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4 papers · 2 filters
Incremental Truncated LSTD
Clement Gehring, Yangchen Pan, Martha White
Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update…
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
Trevor Campbell, Julian Straub, John W. Fisher +1
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes rece…
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
Zeyuan Allen-Zhu, Zhenyu Liao, Lorenzo Orecchia
In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required r…
Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi +2
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we deve…