Compressive identification of linear operators. - Reinhard Heckel; Helmut Bölcskei (ISIT, 2011)

Detecting controlling nodes of boolean regulatory networks. - Steffen Schober; David Kracht; Reinhard Heckel; Martin Bossert (EURASIP J. Bioinform. Syst. Biol., 2011)

Joint sparsity with different measurement matrices. - Reinhard Heckel; Helmut Bölcskei (Allerton Conference, 2012)

Subspace clustering via thresholding and spectral clustering. - Reinhard Heckel; Helmut Bölcskei (ICASSP, 2013)

Noisy subspace clustering via thresholding. - Reinhard Heckel; Helmut Bölcskei (ISIT, 2013)

Harmonic analysis of Boolean networks: determinative power and perturbations. - Reinhard Heckel; Steffen Schober; Martin Bossert (EURASIP J. Bioinform. Syst. Biol., 2013)

Identification of Sparse Linear Operators. - Reinhard Heckel; Helmut Bölcskei (IEEE Trans. Inf. Theory, 2013)

Neighborhood selection for thresholding-based subspace clustering. - Reinhard Heckel; Eirikur Agustsson; Helmut Bölcskei (ICASSP, 2014)

Compressive nonparametric graphical model selection for time series. - Alexander Jung; Reinhard Heckel; Helmut Bölcskei; Franz Hlawatsch (ICASSP, 2014)

Subspace clustering of dimensionality-reduced data. - Reinhard Heckel; Michael Tschannen; Helmut Bölcskei (ISIT, 2014)

Super-Resolution Radar. - Reinhard Heckel; Veniamin I. Morgenshtern; Mahdi Soltanolkotabi (CoRR, 2014)

Sparse signal processing: subspace clustering and system identification. - Reinhard Heckel (Hartung-Gorre, 2014)

Dimensionality-reduced subspace clustering. - Reinhard Heckel; Michael Tschannen; Helmut Bölcskei (CoRR, 2015)

Robust Subspace Clustering via Thresholding. - Reinhard Heckel; Helmut Bölcskei (IEEE Trans. Inf. Theory, 2015)

Super-resolution MIMO radar. - Reinhard Heckel (ISIT, 2016)

Active Ranking from Pairwise Comparisons and the Futility of Parametric Assumptions. - Reinhard Heckel; Nihar B. Shah; Kannan Ramchandran; Martin J. Wainwright (CoRR, 2016)

Interpretable recommendations via overlapping co-clusters. - Reinhard Heckel; Michail Vlachos (CoRR, 2016)

Toward interpretable predictive models in B2B recommender systems. - Michail Vlachos; Vassilios G. Vassiliadis; Reinhard Heckel; Abdel Labbi (IBM J. Res. Dev., 2016)

Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. - Reinhard Heckel; Michail Vlachos; Thomas P. Parnell; Celestine Dünner (ICDE, 2017)

The Sample Complexity of Online One-Class Collaborative Filtering. - Reinhard Heckel; Kannan Ramchandran (ICML, 2017)

Fundamental limits of DNA storage systems. - Reinhard Heckel; Ilan Shomorony; Kannan Ramchandran; David N. C. Tse (ISIT, 2017)

Private and Right-Protected Big Data Publication: An Analysis. - Reinhard Heckel; Michail Vlachos (SDM, 2017)

DiffuserCam: Lensless Single-exposure 3D Imaging. - Nick Antipa; Grace Kuo; Reinhard Heckel; Ben Mildenhall; Emrah Bostan; Ren Ng; Laura Waller (CoRR, 2017)

Approximate ranking from pairwise comparisons. - Reinhard Heckel; Max Simchowitz; Kannan Ramchandran; Martin J. Wainwright (AISTATS, 2018)

A Characterization of the DNA Data Storage Channel. - Reinhard Heckel; Gediminas Mikutis; Robert N. Grass (CoRR, 2018)

Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior. - Reinhard Heckel; Wen Huang; Paul Hand; Vladislav Voroninski (CoRR, 2018)

Unsupervised Learning with Stein's Unbiased Risk Estimator. - Christopher A. Metzler; Ali Mousavi 003; Reinhard Heckel; Richard G. Baraniuk (CoRR, 2018)

Super-resolution radar imaging via convex optimization. - Reinhard Heckel (CoRR, 2018)

Generalized Line Spectral Estimation via Convex Optimization. - Reinhard Heckel; Mahdi Soltanolkotabi (IEEE Trans. Inf. Theory, 2018)

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. - Daniel LeJeune; Reinhard Heckel; Richard G. Baraniuk (AISTATS, 2019)

