Papers
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen, Shuyang Dai, Yunchen Pu et al.
Efficient and principled score estimation with Nyström kernel exponential families
Danica J. Sutherland, Heiko Strathmann, Michael Arbel et al.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Mathieu Sinn, Ambrish Rawat
Nonparametric Preference Completion
Julian Katz-Samuels, Clayton Scott
Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen et al.
Catalyst for Gradient-based Nonconvex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy et al.
An Optimization Approach to Learning Falling Rule Lists
Chaofan Chen, Cynthia Rudin
Transfer Learning on fMRI Datasets
Hejia Zhang, Po-Hsuan Chen, Peter Ramadge
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams
Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Hyunjik Kim, Yee Whye Teh
A Provable Algorithm for Learning Interpretable Scoring Systems
Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
Group Invariance Principles for Causal Generative Models
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf et al.
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods
Stephan Clémençon, François Portier
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization
Sunil Gupta, Alistair Shilton, Santu Rana et al.
Sum-Product-Quotient Networks
Or Sharir, Amnon Shashua
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes
Yang Cao, Liyan Xie, Yao Xie et al.
Regional Multi-Armed Bandits
Zhiyang Wang, Ruida Zhou, Cong Shen
Medoids in Almost-Linear Time via Multi-Armed Bandits
Vivek Bagaria, Govinda Kamath, Vasilis Ntranos et al.
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization
Emanuel Laude, Tao Wu, Daniel Cremers
Tensor Regression Meets Gaussian Processes
Rose Yu, Guangyu Li, Yan Liu
Personalized and Private Peer-to-Peer Machine Learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki et al.
Boosting Variational Inference: an Optimization Perspective
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh et al.
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
Tianbao Yang, Zhe Li, Lijun Zhang
Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele et al.