Papers
660 papers found
Better Algorithms for Benign Bandits
Elad Hazan, Satyen Kale
Clustering Algorithms for Chains
Antti Ukkonen
Sampling Methods for the Nyström Method
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
Pattern for Python
Tom De Smedt, Walter Daelemans
Ranking Forests
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
A Novel M-Estimator for Robust PCA
Teng Zhang, Gilad Lerman
New Results for Random Walk Learning
Jeffrey C. Jackson, Karl Wimmer
A Gibbs Sampler for Learning DAGs
Robert J. B. Goudie, Sach Mukherjee
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö et al.
Importance Sampling for Minibatches
Dominik Csiba, Peter Richtárik
On Tight Bounds for the Lasso
Sara van de Geer
Significance Tests for Neural Networks
Enguerrand Horel, Kay Giesecke
Dual Extrapolation for Sparse GLMs
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort et al.
Risk Bounds for Reservoir Computing
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra et al.
A Stochastic Bundle Method for Interpolation
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel et al.
Power Iteration for Tensor PCA
Jiaoyang Huang, Daniel Z. Huang, Qing Yang et al.
Tree-Based Models for Correlated Data
Assaf Rabinowicz, Saharon Rosset
Convergence Guarantees for the Good-Turing Estimator
Amichai Painsky
Neural Q-learning for solving PDEs
Samuel N. Cohen, Deqing Jiang, Justin Sirignano
Multilevel CNNs for Parametric PDEs
Cosmas Heiß, Ingo Gühring, Martin Eigel
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding et al.
The Nyström method for convex loss functions
Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada et al.
Operator Learning for Hyperbolic PDEs
Christopher Wang, Alex Townsend
Boosted Trees for Risk Prognosis
Alexis Bellot, Mihaela van der Schaar