Moroshko, Edward

12 publications

ICML 2023 Continual Learning in Linear Classification on Separable Data Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry

NeurIPS 2022 Finite Sample Analysis of Dynamic Regression Parameter Learning Mark Kozdoba, Edward Moroshko, Shie Mannor, Yacov Crammer

COLT 2022 How Catastrophic Can Catastrophic Forgetting Be in Linear Regression? Itay Evron, Edward Moroshko, Rachel Ward, Nathan Srebro, Daniel Soudry

ICML 2021 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry

NeurIPS 2020 Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy Edward Moroshko, Blake E Woodworth, Suriya Gunasekar, Jason Lee, Nati Srebro, Daniel Soudry

COLT 2020 Kernel and Rich Regimes in Overparametrized Models Blake Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro

NeurIPS 2018 Efficient Loss-Based Decoding on Graphs for Extreme Classification Itay Evron, Edward Moroshko, Koby Crammer

ECML-PKDD 2017 Online Regression with Controlled Label Noise Rate Edward Moroshko, Koby Crammer

JMLR 2015 Second-Order Non-Stationary Online Learning for Regression Edward Moroshko, Nina Vaits, Koby Crammer

AISTATS 2014 Selective Sampling with Drift Edward Moroshko, Koby Crammer

AISTATS 2013 A Last-Step Regression Algorithm for Non-Stationary Online Learning Edward Moroshko, Koby Crammer

ALT 2012 Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic Regret Edward Moroshko, Koby Crammer