Papers
On Nonparametric Guidance for Learning Autoencoder Representations
Jasper Snoek, Ryan Adams, Hugo Larochelle
On Ranking and Generalization Bounds
Wojciech Rejchel
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models
David Buchman, Mark Schmidt, Shakir Mohamed et al.
On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference
Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar
On the connections between saliency and tracking
Vijay Mahadevan, Nuno Vasconcelos
On the Convergence Rate of -Norm Multiple Kernel Learning
Marius Kloft, Gilles Blanchard
On the Necessity of Irrelevant Variables
David P. Helmbold, Philip M. Long
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking
Clément Calauzènes, Nicolas Usunier, Patrick Gallinari
On the Sample Complexity of Robust PCA
Matthew Coudron, Gilad Lerman
On the Structure of Nonlinearities in Pose Graph SLAM
Heng Wang, Gibson Hu, Shoudong Huang et al.
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
Bruno Scherrer, Boris Lesner
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks
Qirong Ho, Junming Yin, Eric P. Xing
On Using Nearly-Independent Feature Families for High Precision and Confidence
Omid Madani, Manfred Georg, David A. Ross
Open Problem: Better Bounds for Online Logistic Regression
H. Brendan McMahan, Matthew Streeter
Open Problem: Does AdaBoost Always Cycle?
Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
Open Problem: Learning Dynamic Network Models from a Static Snapshot
Jan Ramon, Constantin Comendant
Optimal Control with Weighted Average Costs and Temporal Logic Specifications
Eric Wolff, Ufuk Topcu, Richard Murray
Optimal Distributed Online Prediction Using Mini-Batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir et al.
Optimal kernel choice for large-scale two-sample tests
Arthur Gretton, Dino Sejdinovic, Heiko Strathmann et al.
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss
Zhuo Wang, Alan Stocker, Daniel D Lee
Optimal Regularized Dual Averaging Methods for Stochastic Optimization
Xi Chen, Qihang Lin, Javier Pena
Optimistic Bayesian Sampling in Contextual-Bandit Problems
Benedict C. May, Nathan Korda, Anthony Lee et al.
Optimistic planning for Markov decision processes
Lucian Busoniu, Remi Munos
Optimization-Based Estimator Design for Vision-Aided Inertial Navigation
Mingyang Li, Anastasios Mourikis