Expected Patch Log Likelihood with a Sparse Prior. - Jeremias Sulam; Michael Elad (EMMCVPR, 2014)

Image denoising through multi-scale learnt dictionaries. - Jeremias Sulam; Boaz Ophir; Michael Elad (ICIP, 2014)

Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation. - Javier S. Turek; Jeremias Sulam; Michael Elad; Irad Yavneh (ICASSP, 2015)

Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding. - Vardan Papyan; Jeremias Sulam; Michael Elad (CoRR, 2016)

Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding. - Vardan Papyan; Jeremias Sulam; Michael Elad (CoRR, 2016)

Large Inpainting of Face Images With Trainlets. - Jeremias Sulam; Michael Elad (IEEE Signal Process. Lett., 2016)

Trainlets: Dictionary Learning in High Dimensions. - Jeremias Sulam; Boaz Ophir; Michael Zibulevsky; Michael Elad (IEEE Trans. Signal Process., 2016)

Convolutional Dictionary Learning via Local Processing. - Vardan Papyan; Yaniv Romano; Michael Elad; Jeremias Sulam (ICCV, 2017)

Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets. - Jeremias Sulam; Rami Ben-Ari; Pavel Kisilev (VCBM, 2017)

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (CoRR, 2017)

Dynamical system classification with diffusion embedding for ECG-based person identification. - Jeremias Sulam; Yaniv Romano; Ronen Talmon (Signal Process., 2017)

Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. - Vardan Papyan; Jeremias Sulam; Michael Elad (IEEE Trans. Signal Process., 2017)

Projecting on to the Multi-Layer Convolutional Sparse Coding Model. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (ICASSP, 2018)

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. - Jeremias Sulam; Aviad Aberdam; Michael Elad (CoRR, 2018)

Improving Pursuit Algorithms Using Stochastic Resonance. - Dror Simon; Jeremias Sulam; Yaniv Romano; Yue M. Lu; Michael Elad (CoRR, 2018)

A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model. - Ev Zisselman; Jeremias Sulam; Michael Elad (CoRR, 2018)

Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks. - Vardan Papyan; Yaniv Romano; Jeremias Sulam; Michael Elad (IEEE Signal Process. Mag., 2018)

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (IEEE Trans. Signal Process., 2018)

From Local to Global Sparse Modeling. - Jeremias Sulam (2018)

A Local Block Coordinate Descent Algorithm for the CSC Model. - Ev Zisselman; Jeremias Sulam; Michael Elad (CVPR, 2019)

Multi-Layer Sparse Coding: The Holistic Way. - Aviad Aberdam; Jeremias Sulam; Michael Elad (SIAM J. Math. Data Sci., 2019)

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance. - Dror Simon; Jeremias Sulam; Yaniv Romano; Yue M. Lu; Michael Elad (IEEE Trans. Signal Process., 2019)

Learned Proximal Networks for Quantitative Susceptibility Mapping. - Kuo-Wei Lai; Manisha Aggarwal; Peter C. M. van Zijl; Xu Li 003; Jeremias Sulam (MICCAI (2), 2020)

Learning to solve TV regularised problems with unrolled algorithms. - Hamza Cherkaoui; Jeremias Sulam; Thomas Moreau 001 (NeurIPS, 2020)

Conformal Symplectic and Relativistic Optimization. - Guilherme França; Jeremias Sulam; Daniel P. Robinson; René Vidal (NeurIPS, 2020)

Adversarial Robustness of Supervised Sparse Coding. - Jeremias Sulam; Ramchandran Muthukumar; Raman Arora (NeurIPS, 2020)

Geometric potentials from deep learning improve prediction of CDR H3 loop structures. - Jeffrey A. Ruffolo; Carlos Guerra; Sai Pooja Mahajan; Jeremias Sulam; Jeffrey J. Gray (Bioinform., 2020)

Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals. - Yaniv Romano; Aviad Aberdam; Jeremias Sulam; Michael Elad (J. Math. Imaging Vis., 2020)

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. - Jeremias Sulam; Aviad Aberdam; Amir Beck; Michael Elad (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Deep Learning in Protein Structural Modeling and Design. - Wenhao Gao 001; Sai Pooja Mahajan; Jeremias Sulam; Jeffrey J. Gray (Patterns, 2020)

Variations on the Convolutional Sparse Coding Model. - Ives Rey-Otero; Jeremias Sulam; Michael Elad (IEEE Trans. Signal Process., 2020)

A Geometric Analysis of Neural Collapse with Unconstrained Features. - Zhihui Zhu; Tianyu Ding; Jinxin Zhou; Xiao Li 026; Chong You; Jeremias Sulam; Qing Qu 001 (NeurIPS, 2021)

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. - Zhenzhen Wang; Aleksander S. Popel; Jeremias Sulam (CoRR, 2021)

Deciphering antibody affinity maturation with language models and weakly supervised learning. - Jeffrey A. Ruffolo; Jeffrey J. Gray; Jeremias Sulam (CoRR, 2021)

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. - Joshua Agterberg; Jeremias Sulam (AISTATS, 2022)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. - Jacopo Teneggi; Beepul Bharti; Yaniv Romano; Jeremias Sulam (CoRR, 2022)

Estimating and Controlling for Fairness via Sensitive Attribute Predictors. - Beepul Bharti; Paul H. Yi; Jeremias Sulam (CoRR, 2022)

Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT. - Jacopo Teneggi; Paul H. Yi; Jeremias Sulam (CoRR, 2022)

Recovery and Generalization in Over-Realized Dictionary Learning. - Jeremias Sulam; Chong You; Zhihui Zhu (J. Mach. Learn. Res., 2022)

Antibody structure prediction using interpretable deep learning. - Jeffrey A. Ruffolo; Jeremias Sulam; Jeffrey J. Gray (Patterns, 2022)

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. - Zhenzhen Wang; Carla Saoud; Sintawat Wangsiricharoen; Aaron W. James; Aleksander S. Popel; Jeremias Sulam (IEEE Trans. Medical Imaging, 2022)

Sparsity-aware generalization theory for deep neural networks. - Ramchandran Muthukumar; Jeremias Sulam (COLT, 2023)

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. - Jacopo Teneggi; Matthew Tivnan; J. Webster Stayman; Jeremias Sulam (ICML, 2023)

WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging. - Zhenghan Fang; Hyeong-Geol Shin; Peter C. M. van Zijl; Xu Li 003; Jeremias Sulam (MLCN@MICCAI, 2023)

Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors. - Beepul Bharti; Paul H. Yi; Jeremias Sulam (NeurIPS, 2023)

Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. - Ambar Pal; Jeremias Sulam; René Vidal (NeurIPS, 2023)

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging. - Zhenghan Fang; Kuo-Wei Lai; Peter C. M. van Zijl; Xu Li 003; Jeremias Sulam (Medical Image Anal., 2023)

Understanding Noise-Augmented Training for Randomized Smoothing. - Ambar Pal; Jeremias Sulam (Trans. Mach. Learn. Res., 2023)

SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. - Jacopo Teneggi; Beepul Bharti; Yaniv Romano; Jeremias Sulam (Trans. Mach. Learn. Res., 2023)

Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T. - Peter van Gelderen; Xu Li 003; Jacco A. de Zwart; Erin S. Beck; Serhat V. Okar; Yujia Huang; KuoWei Lai; Jeremias Sulam; Peter C. M. van Zijl; Daniel S. Reich; Jeff H. Duyn; Jiaen Liu (NeuroImage, 2023)

Fast Hierarchical Games for Image Explanations. - Jacopo Teneggi; Alexandre Luster; Jeremias Sulam (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Adversarial Robustness of Sparse Local Lipschitz Predictors. - Ramchandran Muthukumar; Jeremias Sulam (SIAM J. Math. Data Sci., 2023)

What's in a Prior? Learned Proximal Networks for Inverse Problems. - Zhenghan Fang; Sam Buchanan; Jeremias Sulam (ICLR, 2024)

ProxiMO: Proximal Multi-operator Networks for Quantitative Susceptibility Mapping. - Shmuel Orenstein; Zhenghan Fang; Hyeong-Geol Shin; Peter C. M. van Zijl; Xu Li 003; Jeremias Sulam (MLCN@MICCAI, 2024)

Testing Semantic Importance via Betting. - Jacopo Teneggi; Jeremias Sulam (NeurIPS, 2024)

I Bet You Did Not Mean That: Testing Semantic Importance via Betting. - Jacopo Teneggi; Jeremias Sulam (CoRR, 2024)

Certified Robustness against Sparse Adversarial Perturbations via Data Localization. - Ambar Pal; René Vidal; Jeremias Sulam (Trans. Mach. Learn. Res., 2024)

Pivotal Auto-Encoder via Self-Normalizing ReLU. - Nelson Goldenstein; Jeremias Sulam; Yaniv Romano (IEEE Trans. Signal Process., 2024)

Sufficient and Necessary Explanations (and What Lies in Between). - Beepul Bharti; Paul H. Yi; Jeremias Sulam (CPAL, 2025)

Concept Bottleneck Model with Zero Performance Loss. - Zhenzhen Wang; Aleksander S. Popel; Jeremias Sulam (CPAL, 2025)

Disentangling Safe and Unsafe Image Corruptions via Anisotropy and Locality. - Ramchandran Muthukumar; Ambar Pal; Jeremias Sulam; René Vidal (CVPR, 2025)

Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology. - Zhenzhen Wang; Cesar A. Santa-Maria; Aleksander S. Popel; Jeremias Sulam (Patterns, 2025)

Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality. - Ramchandran Muthukumar; Ambar Pal; Jeremias Sulam; René Vidal (CoRR, 2025)

Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography. - Jacopo Teneggi; J. Webster Stayman; Jeremias Sulam (CoRR, 2025)

Multiaccuracy and Multicalibration via Proxy Groups. - Beepul Bharti; Mary Versa Clemens-Sewall; Paul H. Yi; Jeremias Sulam (CoRR, 2025)

Aligning Explanations with Human Communication. - Jacopo Teneggi; Zhenzhen Wang; Paul H. Yi; Tianmin Shu; Jeremias Sulam (CoRR, 2025)

Beyond Scores: Proximal Diffusion Models. - Zhenghan Fang; Mateo Díaz; Sam Buchanan; Jeremias Sulam (CoRR, 2025)