I'm a PhD student in the Department of Computer Science at Tel Aviv University, where I'm advised by Prof. Tomer Koren. I previously completed my MSc under his supervision, following a BSc in Computer Science and Mathematics from the Hebrew University of Jerusalem. My research focuses on optimization and generalization in machine learning.
Near-Optimal Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
Matan Schliserman, Tomer Koren
Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity
Shira Vansover-Hager, Matan Schliserman, Ofir Schlisselberg, Tomer Koren
Convergence of Continual Learning in Homogeneous Deep Networks
Matan Schliserman, Gon Buzaglo, Itay Evron, Daniel Soudry
Flat Minima and Generalization: Insights from Stochastic Convex Optimization
Matan Schliserman*, Shira Vansover-Hager*, Tomer Koren
Oral Presentation at OPT2025
From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
Itay Evron*, Ran Levinstein*, Matan Schliserman*, Uri Sherman*, Tomer Koren, Daniel Soudry, Nathan Srebro
Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
Matan Schliserman, Tomer Koren
ALT 2026, Elegant paper award
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
Amit Attia*, Matan Schliserman*, Uri Sherman, Tomer Koren
Optimal Rates in Continual Linear Regression via Increasing Regularization
Ran Levinstein*, Amit Attia*, Matan Schliserman*, Uri Sherman*, Tomer Koren, Daniel Soudry, Itay Evron
Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification
Matan Schliserman, Tomer Koren
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
Matan Schliserman, Uri Sherman, Tomer Koren
ALT 2025, Outstanding paper award
Oral Presentation at OPT2024
Tight risk bounds for gradient descent on separable data
Matan Schliserman, Tomer Koren
NeurIPS 2023, Spotlight