Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing. - Benjamin D. Haeffele; Eric Young; René Vidal (ICML, 2014)

Global Optimality in Tensor Factorization, Deep Learning, and Beyond. - Benjamin D. Haeffele; René Vidal (CoRR, 2015)

Global Optimality in Neural Network Training. - Benjamin D. Haeffele; René Vidal (CVPR, 2017)

Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding. - Benjamin D. Haeffele; Sophie Roth; Lin Zhou; René Vidal (ISBI, 2017)

Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding. - Florence Yellin; Benjamin D. Haeffele; René Vidal (ISBI, 2017)

Efficient Reconstruction of Holographic Lens-Free Images by Sparse Phase Recovery. - Benjamin D. Haeffele; Richard Stahl; Geert Vanmeerbeeck; René Vidal (MICCAI (2), 2017)

An Analysis of Dropout for Matrix Factorization. - Jacopo Cavazza; Connor Lane; Benjamin D. Haeffele; Vittorio Murino; René Vidal (CoRR, 2017)

Dropout as a Low-Rank Regularizer for Matrix Factorization. - Jacopo Cavazza; Pietro Morerio; Benjamin D. Haeffele; Connor Lane; Vittorio Murino; René Vidal (AISTATS, 2018)

Multi-Cell Detection and Classification Using a Generative Convolutional Model. - Florence Yellin; Benjamin D. Haeffele; Sophie Roth; René Vidal (CVPR, 2018)

Separable Dictionary Learning with Global Optimality and Applications to Diffusion MRI. - Evan Schwab; Benjamin D. Haeffele; Nicolas Charon; René Vidal (CoRR, 2018)

Classifying and Comparing Approaches to Subspace Clustering with Missing Data. - Connor Lane; Ron Boger; Chong You; Manolis C. Tsakiris; Benjamin D. Haeffele; René Vidal (ICCV Workshops, 2019)

Adaptive Online k-Subspaces with Cooperative Re-Initialization. - Connor Lane; Benjamin D. Haeffele; René Vidal (ICCV Workshops, 2019)

An Optical Model of Whole Blood for Detecting Platelets in Lens-Free Images. - Benjamin D. Haeffele; Christian Pick; Ziduo Lin; Evelien Mathieu; Stuart C. Ray; René Vidal (SASHIMI@MICCAI, 2019)

Joint Holographic Detection and Reconstruction. - Florence Yellin; Benjamín Béjar Haro; Benjamin D. Haeffele; Evelien Mathieu; Christian Pick; Stuart C. Ray; René Vidal (MLMI@MICCAI, 2019)

Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI. - Evan Schwab; Benjamin D. Haeffele; René Vidal; Nicolas Charon (SIAM J. Imaging Sci., 2019)

On the Regularization Properties of Structured Dropout. - Ambar Pal; Connor Lane; René Vidal; Benjamin D. Haeffele (CVPR, 2020)

A novel variational form of the Schatten-$p$ quasi-norm. - Paris Giampouras; René Vidal; Athanasios A. Rontogiannis; Benjamin D. Haeffele (NeurIPS, 2020)

Doubly Stochastic Subspace Clustering. - Derek Lim; René Vidal; Benjamin D. Haeffele (CoRR, 2020)

Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications. - Benjamin D. Haeffele; René Vidal (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

A Critique of Self-Expressive Deep Subspace Clustering. - Benjamin David Haeffele; Chong You; René Vidal (ICLR, 2021)

Understanding the Dynamics of Gradient Flow in Overparameterized Linear models. - Salma Tarmoun; Guilherme França; Benjamin D. Haeffele; René Vidal (ICML, 2021)

Wave-Informed Matrix Factorization withGlobal Optimality Guarantees. - Harsha Vardhan Tetali; Joel B. Harley; Benjamin D. Haeffele (CoRR, 2021)

