan , Ali H. Sayed

Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters. However, when Bayes' rule does not result in tractable closed-form, most approximate inference algorithms lack…

Machine Learning · Computer Science 2016-05-09 Bo Dai , Niao He , Hanjun Dai , Le Song

The linear ensemble based strategy, i.e., averaging ensemble, has been proposed to improve the performance in unsupervised domain adaptation tasks. However, a typical UDA task is usually challenged by dynamically changing factors, such as…

Computer Vision and Pattern Recognition · Computer Science 2022-11-16 Weimin Wu , Jiayuan Fan , Tao Chen , Hancheng Ye , Bo Zhang , Baopu Li

Due to the notorious modality imbalance problem, multimodal learning (MML) leads to the phenomenon of optimization imbalance, thus struggling to achieve satisfactory performance. Recently, some representative methods have been proposed to…

Machine Learning · Computer Science 2024-07-08 Qing-Yuan Jiang , Zhouyang Chi , Yang Yang

Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific research, e.g. molecular dynamics. In this work, we intend to…

Machine Learning · Computer Science 2026-03-10 RuiKang OuYang , Bo Qiang , José Miguel Hernández-Lobato

Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires…

Systems and Control · Electrical Eng. & Systems 2024-09-23 Mohammed Rashid , Jeffrey A. Nanzer