A Fast and Robust Paradigm for Fourier Compressed Sensing Based on Coded Sampling. - Frank Ong; Reinhard Heckel; Kannan Ramchandran (ICASSP, 2019)

Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks. - Reinhard Heckel; Paul Hand (ICLR (Poster), 2019)

Capacity Results for the Noisy Shuffling Channel. - Ilan Shomorony; Reinhard Heckel (ISIT, 2019)

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. - Daniel LeJeune; Richard G. Baraniuk; Reinhard Heckel (CoRR, 2019)

Regularizing linear inverse problems with convolutional neural networks. - Reinhard Heckel (CoRR, 2019)

Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients. - Zhenwei Dai; Reinhard Heckel (CoRR, 2019)

Leveraging inductive bias of neural networks for learning without explicit human annotations. - Fatih Furkan Yilmaz; Reinhard Heckel (CoRR, 2019)

Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems. - Michail Vlachos; Celestine Dünner; Reinhard Heckel; Vassilios G. Vassiliadis; Thomas P. Parnell; Kubilay Atasu (IEEE Trans. Knowl. Data Eng., 2019)

Capacity of the Erasure Shuffling Channel. - Seiyun Shin; Reinhard Heckel; Ilan Shomorony (ICASSP, 2020)

Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators. - Reinhard Heckel; Mahdi Soltanolkotabi (ICLR, 2020)

Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation. - Reinhard Heckel; Mahdi Soltanolkotabi (ICML, 2020)

Reducing the Representation Error of GAN Image Priors Using the Deep Decoder. - Max Daniels; Paul Hand; Reinhard Heckel (CoRR, 2020)

Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation. - Reinhard Heckel; Mahdi Soltanolkotabi (CoRR, 2020)

Can Un-trained Neural Networks Compete with Trained Neural Networks at Image Reconstruction? - Mohammad Zalbagi Darestani; Reinhard Heckel (CoRR, 2020)

Early Stopping in Deep Networks: Double Descent and How to Eliminate it. - Reinhard Heckel; Fatih Furkan Yilmaz (ICLR, 2021)

Measuring Robustness in Deep Learning Based Compressive Sensing. - Mohammad Zalbagi Darestani; Akshay S. Chaudhari; Reinhard Heckel (ICML, 2021)

Data augmentation for deep learning based accelerated MRI reconstruction with limited data. - Zalan Fabian; Reinhard Heckel; Mahdi Soltanolkotabi (ICML, 2021)

Interpolation can hurt robust generalization even when there is no noise. - Konstantin Donhauser; Alexandru Tifrea; Michael Aerni; Reinhard Heckel; Fanny Yang (NeurIPS, 2021)

Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. - Zhenwei Dai; Aditya Desai; Reinhard Heckel; Anshumali Shrivastava (SIGMOD Conference, 2021)

Accelerated MRI With Un-Trained Neural Networks. - Mohammad Zalbagi Darestani; Reinhard Heckel (IEEE Trans. Computational Imaging, 2021)

DNA-Based Storage: Models and Fundamental Limits. - Ilan Shomorony; Reinhard Heckel (IEEE Trans. Inf. Theory, 2021)

Provable Continual Learning via Sketched Jacobian Approximations. - Reinhard Heckel (AISTATS, 2022)

Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes. - Kel Levick; Reinhard Heckel; Ilan Shomorony (CISS, 2022)

Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing. - Mohammad Zalbagi Darestani; Jiayu Liu; Reinhard Heckel (ICML, 2022)

Regularization-wise double descent: Why it occurs and how to eliminate it. - Fatih Furkan Yilmaz; Reinhard Heckel (ISIT, 2022)

Vision Transformers Enable Fast and Robust Accelerated MRI. - Kang Lin; Reinhard Heckel (MIDL, 2022)

Image-to-Image MLP-mixer for Image Reconstruction. - Youssef Mansour; Kang Lin; Reinhard Heckel (CoRR, 2022)

Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples. - Fatih Furkan Yilmaz; Reinhard Heckel (CoRR, 2022)

Information-Theoretic Foundations of DNA Data Storage. - Ilan Shomorony; Reinhard Heckel (Found. Trends Commun. Inf. Theory, 2022)

Untrained Graph Neural Networks for Denoising. - Samuel Rey; Santiago Segarra; Reinhard Heckel; Antonio G. Marques (IEEE Trans. Signal Process., 2022)

Guest Editorial. - Paul Hand; Reinhard Heckel; Jonathan Scarlett (IEEE J. Sel. Areas Inf. Theory, 2022)

