Papers
Scaling Up Robust MDPs using Function Approximation
Aviv Tamar, Shie Mannor, Huan Xu
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
Jérémie Mary, Philippe Preux, Olivier Nicol
On the convergence of no-regret learning in selfish routing
Walid Krichene, Benjamin Drighès, Alexandre Bayen
Near-Optimally Teaching the Crowd to Classify
Adish Singla, Ilija Bogunovic, Gabor Bartok et al.
Geodesic Distance Function Learning via Heat Flow on Vector Fields
Binbin Lin, Ji Yang, Xiaofei He et al.
A Unified Framework for Consistency of Regularized Loss Minimizers
Jean Honorio, Tommi Jaakkola
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
Xiaotong Yuan, Ping Li, Tong Zhang
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection
Pratik Jawanpuria, Manik Varma, Saketha Nath
Convex Total Least Squares
Dmitry Malioutov, Nikolai Slavov
Near-Optimal Joint Object Matching via Convex Relaxation
Yuxin Chen, Leonidas Guibas, Qixing Huang
Rank-One Matrix Pursuit for Matrix Completion
Zheng Wang, Ming-Jun Lai, Zhaosong Lu et al.
Automated inference of point of view from user interactions in collective intelligence venues
Sanmay Das, Allen Lavoie
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu, Bo Huang, John Wright et al.
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Qixing Huang, Yuxin Chen, Leonidas Guibas
Robust Principal Component Analysis with Complex Noise
Qian Zhao, Deyu Meng, Zongben Xu et al.
Spectral Bandits for Smooth Graph Functions
Michal Valko, Remi Munos, Branislav Kveton et al.
Heavy-tailed regression with a generalized median-of-means
Daniel Hsu, Sivan Sabato
Concentration in unbounded metric spaces and algorithmic stability
Aryeh Kontorovich
Compact Random Feature Maps
Raffay Hamid, Ying Xiao, Alex Gittens et al.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
Masrour Zoghi, Shimon Whiteson, Remi Munos et al.
An Information Geometry of Statistical Manifold Learning
Ke Sun, Stéphane Marchand-Maillet
An Efficient Approach for Assessing Hyperparameter Importance
Frank Hutter, Holger Hoos, Kevin Leyton-Brown
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
Jian Zhou, Olga Troyanskaya
Filtering with Abstract Particles
Jacob Steinhardt, Percy Liang