Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond. - Matan Schliserman; Tomer Koren (COLT, 2022)

Tight Risk Bounds for Gradient Descent on Separable Data. - Matan Schliserman; Tomer Koren (NeurIPS, 2023)

Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization. - Matan Schliserman; Tomer Koren (CoRR, 2024)

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. - Matan Schliserman; Uri Sherman; Tomer Koren (ALT, 2025)

Better Rates for Random Task Orderings in Continual Linear Models. - Itay Evron; Ran Levinstein; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Nathan Srebro (CoRR, 2025)

Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification. - Matan Schliserman; Tomer Koren (CoRR, 2025)

Optimal Rates in Continual Linear Regression via Increasing Regularization. - Ran Levinstein; Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Itay Evron (CoRR, 2025)

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime. - Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren (CoRR, 2025)