3 citations · 4 across the 8 of their papers we have counts for
11 papers
Convergence of Continual Learning in Homogeneous Deep Networks
Matan Schliserman, Gon Buzaglo, Itay Evron +1
We characterize weakly regularized continual classification in homogeneous models as sequential projections onto task margin sets. This result generalizes prior analyses restricted…
Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity
Shira Vansover-Hager, Matan Schliserman, Ofir Schlisselberg +1
Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization in reinforcement learning a…
Flat Minima and Generalization: Insights from Stochastic Convex Optimization
Matan Schliserman, Shira Vansover-Hager, Tomer Koren
Understanding the generalization behavior of learning algorithms is a central goal of learning theory. A recently emerging explanation is that learning algorithms are successful in…
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
Amit Attia, Matan Schliserman, Uri Sherman +1
We study population convergence guarantees of stochastic gradient descent (SGD) for smooth convex objectives in the interpolation regime, where the noise at optimum is zero or near…
Optimal Rates in Continual Linear Regression via Increasing Regularization
Ran Levinstein, Amit Attia, Matan Schliserman +4
We study realizable continual linear regression under random task orderings, a common setting for developing continual learning theory. In this setup, the worst-case expected loss…
Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification
Matan Schliserman, Tomer Koren
We study the generalization performance of unregularized gradient methods for separable linear classification. While previous work mostly deal with the binary case, we focus on the…
From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
Itay Evron, Ran Levinstein, Matan Schliserman +4
We study the common continual learning setup where an overparameterized model is sequentially fitted to a set of jointly realizable tasks. We analyze forgetting, defined as the los…
Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
Matan Schliserman, Tomer Koren
We study the problem of learning vector-valued linear predictors: these are prediction rules parameterized by a matrix that maps an -dimensional feature vector to a -dimensio…
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
Matan Schliserman, Uri Sherman, Tomer Koren
We study the generalization performance of gradient methods in the fundamental stochastic convex optimization setting, focusing on its dimension dependence. First, for full-batch g…
Tight Risk Bounds for Gradient Descent on Separable Data
Matan Schliserman, Tomer Koren
We study the generalization properties of unregularized gradient methods applied to separable linear classification -- a setting that has received considerable attention since the…
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond
Matan Schliserman, Tomer Koren
An influential line of recent work has focused on the generalization properties of unregularized gradient-based learning procedures applied to separable linear classification with…