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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 234: Conference on Parsimony and Learning, 3-6 January 2024, Hongkong, China

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Editors: Yuejie Chi, Gintare Karolina Dziugaite, Qing Qu, Atlas Wang Wang, Zhihui Zhu

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PC-X: Profound Clustering via Slow Exemplars

; Conference on Parsimony and Learning, PMLR 234:1-19

[abs][Download PDF][OpenReview]

WS-iFSD: Weakly Supervised Incremental Few-shot Object Detection Without Forgetting

Xinyu Gong, Li Yin, Juan-Manuel Perez-Rua, Zhangyang Wang, Zhicheng Yan; Conference on Parsimony and Learning, PMLR 234:20-38

[abs][Download PDF][OpenReview]

Sparse Fréchet sufficient dimension reduction via nonconvex optimization

Jiaying Weng, Chenlu Ke, Pei Wang; Conference on Parsimony and Learning, PMLR 234:39-53

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Efficiently Disentangle Causal Representations

Yuanpeng Li, Joel Hestness, Mohamed Elhoseiny, Liang Zhao, Kenneth Church; Conference on Parsimony and Learning, PMLR 234:54-71

[abs][Download PDF][OpenReview]

Emergence of Segmentation with Minimalistic White-Box Transformers

Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma; Conference on Parsimony and Learning, PMLR 234:72-93

[abs][Download PDF][OpenReview]

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates

Murat Onur Yildirim, Elif Ceren Gok, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren; Conference on Parsimony and Learning, PMLR 234:94-107

[abs][Download PDF][OpenReview]

Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

Qiucheng Wu, Yifan Jiang, Junru Wu, Kai Wang, Eric Zhang, Humphrey Shi, Zhangyang Wang, Shiyu Chang; Conference on Parsimony and Learning, PMLR 234:108-133

[abs][Download PDF][OpenReview]

HARD: Hyperplane ARrangement Descent

Tianjiao Ding, Liangzu Peng, Rene Vidal; Conference on Parsimony and Learning, PMLR 234:134-158

[abs][Download PDF][OpenReview]

FIXED: Frustratingly Easy Domain Generalization with Mixup

Wang Lu, Jindong Wang, Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, Xing Xie; Conference on Parsimony and Learning, PMLR 234:159-178

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Domain Generalization via Nuclear Norm Regularization

Zhenmei Shi, Yifei Ming, Ying Fan, Frederic Sala, Yingyu Liang; Conference on Parsimony and Learning, PMLR 234:179-201

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Investigating the Catastrophic Forgetting in Multimodal Large Language Model Fine-Tuning

Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, Yi Ma; Conference on Parsimony and Learning, PMLR 234:202-227

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Deep Self-expressive Learning

Chen Zhao, Chun-Guang Li, Wei He, Chong You; Conference on Parsimony and Learning, PMLR 234:228-247

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Sparse Activations with Correlated Weights in Cortex-Inspired Neural Networks

Chanwoo Chun, Daniel Lee; Conference on Parsimony and Learning, PMLR 234:248-268

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Piecewise-Linear Manifolds for Deep Metric Learning

Shubhang Bhatnagar, Narendra Ahuja; Conference on Parsimony and Learning, PMLR 234:269-281

[abs][Download PDF][OpenReview]

HRBP: Hardware-friendly Regrouping towards Block-based Pruning for Sparse CNN Training

Haoyu Ma, Chengming Zhang, lizhi xiang, Xiaolong Ma, Geng Yuan, Wenkai Zhang, Shiwei Liu, Tianlong Chen, Dingwen Tao, Yanzhi Wang, Zhangyang Wang, Xiaohui Xie; Conference on Parsimony and Learning, PMLR 234:282-301

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Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Materials Science Benchmark and A Sparsity-Oriented Optimization Framework

Xuxi Chen, Tianlong Chen, Everardo Yeriel Olivares, Kate Elder, Scott McCall, Aurelien Perron, Joseph McKeown, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher; Conference on Parsimony and Learning, PMLR 234:302-323

[abs][Download PDF][OpenReview]

Deep Leakage from Model in Federated Learning

Zihao Zhao, Mengen Luo, Wenbo Ding; Conference on Parsimony and Learning, PMLR 234:324-340

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Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction

Bowen Lei, Dongkuan Xu, Ruqi Zhang, Shuren He, Bani Mallick; Conference on Parsimony and Learning, PMLR 234:341-378

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An Adaptive Tangent Feature Perspective of Neural Networks

Daniel LeJeune, Sina Alemohammad; Conference on Parsimony and Learning, PMLR 234:379-394

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Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds

Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung; Conference on Parsimony and Learning, PMLR 234:395-418

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Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation

Zhiyu Xue, Yinlong Dai, Qi Lei; Conference on Parsimony and Learning, PMLR 234:419-439

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Unsupervised Learning of Structured Representation via Closed-Loop Transcription

Shengbang Tong, Xili Dai, Yubei Chen, Mingyang Li, ZENGYI LI, Brent Yi, Yann LeCun, Yi Ma; Conference on Parsimony and Learning, PMLR 234:440-457

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Algorithm Design for Online Meta-Learning with Task Boundary Detection

Daouda Sow, Sen Lin, Yingbin Liang, Junshan Zhang; Conference on Parsimony and Learning, PMLR 234:458-479

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NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release

Donghao Li, Yang Cao, Yuan Yao; Conference on Parsimony and Learning, PMLR 234:480-514

[abs][Download PDF][OpenReview]

Jaxpruner: A Concise Library for Sparsity Research

Joo Hyung Lee, Wonpyo Park, Nicole Elyse Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J.C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci; Conference on Parsimony and Learning, PMLR 234:515-528

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Image Quality Assessment: Integrating Model-centric and Data-centric Approaches

Peibei Cao, Dingquan Li, Kede Ma; Conference on Parsimony and Learning, PMLR 234:529-541

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How to Prune Your Language Model: Recovering Accuracy on the “Sparsity May Cry” Benchmark

Eldar Kurtic, Torsten Hoefler, Dan Alistarh; Conference on Parsimony and Learning, PMLR 234:542-553

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Leveraging Sparse Input and Sparse Models: Efficient Distributed Learning in Resource-Constrained Environments

Emmanouil Kariotakis, Grigorios Tsagkatakis, Panagiotis Tsakalides, Anastasios Kyrillidis; Conference on Parsimony and Learning, PMLR 234:554-569

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Closed-Loop Transcription via Convolutional Sparse Coding

Xili Dai, Ke Chen, Shengbang Tong, Jingyuan Zhang, Xingjian Gao, Mingyang Li, Druv Pai, Yuexiang Zhai, Xiaojun Yuan, Heung-Yeung Shum, Lionel Ni, Yi Ma; Conference on Parsimony and Learning, PMLR 234:570-589

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Less is More – Towards parsimonious multi-task models using structured sparsity

Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki; Conference on Parsimony and Learning, PMLR 234:590-601

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