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ORCIDBenjamin D. Haeffele
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2020 – today
- 2024
[c26]Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, René Vidal, Yi Ma:
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models. ICLR 2024- [c25]Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal, Donald Geman:
Performance Bounds for Active Binary Testing with Information Maximization. ICML 2024 - [c24]Carolina Pacheco, Florence Yellin, René Vidal, Benjamin D. Haeffele:
Vertex Proportion Loss for Multi-class Cell Detection from Label Proportions. MICCAI (12) 2024: 366-376 - [i23]Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, René Vidal:
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. CoRR abs/2411.02767 (2024) - [i22]Ziyang Wu, Tianjiao Ding, Yifu Lu, Druv Pai, Jingyuan Zhang, Weida Wang, Yaodong Yu, Yi Ma, Benjamin D. Haeffele:
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction. CoRR abs/2412.17810 (2024) - 2023
- [j4]Aditya Chattopadhyay
, Stewart Slocum, Benjamin D. Haeffele, René Vidal, Donald Geman:
Interpretable by Design: Learning Predictors by Composing Interpretable Queries. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7430-7443 (2023) - [j3]Gregory N. McKay, Anisha Oommen, Carolina Pacheco, Mason T. Chen, Stuart C. Ray, René Vidal, Benjamin D. Haeffele, Nicholas J. Durr:
Lens Free Holographic Imaging for Urinary Tract Infection Screening. IEEE Trans. Biomed. Eng. 70(3): 1053-1061 (2023) - [c23]Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, Benjamin D. Haeffele:
Unsupervised Manifold Linearizing and Clustering. ICCV 2023: 5427-5438 - [c22]Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal:
Variational Information Pursuit for Interpretable Predictions. ICLR 2023 - [c21]Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro:
Learning Globally Smooth Functions on Manifolds. ICML 2023: 3815-3854 - [c20]Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma:
White-Box Transformers via Sparse Rate Reduction. NeurIPS 2023 - [i21]Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, Benjamin D. Haeffele:
Unsupervised Manifold Linearizing and Clustering. CoRR abs/2301.01805 (2023) - [i20]Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele, Donald Geman, René Vidal:
Variational Information Pursuit for Interpretable Predictions. CoRR abs/2302.02876 (2023) - [i19]Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma:
White-Box Transformers via Sparse Rate Reduction. CoRR abs/2306.01129 (2023) - [i18]Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, René Vidal, Yi Ma:
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models. CoRR abs/2306.05272 (2023) - [i17]Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal:
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions. CoRR abs/2308.12562 (2023) - [i16]Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma:
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? CoRR abs/2311.13110 (2023) - [i15]Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele:
Wave Physics-informed Matrix Factorizations. CoRR abs/2312.13584 (2023) - 2022
- [c19]Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele:
Efficient Maximal Coding Rate Reduction by Variational Forms. CVPR 2022: 490-498 - [c18]Joel B. Harley, Benjamin D. Haeffele, Harsha Vardhan Tetali:
Unsupervised Wave Physics-Informed Representation Learning for Guided Wavefield Reconstruction. DDDAS 2022: 163-172 - [c17]Paris Giampouras, Benjamin David Haeffele, René Vidal:
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension. ICLR 2022 - [c16]Tianjiao Ding, Derek Lim, René Vidal, Benjamin D. Haeffele:
Understanding Doubly Stochastic Clustering. ICML 2022: 5153-5165 - [i14]Paris V. Giampouras, Benjamin D. Haeffele, René Vidal:
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension. CoRR abs/2201.09079 (2022) - [i13]Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele:
Efficient Maximal Coding Rate Reduction by Variational Forms. CoRR abs/2204.00077 (2022) - [i12]Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele, René Vidal, Donald Geman:
Interpretable by Design: Learning Predictors by Composing Interpretable Queries. CoRR abs/2207.00938 (2022) - [i11]Juan Cerviño, Luiz F. O. Chamon, Benjamin D. Haeffele, René Vidal, Alejandro Ribeiro:
