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Revisiting Design Choices in Proximal Policy Optimization
Proximal Policy Optimization (PPO) is a popular deep policy gradient alg...
09/23/2020 ∙ by Chloe Ching-Yun Hsu, et al. ∙
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Randomized Block-Diagonal Preconditioning for Parallel Learning
We study preconditioned gradient-based optimization methods where the pr...
06/24/2020 ∙ by Celestine Mendler-Dünner, et al. ∙
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Differentially Private Stochastic Coordinate Descent
In this paper we tackle the challenge of making the stochastic coordinat...
06/12/2020 ∙ by Georgios Damaskinos, et al. ∙
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Stochastic Optimization for Performative Prediction
In performative prediction, the choice of a model influences the distrib...
06/12/2020 ∙ by Celestine Mendler-Dünner, et al. ∙
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Performative Prediction
When predictions support decisions they may influence the outcome they a...
02/16/2020 ∙ by Juan C. Perdomo, et al. ∙
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SySCD: A System-Aware Parallel Coordinate Descent Algorithm
In this paper we propose a novel parallel stochastic coordinate descent ...
11/18/2019 ∙ by Nikolas Ioannou, et al. ∙
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Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of train...
10/15/2019 ∙ by Andreea Anghel, et al. ∙
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Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
Distributed machine learning training is one of the most common and impo...
09/11/2019 ∙ by Michael Kaufmann, et al. ∙
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Sampling Acquisition Functions for Batch Bayesian Optimization
This paper presents Acquisition Thompson Sampling (ATS), a novel algorit...
03/22/2019 ∙ by Alessandro De Palma, et al. ∙
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