Papers
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan et al.
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley et al.
Structure Discovery in Bayesian Networks by Sampling Partial Orders
Teppo Niinimäki, Pekka Parviainen, Mikko Koivisto
Causal Inference through a Witness Protection Program
Ricardo Silva, Robin Evans
Adaptive Lasso and group-Lasso for functional Poisson regression
Stéphane Ivanoff, Franck Picard, Vincent Rivoirard
Estimating Causal Structure Using Conditional DAG Models
Chris. J. Oates, Jim Q. Smith, Sach Mukherjee
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
Mert Pilanci, Martin J. Wainwright
Subspace Learning with Partial Information
Alon Gonen, Dan Rosenbaum, Yonina C. Eldar et al.
Differentially Private Data Releasing for Smooth Queries
Ziteng Wang, Chi Jin, Kai Fan et al.
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
Wei Chen, Yajun Wang, Yang Yuan et al.
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
Shusen Wang, Luo Luo, Zhihua Zhang
Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu et al.
Large Scale Online Kernel Learning
Jing Lu, Steven C.H. Hoi, Jialei Wang et al.
Addressing Environment Non-Stationarity by Repeating Q-learning Updates
Sherief Abdallah, Michael Kaisers
A Unified View on Multi-class Support Vector Classification
Ürün Doğan, Tobias Glasmachers, Christian Igel
Scalable Learning of Bayesian Network Classifiers
Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen et al.
On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
Ery Arias-Castro, David Mason, Bruno Pelletier
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
BayesPy: Variational Bayesian Inference in Python
Jaakko Luttinen
On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint
Chong Zhang, Yufeng Liu, Yichao Wu
End-to-End Training of Deep Visuomotor Policies
Sergey Levine, Chelsea Finn, Trevor Darrell et al.
The Optimal Sample Complexity of PAC Learning
Steve Hanneke
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks
Shiliang Zhang, Hui Jiang, Lirong Dai
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors
Julianus Pfeuffer, Oliver Serang
OLPS: A Toolbox for On-Line Portfolio Selection
Bin Li, Doyen Sahoo, Steven C.H. Hoi