Papers
8,340 papers found
The Arrow of Time in Multivariate Time Series
Stefan Bauer, Bernhard Schölkopf, Jonas Peters
The Information Sieve
Greg Ver Steeg, Aram Galstyan
The Information-Theoretic Requirements of Subspace Clustering with Missing Data
Daniel Pimentel-Alarcon, Robert Nowak
The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai, Rina Barber
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang, Chu Wang, Warren Powell
The Label Complexity of Mixed-Initiative Classifier Training
Jina Suh, Xiaojin Zhu, Saleema Amershi
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson et al.
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram Friesen, Pedro Domingos
The Teaching Dimension of Linear Learners
Ji Liu, Xiaojin Zhu, Hrag Ohannessian
The Variational Nystrom method for large-scale spectral problems
Max Vladymyrov, Miguel Carreira-Perpinan
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Huan Gui, Jiawei Han, Quanquan Gu
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Tianbao Yang, Lijun Zhang, Rong Jin et al.
Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller et al.
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, Yoram Singer
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander Schwing, Richard et al.
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu et al.
Truthful Univariate Estimators
Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Wenling Shang, Kihyuk Sohn, Diogo Almeida et al.
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi
Uprooting and Rerooting Graphical Models
Adrian Weller
Variable Elimination in the Fourier Domain
Yexiang Xue, Stefano Ermon, Ronan Le Bras et al.
Variance-Reduced and Projection-Free Stochastic Optimization
Elad Hazan, Haipeng Luo
Variance Reduction for Faster Non-Convex Optimization
Zeyuan Allen-Zhu, Elad Hazan
Variational Inference for Monte Carlo Objectives
Andriy Mnih, Danilo Rezende