- Vincent Cohen-Addad, Varun Kanade:
Online Optimization of Smoothed Piecewise Constant Functions. AISTATS 2017: 412-420 - Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Embeddings. AISTATS 2017: 1458-1467 - Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein:
Automated Inference with Adaptive Batches. AISTATS 2017: 1504-1513 - Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671 - Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan:
Scalable Learning of Non-Decomposable Objectives. AISTATS 2017: 832-840 - Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu:
Trading off Rewards and Errors in Multi-Armed Bandits. AISTATS 2017: 709-717 - Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes:
Encrypted Accelerated Least Squares Regression. AISTATS 2017: 334-343 - Francois Fagan, Jalaj Bhandari, John P. Cunningham:
Annular Augmentation Sampling. AISTATS 2017: 139-147 - Géraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale:
On the Troll-Trust Model for Edge Sign Prediction in Social Networks. AISTATS 2017: 402-411 - Amir Massoud Farahmand, André Barreto, Daniel Nikovski:
Value-Aware Loss Function for Model-based Reinforcement Learning. AISTATS 2017: 1486-1494 - Vivek F. Farias, Andrew A. Li:
Optimal Recovery of Tensor Slices. AISTATS 2017: 1394-1402 - Ian Fellows, Mark Handcock:
Removing Phase Transitions from Gibbs Measures. AISTATS 2017: 289-297 - Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic:
Poisson intensity estimation with reproducing kernels. AISTATS 2017: 270-279 - Ronan Fruit, Alessandro Lazaric:
Exploration-Exploitation in MDPs with Options. AISTATS 2017: 576-584 - Tianfan Fu, Zhihua Zhang:
CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC. AISTATS 2017: 841-850 - Pierre Gaillard, Olivier Wintenberger:
Sparse Accelerated Exponential Weights. AISTATS 2017: 75-82 - Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto:
Gray-box Inference for Structured Gaussian Process Models. AISTATS 2017: 353-361 - Yihan Gao, Aditya G. Parameswaran, Jian Peng:
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting. AISTATS 2017: 1367-1374 - Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse:
Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS 2017: 1311-1319 - Asish Ghoshal, Jean Honorio:
Information-theoretic limits of Bayesian network structure learning. AISTATS 2017: 767-775 - Asish Ghoshal, Jean Honorio:
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions. AISTATS 2017: 1532-1540 - Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. AISTATS 2017: 362-371 - Donald Goldfarb, Garud Iyengar, Chaoxu Zhou:
Linear Convergence of Stochastic Frank Wolfe Variants. AISTATS 2017: 1066-1074 - Matthew M. Graham, Amos J. Storkey:
Asymptotically exact inference in differentiable generative models. AISTATS 2017: 499-508 - Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison-Based Nearest Neighbor Search. AISTATS 2017: 851-859 - Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Unsupervised Sequential Sensor Acquisition. AISTATS 2017: 803-811 - Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi:
Consistent and Efficient Nonparametric Different-Feature Selection. AISTATS 2017: 130-138 - Dezhi Hong, Quanquan Gu, Kamin Whitehouse:
High-dimensional Time Series Clustering via Cross-Predictability. AISTATS 2017: 642-651 - Yi Hong, Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer:
Regression Uncertainty on the Grassmannian. AISTATS 2017: 785-793 - Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz:
ConvNets with Smooth Adaptive Activation Functions for Regression. AISTATS 2017: 430-439