Papers
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote et al.
Adaptive Clustering through Semidefinite Programming
Martin Royer
Riemannian approach to batch normalization
Minhyung Cho, Jaehyung Lee
A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei et al.
Learning with Bandit Feedback in Potential Games
Amélie Heliou, Johanne Cohen, Panayotis Mertikopoulos
Shallow Updates for Deep Reinforcement Learning
Nir Levine, Tom Zahavy, Daniel J Mankowitz et al.
Variational Memory Addressing in Generative Models
Jörg Bornschein, Andriy Mnih, Daniel Zoran et al.
Invariance and Stability of Deep Convolutional Representations
Alberto Bietti, Julien Mairal
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro et al.
Effective Parallelisation for Machine Learning
Michael Kamp, Mario Boley, Olana Missura et al.
Independence clustering (without a matrix)
Daniil Ryabko
Online Reinforcement Learning in Stochastic Games
Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Solving Most Systems of Random Quadratic Equations
Gang Wang, Georgios Giannakis, Yousef Saad et al.
Efficient Second-Order Online Kernel Learning with Adaptive Embedding
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal, Randy Jia
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Roger B Grosse et al.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, xuejun Liao, Lawrence Carin
Variational Inference via χ Upper Bound Minimization
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath et al.
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Yue Wang, Wei Chen, Yuting Liu et al.
Hypothesis Transfer Learning via Transformation Functions
Simon S Du, Jayanth Koushik, Aarti Singh et al.
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li, Lin Yang, Jason Ge et al.
Successor Features for Transfer in Reinforcement Learning
Andre Barreto, Will Dabney, Remi Munos et al.
Pose Guided Person Image Generation
Liqian Ma, Xu Jia, Qianru Sun et al.