Learning Globally Smooth Functions on Manifolds. CoRR abs/2210.00301 (2022) - 2021
- [c15]Benjamin David Haeffele, Chong You, René Vidal:
A Critique of Self-Expressive Deep Subspace Clustering. ICLR 2021 - [c14]Salma Tarmoun, Guilherme França, Benjamin D. Haeffele, René Vidal:
Understanding the Dynamics of Gradient Flow in Overparameterized Linear models. ICML 2021: 10153-10161 - [i10]Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele:
Wave-Informed Matrix Factorization withGlobal Optimality Guarantees. CoRR abs/2107.09144 (2021) - 2020
- [j2]Benjamin D. Haeffele, René Vidal:
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications. IEEE Trans. Pattern Anal. Mach. Intell. 42(6): 1468-1482 (2020) - [c13]Ambar Pal, Connor Lane, René Vidal, Benjamin D. Haeffele:
On the Regularization Properties of Structured Dropout. CVPR 2020: 7668-7676 - [c12]Paris Giampouras, René Vidal, Athanasios A. Rontogiannis, Benjamin D. Haeffele:
A novel variational form of the Schatten-$p$ quasi-norm. NeurIPS 2020 - [i9]Benjamin D. Haeffele, Chong You, René Vidal:
A Critique of Self-Expressive Deep Subspace Clustering. CoRR abs/2010.03697 (2020) - [i8]Paris Giampouras, René Vidal, Athanasios A. Rontogiannis, Benjamin D. Haeffele:
A novel variational form of the Schatten-p quasi-norm. CoRR abs/2010.13927 (2020) - [i7]Derek Lim, René Vidal, Benjamin D. Haeffele:
Doubly Stochastic Subspace Clustering. CoRR abs/2011.14859 (2020)
2010 – 2019
- 2019
- [j1]Evan Schwab, Benjamin D. Haeffele, René Vidal, Nicolas Charon:
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI. SIAM J. Imaging Sci. 12(4): 1967-2008 (2019) - [c11]Connor Lane, Ron Boger, Chong You, Manolis C. Tsakiris, Benjamin D. Haeffele, René Vidal:
Classifying and Comparing Approaches to Subspace Clustering with Missing Data. ICCV Workshops 2019: 669-677 - [c10]Connor Lane, Benjamin D. Haeffele, René Vidal:
Adaptive Online k-Subspaces with Cooperative Re-Initialization. ICCV Workshops 2019: 678-688 - [c9]Benjamin D. Haeffele, Christian Pick, Ziduo Lin, Evelien Mathieu, Stuart C. Ray, René Vidal:
An Optical Model of Whole Blood for Detecting Platelets in Lens-Free Images. SASHIMI@MICCAI 2019: 140-150 - [c8]Florence Yellin, Benjamín Béjar Haro, Benjamin D. Haeffele, Evelien Mathieu, Christian Pick, Stuart C. Ray, René Vidal:
Joint Holographic Detection and Reconstruction. MLMI@MICCAI 2019: 664-672 - [i6]Ambar Pal, Connor Lane, René Vidal, Benjamin D. Haeffele:
On the Regularization Properties of Structured Dropout. CoRR abs/1910.14186 (2019) - 2018
- [c7]Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal:
Dropout as a Low-Rank Regularizer for Matrix Factorization. AISTATS 2018: 435-444 - [c6]Florence Yellin, Benjamin D. Haeffele, Sophie Roth, René Vidal:
Multi-Cell Detection and Classification Using a Generative Convolutional Model. CVPR 2018: 8953-8961 - [i5]Evan Schwab, Benjamin D. Haeffele, Nicolas Charon, René Vidal:
Separable Dictionary Learning with Global Optimality and Applications to Diffusion MRI. CoRR abs/1807.05595 (2018) - 2017
- [c5]Benjamin D. Haeffele, René Vidal:
Global Optimality in Neural Network Training. CVPR 2017: 4390-4398 - [c4]Florence Yellin, Benjamin D. Haeffele, René Vidal:
Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding. ISBI 2017: 650-653 - [c3]Benjamin D. Haeffele, Sophie Roth, Lin Zhou, René Vidal:
Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding. ISBI 2017: 741-744 - [c2]Benjamin D. Haeffele, Richard Stahl, Geert Vanmeerbeeck, René Vidal:
Efficient Reconstruction of Holographic Lens-Free Images by Sparse Phase Recovery. MICCAI (2) 2017: 109-117 - [i4]Benjamin D. Haeffele, René Vidal:
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications. CoRR abs/1708.07850 (2017) - [i3]Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele, Vittorio Murino, René Vidal:
An Analysis of Dropout for Matrix Factorization. CoRR abs/1710.03487 (2017) - [i2]Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal:
Dropout as a Low-Rank Regularizer for Matrix Factorization. CoRR abs/1710.05092 (2017) - 2015
- [i1]Benjamin D. Haeffele, René Vidal:
Global Optimality in Tensor Factorization, Deep Learning, and Beyond. CoRR abs/1506.07540 (2015) - 2014
- [c1]Benjamin D. Haeffele, Eric Young, René Vidal:
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing. ICML 2014: 2007-2015
Coauthor Index
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