Papers
8,340 papers found
Online Learning under Delayed Feedback
Pooria Joulani, Andras Gyorgy, Csaba Szepesvari
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Purushottam Kar, Bharath Sriperumbudur, Prateek Jain et al.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl et al.
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance
Aditya Menon, Harikrishna Narasimhan, Shivani Agarwal et al.
Optimal rates for stochastic convex optimization under Tsybakov noise condition
Aaditya Ramdas, Aarti Singh
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning
Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner et al.
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing
Xi Chen, Qihang Lin, Dengyong Zhou
Optimization with First-Order Surrogate Functions
Julien Mairal
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization
Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski et al.
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models
Sinead Williamson, Avinava Dubey, Eric Xing
Parameter Learning and Convergent Inference for Dense Random Fields
Philipp Kraehenbuehl, Vladlen Koltun
Parsing epileptic events using a Markov switching process model for correlated time series
Drausin Wulsin, Emily Fox, Brian Litt
Planning by Prioritized Sweeping with Small Backups
Harm Van Seijen, Rich Sutton
Precision-recall space to correct external indices for biclustering
Blaise Hanczar, Mohamed Nadif
Predictable Dual-View Hashing
Mohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei et al.
Principal Component Analysis on non-Gaussian Dependent Data
Fang Han, Han Liu
\proptoSVM for Learning with Label Proportions
Felix Yu, Dong Liu, Sanjiv Kumar et al.
Quantile Regression for Large-scale Applications
Jiyan Yang, Xiangrui Meng, Michael Mahoney
Quickly Boosting Decision Trees – Pruning Underachieving Features Early
Ron Appel, Thomas Fuchs, Piotr Dollar et al.
Regularization of Neural Networks using DropConnect
Li Wan, Matthew Zeiler, Sixin Zhang et al.
Revisiting the Nystrom method for improved large-scale machine learning
Alex Gittens, Michael Mahoney
Riemannian Similarity Learning
Li Cheng
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction
Sébastien Giguère, François Laviolette, Mario Marchand et al.