Papers
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang et al.
Adaptation Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
Wenjing Liao, Mauro Maggioni
ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM
Setareh Ariafar, Jaume Coll-Font, Dana Brooks et al.
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets
Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer et al.
A Kernel Multiple Change-point Algorithm via Model Selection
Sylvain Arlot, Alain Celisse, Zaid Harchaoui
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher, Cynthia Rudin, Francesca Dominici
Analysis of Langevin Monte Carlo via Convex Optimization
Alain Durmus, Szymon Majewski, Błażej Miasojedow
Analysis of spectral clustering algorithms for community detection: the general bipartite setting
Zhixin Zhou, Arash A.Amini
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
Mehmet Eren Ahsen, Mathukumalli Vidyasagar
An asymptotic analysis of distributed nonparametric methods
Botond Szabó, Harry van Zanten
An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search
Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos
A New Approach to Laplacian Solvers and Flow Problems
Patrick Rebeschini, Sekhar Tatikonda
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
Muhammad A Masood, Finale Doshi-Velez
Approximate Profile Maximum Likelihood
Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman
Approximation Algorithms for Stochastic Clustering
David G. Harris, Shi Li, Thomas Pensyl et al.
Approximation Hardness for A Class of Sparse Optimization Problems
Yichen Chen, Yinyu Ye, Mengdi Wang
Approximations of the Restless Bandit Problem
Steffen Grünewälder, Azadeh Khaleghi
A Representer Theorem for Deep Kernel Learning
Bastian Bohn, Michael Griebel, Christian Rieger
A Representer Theorem for Deep Neural Networks
Michael Unser
Automated Scalable Bayesian Inference via Hilbert Coresets
Trevor Campbell, Tamara Broderick
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
Tyler Maunu, Teng Zhang, Gilad Lerman
Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures
Gregor Pirš, Erik Štrumbelj
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham, Eytan Bakshy
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees
Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi