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Papers

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2016 NIPS
The Forget-me-not Process
Kieran Milan, Joel Veness, James Kirkpatrick et al.
2016 NIPS
The Generalized Reparameterization Gradient
Francisco R Ruiz, Michalis Titsias RC AUEB, David Blei
2016 NIPS
The Limits of Learning with Missing Data
Brian Bullins, Elad Hazan, Tomer Koren
2016 NIPS
The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos et al.
2016 NIPS
The Multiple Quantile Graphical Model
Alnur Ali, J. Zico Kolter, Ryan J Tibshirani
2016 NIPS
The Multiscale Laplacian Graph Kernel
Risi Kondor, Horace Pan
2016 NIPS
The non-convex Burer-Monteiro approach works on smooth semidefinite programs
Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
2016 NIPS
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai et al.
2016 NIPS
The Power of Adaptivity in Identifying Statistical Alternatives
Kevin G. Jamieson, Daniel Haas, Benjamin Recht
2016 NIPS
The Power of Optimization from Samples
Eric Balkanski, Aviad Rubinstein, Yaron Singer
2016 NIPS
The Product Cut
Thomas Laurent, James von Brecht, Xavier Bresson et al.
2016 NIPS
The Robustness of Estimator Composition
Pingfan Tang, Jeff M Phillips
2016 NIPS
Threshold Bandits, With and Without Censored Feedback
Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
2016 NIPS
2016 NIPS
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J Tibshirani
2016 NIPS
Towards Conceptual Compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende et al.
2016 NIPS
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen et al.
2016 NIPS
2016 NIPS
2016 NIPS