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Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices
Motivated by the popularity of stochastic rounding in the context of machine learning and the training of large-scale deep neural network models, we consider stochastic nearness rounding of real matrices $\mathbf{A}$ with many more rows than columns.
A Precise Characterization of SGD Stability Using Loss Surface Geometry
In this paper, we delve deeper into the relationship between linear stability and sharpness.
Feature Space Sketching for Logistic Regression
We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression.
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior.
