Papers
4,025 papers found
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data
Abhimanu Kumar, Alex Beutel, Qirong Ho et al.
Fully-Automatic Bayesian Piecewise Sparse Linear Models
Riki Eto, Ryohei Fujimaki, Satoshi Morinaga et al.
FuSSO: Functional Shrinkage and Selection Operator
Junier Oliva, Barnabas Poczos, Timothy Verstynen et al.
Gaussian Copula Precision Estimation with Missing Values
Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee et al.
Generating Efficient MCMC Kernels from Probabilistic Programs
Lingfeng Yang, Patrick Hanrahan, Noah Goodman
Global Optimization Methods for Extended Fisher Discriminant Analysis
Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda
Heterogeneous Domain Adaptation for Multiple Classes
Joey Tianyi Zhou, Ivor W.Tsang, Sinno Jialin Pan et al.
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation
Rafael Izbicki, Ann Lee, Chad Schafer
Hybrid Discriminative-Generative Approach with Gaussian Processes
Ricardo Andrade Pacheco, James Hensman, Max Zwiessele et al.
Incremental Tree-Based Inference with Dependent Normalized Random Measures
Juho Lee, Seungjin Choi
In Defense of Minhash over Simhash
Anshumali Shrivastava, Ping Li
Information-Theoretic Characterization of Sparse Recovery
Cem Aksoylar, Venkatesh Saligrama
Interpretable Sparse High-Order Boltzmann Machines
Martin Renqiang Min, Xia Ning, Chao Cheng et al.
Jointly Informative Feature Selection
Leonidas Lefakis, Francois Fleuret
Joint Structure Learning of Multiple Non-Exchangeable Networks
Chris Oates, Sach Mukherjee
LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series
Yubin Park, Carlos Carvalho, Joydeep Ghosh
Latent Gaussian Models for Topic Modeling
Changwei Hu, Eunsu Ryu, David Carlson et al.
Learning and Evaluation in Presence of Non-i.i.d. Label Noise
Nico Görnitz, Anne Porbadnigk, Alexander Binder et al.
Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming
Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren
Learning Heterogeneous Hidden Markov Random Fields
Jie Liu, Chunming Zhang, Elizabeth Burnside et al.
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability
Jeremias Berg, Matti Järvisalo, Brandon Malone
Learning Structured Models with the AUC Loss and Its Generalizations
Nir Rosenfeld, Ofer Meshi, Danny Tarlow et al.
Learning with Maximum A-Posteriori Perturbation Models
Andreea Gane, Tamir Hazan, Tommi Jaakkola
Lifted MAP Inference for Markov Logic Networks
Somdeb Sarkhel, Deepak Venugopal, Parag Singla et al.