Papers
On Markov chain Monte Carlo methods for tall data
Rémi Bardenet, Arnaud Doucet, Chris Holmes
On Minimum Representations of Matched Formulas (Extended Abstract)
Ondřej Čepek, Štefan Gurský, Petr Kučera
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
On Modeling Sense Relatedness in Multi-prototype Word Embedding
Yixin Cao, Jiaxin Shi, Juanzi Li et al.
On Multi-Domain Training and Adaptation of End-to-End RNN Acoustic Models for Distant Speech Recognition
Seyedmahdad Mirsamadi, John H.L. Hansen
On Neighborhood Singleton Consistencies
Anastasia Paparrizou, Kostas Stergiou
On Optimal Generalizability in Parametric Learning
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour et al.
On orthogonality and learning recurrent networks with long term dependencies
Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury et al.
On Perturbed Proximal Gradient Algorithms
Yves F. Atchadé, Gersende Fort, Eric Moulines
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li, Lin Yang, Jason Ge et al.
On Querying Incomplete Information in Databases under Bag Semantics
Marco Console, Paolo Guagliardo, Leonid Libkin
On Redundant Topological Constraints (Extended Abstract)
Sanjiang Li, Zhiguo Long, Weiming Liu et al.
On Relaxing Determinism in Arithmetic Circuits
Arthur Choi, Adnan Darwiche
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K Ravikumar
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin, Francis Bach, Simon Lacoste-Julien
On Subset Selection with General Cost Constraints
Chao Qian, Jing-Cheng Shi, Yang Yu et al.
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
On the Ability of Neural Nets to Express Distributions
Holden Lee, Rong Ge, Tengyu Ma et al.
On the Complexity of Enumerating the Extensions of Abstract Argumentation Frameworks
Markus Kröll, Reinhard Pichler, Stefan Woltran
On the Complexity of Learning from Label Proportions
Benjamin Fish, Lev Reyzin
On the Complexity of Learning Neural Networks
Le Song, Santosh Vempala, John Wilmes et al.
On the Computational Complexity of Gossip Protocols
Krzysztof R. Apt, Eryk Kopczyński, Dominik Wojtczak
On the Consistency of Ordinal Regression Methods
Fabian Pedregosa, Francis Bach, Alexandre Gramfort