174 citations · 991 across the 46 of their papers we have counts for
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cs.LG2022★ 1 cited
Feature selection with gradient descent on two-layer networks in low-rotation regimes
Matus Telgarsky
This work establishes low test error of gradient flow (GF) and stochastic gradient descent (SGD) on two-layer ReLU networks with standard initialization, in three regimes where key…
math.OC2022
Astral Space: Convex Analysis at Infinity
Miroslav Dudík, Robert E. Schapire, Matus Telgarsky
Not all convex functions on have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for un…
cs.LG2022★ 2 cited
Stochastic linear optimization never overfits with quadratically-bounded losses on general data
Matus Telgarsky
This work provides test error bounds for iterative fixed point methods on linear predictors -- specifically, stochastic and batch mirror descent (MD), and stochastic temporal diffe…