Papers
Nonlinear Inverse Reinforcement Learning with Gaussian Processes
Sergey Levine, Zoran Popovic, Vladlen Koltun
Multi-Bandit Best Arm Identification
Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric et al.
Probabilistic amplitude and frequency demodulation
Richard Turner, Maneesh Sahani
Shaping Level Sets with Submodular Functions
Francis R. Bach
Active dendrites: adaptation to spike-based communication
Balazs B. Ujfalussy, Máté Lengyel
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC
Trung T. Pham, Tat-jun Chin, Jin Yu et al.
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Philipp Krähenbühl, Vladlen Koltun
Thinning Measurement Models and Questionnaire Design
Ricardo Silva
Semi-supervised Regression via Parallel Field Regularization
Binbin Lin, Chiyuan Zhang, Xiaofei He
Algorithms and hardness results for parallel large margin learning
Phil Long, Rocco Servedio
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent
Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
Energetically Optimal Action Potentials
Martin B. Stemmler, Biswa Sengupta, Simon Laughlin et al.
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
Gilles Blanchard, Gyemin Lee, Clayton Scott
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Andrew Cotter, Ohad Shamir, Nati Srebro et al.
Reinforcement Learning using Kernel-Based Stochastic Factorization
Andre Barreto, Doina Precup, Joelle Pineau
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability
David P. Reichert, Peggy Series, Amos J. Storkey
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning
Michalis K. Titsias, Miguel Lázaro-Gredilla
Sparse Features for PCA-Like Linear Regression
Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
A Machine Learning Approach to Predict Chemical Reactions
Matthew A. Kayala, Pierre F. Baldi
Statistical Performance of Convex Tensor Decomposition
Ryota Tomioka, Taiji Suzuki, Kohei Hayashi et al.
Statistical Tests for Optimization Efficiency
Levi Boyles, Anoop Korattikara, Deva Ramanan et al.
Variational Gaussian Process Dynamical Systems
Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
Generalized Beta Mixtures of Gaussians
Artin Armagan, Merlise Clyde, David B. Dunson
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation
Nico Goernitz, Christian K. Widmer, Georg Zeller et al.