Statistical Learning
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Neural Information Processing Systems http://nips.cc/
Total Least Squares Regression in Input Sparsity Time
Huaian Diao, Zhao Song, David Woodruff, Xin Yang
In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices ร and B, so that there exists an X satisfying the equation รX = B. Typically the problem is overconstrained, meaning that m max(n, d).
Computing Higher Order Derivatives of Matrix and Tensor Expressions
Soeren Laue, Matthias Mitterreiter, Joachim Giesen
Optimization is an integral part of most machine learning systems and most numerical optimization schemes relyonthecomputation ofderivatives.
Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Neural Information Processing Systems http://nips.cc/
Learning to Discover Skills through Guidance Hyunseung Kim,1 Byungkun Lee,1 Hojoon Lee
However, we have identified that the effectiveness of these rewards declines as the environmental complexity rises. Therefore, we present a novel USD algorithm, skill disco very with gui dance ( DISCO-DANCE), which (1) selects the guide skill that possesses the highest potential to reach unexplored states, (2) guides other skills to follow guide skill, then (3) the guided skills are dispersed to maximize their discriminability in unexplored states. Empirical evaluation demonstrates that DISCO-DANCE outperforms other USD baselines in challenging environments, including two navigation benchmarks and a continuous control benchmark.
A Dual Framework for Low-rank Tensor Completion
Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra
Theworks[41,42]discuss theoverlapped and latent trace norm regularizations for tensors.
Practical Differentially Private Hyperparameter Tuning with Subsampling
The reasons for this are clear.