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Found 9 papers, 4 papers with code
Robust and differentially private stochastic linear bandits
In this paper, we study the stochastic linear bandit problem under the additional requirements of differential privacy, robustness and batched observations.
Reducing Aggregate Electric Vehicle Battery Capacity through Sharing
More precisely, we consider the problem of minimizing aggregate battery capacity in settings with and without a shared resource subject to the requirement that driver commuting needs are met with high reliability.
Communication-efficient distributed eigenspace estimation with arbitrary node failures
We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses.
Communication-efficient distributed eigenspace estimation
Spectral methods are a collection of such problems, where solutions are orthonormal bases of the leading invariant subspace of an associated data matrix, which are only unique up to rotation and reflections.
Entrywise convergence of iterative methods for eigenproblems
Several problems in machine learning, statistics, and other fields rely on computing eigenvectors.
Stochastic algorithms with geometric step decay converge linearly on sharp functions
In this work, we ask whether geometric step decay similarly improves stochastic algorithms for the class of sharp nonconvex problems.
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science.
Composite optimization for robust blind deconvolution
The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements.
A Tropical Approach to Neural Networks with Piecewise Linear Activations
We present a new, unifying approach following some recent developments on the complexity of neural networks with piecewise linear activations.
