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Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds
We propose novel randomized optimization methods for high-dimensional co...
12/13/2020 ∙ by Jonathan Lacotte, et al. ∙
9
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All Local Minima are Global for Two-Layer ReLU Neural Networks: The Hidden Convex Optimization Landscape
We are interested in two-layer ReLU neural networks from an optimization...
06/10/2020 ∙ by Jonathan Lacotte, et al. ∙
12
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Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
We propose a new randomized algorithm for solving L2-regularized least-s...
06/10/2020 ∙ by Jonathan Lacotte, et al. ∙
3
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Optimal Randomized First-Order Methods for Least-Squares Problems
We provide an exact analysis of a class of randomized algorithms for sol...
02/21/2020 ∙ by Jonathan Lacotte, et al. ∙
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Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods
We provide an exact analysis of the limiting spectrum of matrices random...
02/03/2020 ∙ by Jonathan Lacotte, et al. ∙
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Faster Least Squares Optimization
We investigate randomized methods for solving overdetermined linear leas...
11/06/2019 ∙ by Jonathan Lacotte, et al. ∙
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High-Dimensional Optimization in Adaptive Random Subspaces
We propose a new randomized optimization method for high-dimensional pro...
06/27/2019 ∙ by Jonathan Lacotte, et al. ∙
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Risk-Sensitive Generative Adversarial Imitation Learning
We study risk-sensitive imitation learning where the agent's goal is to ...
08/13/2018 ∙ by Jonathan Lacotte, et al. ∙
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Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
The literature on Inverse Reinforcement Learning (IRL) typically assumes...
11/28/2017 ∙ by Sumeet Singh, et al. ∙
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