Papers
Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions
Heng Lian, Zengyan Fan
Dual Principal Component Pursuit
Manolis C. Tsakiris, René Vidal
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
Christian Donner, Manfred Opper
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Quanming Yao, James T. Kwok
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö et al.
Emergence of Invariance and Disentanglement in Deep Representations
Alessandro Achille, Stefano Soatto
Enhancing Identification of Causal Effects by Pruning
Santtu Tikka, Juha Karvanen
Estimation of Graphical Models through Structured Norm Minimization
Davoud Ataee Tarzanagh, George Michailidis
Exact Learning of Lightweight Description Logic Ontologies
Boris Konev, Carsten Lutz, Ana Ozaki et al.
Experience Selection in Deep Reinforcement Learning for Control
Tim de Bruin, Jens Kober, Karl Tuyls et al.
Extrapolating Expected Accuracies for Large Multi-Class Problems
Charles Zheng, Rakesh Achanta, Yuval Benjamini
Fast MCMC Sampling Algorithms on Polytopes
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright et al.
From Predictive Methods to Missing Data Imputation: An Optimization Approach
Dimitris Bertsimas, Colin Pawlowski, Ying Daisy Zhuo
Gaussian Lower Bound for the Information Bottleneck Limit
Amichai Painsky, Naftali Tishby
Generalized Rank-Breaking: Computational and Statistical Tradeoffs
Ashish Khetan, Sewoong Oh
Gradient Descent Learns Linear Dynamical Systems
Moritz Hardt, Tengyu Ma, Benjamin Recht
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe
Gradient Hard Thresholding Pursuit
Xiao-Tong Yuan, Ping Li, Tong Zhang
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
Christian Kümmerle, Juliane Sigl
Hinge-Minimax Learner for the Ensemble of Hyperplanes
Dolev Raviv, Tamir Hazan, Margarita Osadchy
How Deep Are Deep Gaussian Processes?
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart et al.
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo et al.
HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen et al.