Papers
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris Oates, Steven Niederer, Angela Lee et al.
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu, Buser Say, Scott Sanner
Rotting Bandits
Nir Levine, Koby Crammer, Shie Mannor
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo et al.
Fair Clustering Through Fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi et al.
Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch et al.
Ranking Data with Continuous Labels through Oriented Recursive Partitions
Stéphan Clémençon, Mastane Achab
A Decomposition of Forecast Error in Prediction Markets
Miro Dudik, Sebastien Lahaie, Ryan M Rogers et al.
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Emmanouil Platanios, Hoifung Poon, Tom M. Mitchell et al.
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris M. Mooij et al.
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich, Sivan Sabato, Roi Weiss
An Empirical Study on The Properties of Random Bases for Kernel Methods
Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt et al.
Online Learning for Multivariate Hawkes Processes
Yingxiang Yang, Jalal Etesami, Niao He et al.
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Mikhail Yurochkin, Aritra Guha, Xuanlong Nguyen
Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch, Julien Mairal, Danilo Bzdok et al.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio
Regret Minimization in MDPs with Options without Prior Knowledge
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric et al.
Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal ALIAS PARTH GOYAL, Alessandro Sordoni, Marc-Alexandre Côté et al.
Subset Selection and Summarization in Sequential Data
Ehsan Elhamifar, M. Clara De Paolis Kaluza
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Linus Hamilton, Frederic Koehler, Ankur Moitra
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Alejandro Newell, Zhiao Huang, Jia Deng
Teaching Machines to Describe Images with Natural Language Feedback
huan ling, Sanja Fidler
Gauging Variational Inference
Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat A Erdogdu, Yash Deshpande, Andrea Montanari
Repeated Inverse Reinforcement Learning
Kareem Amin, Nan Jiang, Satinder Singh