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Revisiting Design Choices in Proximal Policy Optimization
Proximal Policy Optimization (PPO) is a popular deep policy gradient alg...
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Randomized Block-Diagonal Preconditioning for Parallel Learning
We study preconditioned gradient-based optimization methods where the pr...
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Differentially Private Stochastic Coordinate Descent
In this paper we tackle the challenge of making the stochastic coordinat...
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Stochastic Optimization for Performative Prediction
In performative prediction, the choice of a model influences the distrib...
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Performative Prediction
When predictions support decisions they may influence the outcome they a...
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SySCD: A System-Aware Parallel Coordinate Descent Algorithm
In this paper we propose a novel parallel stochastic coordinate descent ...
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Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of train...
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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...
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Sampling Acquisition Functions for Batch Bayesian Optimization
This paper presents Acquisition Thompson Sampling (ATS), a novel algorit...
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