Yehuda Dar - RankMe

39 papers A* 2B 2C 1Journal 31Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing. CoRR Daniel Boharon, Yehuda Dar
2025	J	`jnl`	How Does Overparameterization Affect Machine Unlearning of Deep Neural Networks? CoRR Gal Alon, Yehuda Dar
2025	J	`jnl`	Mixture of Many Zero-Compute Experts: A High-Rate Quantization Theory Perspective. CoRR Yehuda Dar
2024	J	`jnl`	How Does Perfect Fitting Affect Representation Learning? On the Training Dynamics of Representations in Deep Neural Networks. CoRR Yuval Sharon, Yehuda Dar
2024	—	`conf`	How Much Training Data Is Memorized in Overparameterized Autoencoders? An Inverse Problem Perspective on Memorization Evaluation. ECML/PKDD (2) Koren Abitbul, Yehuda Dar
2024	J	`jnl`	TL-PCA: Transfer Learning of Principal Component Analysis. CoRR Sharon Hendy, Yehuda Dar
2024	J	`jnl`	The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. SIAM J. Math. Data Sci. Yehuda Dar, Daniel LeJeune, Richard G. Baraniuk
2023	J	`jnl`	Recovery of Training Data from Overparameterized Autoencoders: An Inverse Problem Perspective. CoRR Koren Abitbul, Yehuda Dar
2022	A*	`conf`	Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein
2022	J	`jnl`	Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein
2022	J	`jnl`	Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks. SIAM J. Math. Data Sci. Yehuda Dar, Richard G. Baraniuk
2022	J	`jnl`	Overfreezing Meets Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks. CoRR Yehuda Dar, Lorenzo Luzi, Richard G. Baraniuk
2021	J	`jnl`	A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning. CoRR Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk
2021	J	`jnl`	Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images. J. Math. Imaging Vis. Yehuda Dar, Alfred M. Bruckstein
2021	J	`jnl`	Double Descent and Other Interpolation Phenomena in GANs. CoRR Lorenzo Luzi, Yehuda Dar, Richard G. Baraniuk
2021	J	`jnl`	Transfer Learning Can Outperform the True Prior in Double Descent Regularization. CoRR Yehuda Dar, Richard G. Baraniuk
2020	J	`jnl`	Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks. CoRR Yehuda Dar, Richard G. Baraniuk
2020	J	`jnl`	Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding. CoRR Veronica Corona, Yehuda Dar, Carola-Bibiane Schönlieb
2020	A*	`conf`	Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. ICML Yehuda Dar, Paul M. Mayer, Lorenzo Luzi, Richard G. Baraniuk
2020	J	`jnl`	Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. CoRR Yehuda Dar, Paul M. Mayer, Lorenzo Luzi, Richard G. Baraniuk
2019	—	`conf`	Algorithms for Piecewise Constant Signal Approximations. EUSIPCO Leif Bergerhoff, Joachim Weickert, Yehuda Dar
2019	J	`jnl`	Algorithms for Piecewise Constant Signal Approximations. CoRR Leif Bergerhoff, Joachim Weickert, Yehuda Dar
2019	J	`jnl`	Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images. CoRR Yehuda Dar, Alfred M. Bruckstein
2019	J	`jnl`	On High-Resolution Adaptive Sampling of Deterministic Signals. J. Math. Imaging Vis. Yehuda Dar, Alfred M. Bruckstein
2018	B	`conf`	Compression for Multiple Reconstructions. ICIP Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2018	J	`jnl`	Compression for Multiple Reconstructions. CoRR Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2018	—	`—`	New Methods for Signal Compression and Their Relations to Restoration Problems. Yehuda Dar
2018	J	`jnl`	Optimized Pre-Compensating Compression. IEEE Trans. Image Process. Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2018	J	`jnl`	Restoration by Compression. IEEE Trans. Signal Process. Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2018	B	`conf`	System-Aware Compression. ISIT Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2018	J	`jnl`	System-Aware Compression. CoRR Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2017	J	`jnl`	Optimized Pre-Compensating Compression. CoRR Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2017	J	`jnl`	Restoration by Compression. CoRR Yehuda Dar, Michael Elad, Alfred M. Bruckstein
2016	C	`conf`	Image restoration via successive compression. PCS Yehuda Dar, Alfred M. Bruckstein, Michael Elad
2016	J	`jnl`	Optimal High-Resolution Adaptive Sampling of Deterministic Signals. CoRR Yehuda Dar, Alfred M. Bruckstein
2016	J	`jnl`	Postprocessing of Compressed Images via Sequential Denoising. IEEE Trans. Image Process. Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes
2015	J	`jnl`	Postprocessing of Compressed Images via Sequential Denoising. CoRR Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes
2014	J	`jnl`	Improving Low Bit-Rate Video Coding using Spatio-Temporal Down-Scaling. CoRR Yehuda Dar, Alfred M. Bruckstein
2014	J	`jnl`	Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis. CoRR Yehuda Dar, Alfred M. Bruckstein