Papers
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
Steven Cheng-Xian Li, Benjamin M. Marlin
On Regularizing Rademacher Observation Losses
Richard Nock
Variance Reduction in Stochastic Gradient Langevin Dynamics
Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson et al.
Multimodal Residual Learning for Visual QA
Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak et al.
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
Safe Policy Improvement by Minimizing Robust Baseline Regret
Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow
Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe, Kai Zhang, Pengyuan Wang et al.
Review Networks for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu et al.
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Hao Zhou, Vamsi K Ithapu, Sathya Narayanan Ravi et al.
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
Adaptive Skills Adaptive Partitions (ASAP)
Daniel J Mankowitz, Timothy A Mann, Shie Mannor
The Power of Adaptivity in Identifying Statistical Alternatives
Kevin G. Jamieson, Daniel Haas, Benjamin Recht
Measuring Neural Net Robustness with Constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos et al.
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
Himabindu Lakkaraju, Jure Leskovec
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother et al.
Threshold Learning for Optimal Decision Making
Nathan F Lepora
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Deconvolving Feedback Loops in Recommender Systems
Ayan Sinha, David F Gleich, Karthik Ramani
Clustering with Same-Cluster Queries
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
The Limits of Learning with Missing Data
Brian Bullins, Elad Hazan, Tomer Koren
Variational Inference in Mixed Probabilistic Submodular Models
Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan Fang
Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap et al.
What Makes Objects Similar: A Unified Multi-Metric Learning Approach
Han-Jia Ye, De-Chuan Zhan, Xue-Min Si et al.