Papers
4,025 papers found
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
Yuxin Chen, Hamed Hassani, Andreas Krause
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Ankit Anand, Ritesh Noothigattu, Parag Singla et al.
Nonlinear ICA of Temporally Dependent Stationary Sources
Aapo Hyvarinen, Hiroshi Morioka
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
Dohyung Park, Anastasios Kyrillidis, Constantine Carmanis et al.
Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates
Joon Kwon, Vianney Perchet
Online Nonnegative Matrix Factorization with General Divergences
Renbo Zhao, Vincent Tan, Huan Xu
Online Optimization of Smoothed Piecewise Constant Functions
Vincent Cohen-Addad, Varun Kanade
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior
Juho Piironen, Aki Vehtari
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting
Yihan Gao, Aditya Parameswaran, Jian Peng
On the Learnability of Fully-Connected Neural Networks
Yuchen Zhang, Jason Lee, Martin Wainwright et al.
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
Géraud Le Falher, Nicolo Cesa-Bianchi, Claudio Gentile et al.
Optimal Recovery of Tensor Slices
Vivek Farias, Andrew Li
Optimistic Planning for the Stochastic Knapsack Problem
Ciara Pike-Burke, Steffen Grunewalder
Performance Bounds for Graphical Record Linkage
Rebecca C. Steorts, Mattew Barnes, Willie Neiswanger
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
Sohail Bahmani, Justin Romberg
Poisson intensity estimation with reproducing kernels
Seth Flaxman, Yee Whye Teh, Dino Sejdinovic
Prediction Performance After Learning in Gaussian Process Regression
Johan Wagberg, Dave Zachariah, Thomas Schon et al.
Quantifying the accuracy of approximate diffusions and Markov chains
Jonathan Huggins, James Zou
Random Consensus Robust PCA
Daniel Pimentel-Alarcon, Robert Nowak
Random projection design for scalable implicit smoothing of randomly observed stochastic processes
Francois Belletti, Evan Sparks, Alexandre Bayen et al.
Rank Aggregation and Prediction with Item Features
Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
Sejun Park, Yunhun Jang, Andreas Galanis et al.
Regression Uncertainty on the Grassmannian
Yi Hong, Xiao Yang, Roland Kwitt et al.
Regret Bounds for Lifelong Learning
Pierre Alquier, The Tien Mai, Massimiliano Pontil
Regret Bounds for Transfer Learning in Bayesian Optimisation
Alistair Shilton, Sunil Gupta, Santu Rana et al.