Theoretical Perspectives on Deep Learning Methods in Inverse Problems. - Jonathan Scarlett; Reinhard Heckel; Miguel R. D. Rodrigues; Paul Hand; Yonina C. Eldar (IEEE J. Sel. Areas Inf. Theory, 2022)

Embracing errors is more effective than avoiding them through constrained coding for DNA data storage. - Franziska Weindel; Andreas L. Gimpel; Robert N. Grass; Reinhard Heckel (Allerton, 2023)

Zero-Shot Noise2Noise: Efficient Image Denoising without any Data. - Youssef Mansour; Reinhard Heckel (CVPR, 2023)

Scaling Laws For Deep Learning Based Image Reconstruction. - Tobit Klug; Reinhard Heckel (ICLR, 2023)

Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods. - Tobit Klug; Dogukan Atik; Reinhard Heckel (NeurIPS, 2023)

Learning Provably Robust Estimators for Inverse Problems via Jittering. - Anselm Krainovic; Mahdi Soltanolkotabi; Reinhard Heckel (NeurIPS, 2023)

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets. - Frédéric Wang; Han Qi; Alfredo De Goyeneche; Reinhard Heckel; Michael Lustig; Efrat Shimron (CoRR, 2023)

Embracing Errors is More Efficient than Avoiding Them through Constrained Coding for DNA Data Storage. - Franziska Weindel; Andreas L. Gimpel; Robert N. Grass; Reinhard Heckel (CoRR, 2023)

A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography. - Simon Wiedemann; Reinhard Heckel (CoRR, 2023)

Free-breathing 2D Cartesian Cardiac MRI Datasets. - Johannes Künzel; Stefan Ruschke; Reinhard Heckel (IEEE DataPort, 2023)

TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts. - Youssef Mansour; Xuyang Zhong; Serdar I. Caglar; Reinhard Heckel (ECCV (12), 2024)

Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data. - Kang Lin; Reinhard Heckel (ICML, 2024)

MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI. - Tobit Klug; Kun Wang; Stefan Ruschke; Reinhard Heckel (NeurIPS, 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

IR-FRestormer: Iterative Refinement with Fourier-Based Restormer for Accelerated MRI Reconstruction. - Mohammad Zalbagi Darestani; Vishwesh Nath; Wenqi Li 001; Yufan He; Holger R. Roth; Ziyue Xu 001; Daguang Xu; Reinhard Heckel; Can Zhao 001 (WACV, 2024)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (CoRR, 2024)

GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration. - Youssef Mansour; Reinhard Heckel (CoRR, 2024)

Deep Learning for Accelerated and Robust MRI Reconstruction: a Review. - Reinhard Heckel; Mathews Jacob; Akshay Chaudhari; Or Perlman; Efrat Shimron (CoRR, 2024)

Measuring Bias of Web-filtered Text Datasets and Bias Propagation Through Training. - Youssef Mansour; Reinhard Heckel (CoRR, 2024)

Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation. - Anselm Krainovic; Stefan Ruschke; Reinhard Heckel (CoRR, 2024)

Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization. - Daniel LeJeune; Jiayu Liu; Reinhard Heckel (J. Mach. Learn. Res., 2024)

Implicit Neural Networks With Fourier-Feature Inputs for Free-Breathing Cardiac MRI Reconstruction. - Johannes F. Kunz; Stefan Ruschke; Reinhard Heckel (IEEE Trans. Computational Imaging, 2024)

Approximating positive homogeneous functions with scale invariant neural networks. - Stefan Bamberger; Reinhard Heckel; Felix Krahmer (J. Approx. Theory, 2025)

LLM-Guided Search for Deletion-Correcting Codes. - Franziska Weindel; Reinhard Heckel (CoRR, 2025)

Efficient Noise Calculation in Deep Learning-based MRI Reconstructions. - Onat Dalmaz; Arjun D. Desai; Reinhard Heckel; Tolga Çukur; Akshay S. Chaudhari; Brian A. Hargreaves (CoRR, 2025)

OpenThoughts: Data Recipes for Reasoning Models. - Etash Kumar Guha; Ryan Marten; Sedrick Keh; Negin Raoof; Georgios Smyrnis; Hritik Bansal; Marianna Nezhurina; Jean Mercat; Trung Vu 002; Zayne Sprague; Ashima Suvarna; Benjamin Feuer; et al. (CoRR, 2025)

Trace Reconstruction with Language Models. - Franziska Weindel; Michael Girsch; Reinhard Heckel (CoRR, 2025)