Decoupling "when to update" from "how to update". - Eran Malach; Shai Shalev-Shwartz (NIPS, 2017)

SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. - Alon Brutzkus; Amir Globerson; Eran Malach; Shai Shalev-Shwartz (ICLR (Poster), 2018)

A Provably Correct Algorithm for Deep Learning that Actually Works. - Eran Malach; Shai Shalev-Shwartz (CoRR, 2018)

Is Deeper Better only when Shallow is Good? - Eran Malach; Shai Shalev-Shwartz (NeurIPS, 2019)

Decoupling Gating from Linearity. - Jonathan Fiat; Eran Malach; Shai Shalev-Shwartz (CoRR, 2019)

On the Optimality of Trees Generated by ID3. - Alon Brutzkus; Amit Daniely; Eran Malach (CoRR, 2019)

Learning Boolean Circuits with Neural Networks. - Eran Malach; Shai Shalev-Shwartz (CoRR, 2019)

ID3 Learns Juntas for Smoothed Product Distributions. - Alon Brutzkus; Amit Daniely; Eran Malach (COLT, 2020)

Proving the Lottery Ticket Hypothesis: Pruning is All You Need. - Eran Malach; Gilad Yehudai; Shai Shalev-Shwartz; Ohad Shamir (ICML, 2020)

Learning Parities with Neural Networks. - Amit Daniely; Eran Malach (NeurIPS, 2020)

The Implications of Local Correlation on Learning Some Deep Functions. - Eran Malach; Shai Shalev-Shwartz (NeurIPS, 2020)

The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. - Eran Malach; Gilad Yehudai; Shai Shalev-Shwartz; Ohad Shamir (COLT, 2021)

Computational Separation Between Convolutional and Fully-Connected Networks. - Eran Malach; Shai Shalev-Shwartz (ICLR, 2021)

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. - Eran Malach; Pritish Kamath; Emmanuel Abbe; Nathan Srebro (ICML, 2021)

On the Power of Differentiable Learning versus PAC and SQ Learning. - Emmanuel Abbe; Pritish Kamath; Eran Malach; Colin Sandon; Nathan Srebro (NeurIPS, 2021)

Efficient Learning of CNNs using Patch Based Features. - Alon Brutzkus; Amir Globerson; Eran Malach; Alon Regev Netser; Shai Shalev-Shwartz (ICML, 2022)

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. - Boaz Barak; Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2022)

Knowledge Distillation: Bad Models Can Be Good Role Models. - Gal Kaplun; Eran Malach; Preetum Nakkiran; Shai Shalev-Shwartz (NeurIPS, 2022)

When Hardness of Approximation Meets Hardness of Learning. - Eran Malach; Shai Shalev-Shwartz (J. Mach. Learn. Res., 2022)

Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2023)

SubTuning: Efficient Finetuning for Multi-Task Learning. - Gal Kaplun; Andrey Gurevich; Tal Swisa; Mazor David; Shai Shalev-Shwartz; Eran Malach (CoRR, 2023)

Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD. - Etay Livne; Gal Kaplun; Eran Malach; Shai Shalev-Schwatz (CoRR, 2023)

Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (CoRR, 2023)

Repeat After Me: Transformers are Better than State Space Models at Copying. - Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach (ICML, 2024)

Auto-Regressive Next-Token Predictors are Universal Learners. - Eran Malach (ICML, 2024)

The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains. - Ezra Edelman; Nikolaos Tsilivis 002; Benjamin L. Edelman; Eran Malach; Surbhi Goel (NeurIPS, 2024)

On the Power of Decision Trees in Auto-Regressive Language Modeling. - Yulu Gan; Tomer Galanti; Tomaso A. Poggio; Eran Malach (NeurIPS, 2024)

Transcendence: Generative Models Can Outperform The Experts That Train Them. - Edwin Zhang; Vincent Zhu; Naomi Saphra; Anat Kleiman; Benjamin L. Edelman; Milind Tambe; Sham M. Kakade; Eran Malach (NeurIPS, 2024)

Universal Length Generalization with Turing Programs. - Kaiying Hou; David Brandfonbrener; Sham M. Kakade; Samy Jelassi; Eran Malach (CoRR, 2024)

Is ML-Based Cryptanalysis Inherently Limited? Simulating Cryptographic Adversaries via Gradient-Based Methods. - Avital Shafran; Eran Malach; Thomas Ristenpart; Gil Segev 001; Stefano Tessaro (IACR Cryptol. ePrint Arch., 2024)

LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks. - Akshara Prabhakar; Yuanzhi Li; Karthik Narasimhan; Sham M. Kakade; Eran Malach; Samy Jelassi (COLING (Industry), 2025)

Mixture of Parrots: Experts improve memorization more than reasoning. - Samy Jelassi; Clara Mohri; David Brandfonbrener; Alex Gu; Nikhil Vyas 001; Nikhil Anand; David Alvarez-Melis; Yuanzhi Li; Sham M. Kakade; Eran Malach (ICLR, 2025)

A New Perspective on Shampoo's Preconditioner. - Depen Morwani; Itai Shapira; Nikhil Vyas 001; Eran Malach; Sham M. Kakade; Lucas Janson (ICLR, 2025)

Don't stop me Now: Embedding based Scheduling for LLMS. - Rana Shahout; Eran Malach; Chunwei Liu; Weifan Jiang; Minlan Yu; Michael Mitzenmacher (ICLR, 2025)

Loss-to-Loss Prediction: Scaling Laws for All Datasets. - David Brandfonbrener; Nikhil Anand; Nikhil Vyas 001; Eran Malach; Sham M. Kakade (Trans. Mach. Learn. Res., 2025)

The Role of Sparsity for Length Generalization in Transformers. - Noah Golowich; Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach (CoRR, 2025)

To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning. - Tian Qin; David Alvarez-Melis; Samy Jelassi; Eran Malach (CoRR, 2025)

Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining. - Rosie Zhao; Alexandru Meterez; Sham M. Kakade; Cengiz Pehlevan; Samy Jelassi; Eran Malach (CoRR, 2025)

The Power of Random Features and the Limits of Distribution-Free Gradient Descent. - Ari Karchmer; Eran Malach (CoRR, 2025)

Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones. - Parsa Mirtaheri; Ezra Edelman; Samy Jelassi; Eran Malach; Enric Boix-Adserà (CoRR, 2025)

Decomposing Elements of Problem Solving: What "Math" Does RL Teach? - Tian Qin; Core Francisco Park; Mujin Kwun; Aaron Walsman; Eran Malach; Nikhil Anand; Hidenori Tanaka; David Alvarez-Melis (CoRR, 2025)