Efficient Maximal Coding Rate Reduction by Variational Forms. - Christina Baek; Ziyang Wu; Kwan Ho Ryan Chan; Tianjiao Ding; Yi Ma 001; Benjamin D. Haeffele (CVPR, 2022)

Unsupervised Wave Physics-Informed Representation Learning for Guided Wavefield Reconstruction. - Joel B. Harley; Benjamin D. Haeffele; Harsha Vardhan Tetali (DDDAS, 2022)

Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension. - Paris Giampouras; Benjamin David Haeffele; René Vidal (ICLR, 2022)

Understanding Doubly Stochastic Clustering. - Tianjiao Ding; Derek Lim; René Vidal; Benjamin D. Haeffele (ICML, 2022)

Unsupervised Manifold Linearizing and Clustering. - Tianjiao Ding; Shengbang Tong; Kwan Ho Ryan Chan; Xili Dai; Yi Ma 001; Benjamin D. Haeffele (ICCV, 2023)

Variational Information Pursuit for Interpretable Predictions. - Aditya Chattopadhyay; Kwan Ho Ryan Chan; Benjamin David Haeffele; Donald Geman; René Vidal (ICLR, 2023)

Learning Globally Smooth Functions on Manifolds. - Juan Cerviño; Luiz F. O. Chamon; Benjamin David Haeffele; René Vidal; Alejandro Ribeiro (ICML, 2023)

White-Box Transformers via Sparse Rate Reduction. - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Benjamin D. Haeffele; Yi Ma 001 (NeurIPS, 2023)

Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions. - Kwan Ho Ryan Chan; Aditya Chattopadhyay; Benjamin David Haeffele; René Vidal (CoRR, 2023)

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Hao Bai; Yuexiang Zhai; Benjamin D. Haeffele; Yi Ma 001 (CoRR, 2023)

Lens Free Holographic Imaging for Urinary Tract Infection Screening. - Gregory N. McKay; Anisha Oommen; Carolina Pacheco; Mason T. Chen; Stuart C. Ray; René Vidal; Benjamin D. Haeffele; Nicholas J. Durr (IEEE Trans. Biomed. Eng., 2023)

Interpretable by Design: Learning Predictors by Composing Interpretable Queries. - Aditya Chattopadhyay; Stewart Slocum; Benjamin D. Haeffele; René Vidal; Donald Geman (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models. - Tianzhe Chu; Shengbang Tong; Tianjiao Ding; Xili Dai; Benjamin David Haeffele; René Vidal; Yi Ma 001 (ICLR, 2024)

Performance Bounds for Active Binary Testing with Information Maximization. - Aditya Chattopadhyay; Benjamin David Haeffele; René Vidal; Donald Geman (ICML, 2024)

Vertex Proportion Loss for Multi-class Cell Detection from Label Proportions. - Carolina Pacheco; Florence Yellin; René Vidal; Benjamin D. Haeffele (MICCAI (12), 2024)

Wave Physics-Informed Matrix Factorizations. - Harsha Vardhan Tetali; Joel B. Harley; Benjamin D. Haeffele (IEEE Trans. Signal Process., 2024)

A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. - Uday Kiran Reddy Tadipatri; Benjamin David Haeffele; Joshua Agterberg; René Vidal (AISTATS, 2025)

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction. - Ziyang Wu; Tianjiao Ding; Yifu Lu; Druv Pai; Jingyuan Zhang; Weida Wang; Yaodong Yu; Yi Ma 001; Benjamin David Haeffele (ICLR, 2025)

Nonconvex Linear System Identification with Minimal State Representation. - Uday Kiran Reddy Tadipatri; Benjamin D. Haeffele; Joshua Agterberg; Ingvar M. Ziemann; René Vidal (L4DC, 2025)

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models. - Laura Balzano; Tianjiao Ding; Benjamin D. Haeffele; Soo Min Kwon; Qing Qu 001; Peng Wang 098; Zhangyang Wang; Can Yaras (CoRR, 2025)