Papers
4,025 papers found
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Calvin McCarter, Seyoung Kim
Latent Point Process Allocation
Chris Lloyd, Tom Gunter, Michael Osborne et al.
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
Sebastian Tschiatschek, Josip Djolonga, Andreas Krause
Learning Relationships between Data Obtained Independently
Alexandra Carpentier, Teresa Schlueter
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
Zhao Song, Ricardo Henao, David Carlson et al.
Learning Sparse Additive Models with Interactions in High Dimensions
Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner et al.
Learning Structured Low-Rank Representation via Matrix Factorization
Jie Shen, Ping Li
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices
Jonathan Scarlett, Volkan Cevher
Loss Bounds and Time Complexity for Speed Priors
Daniel Filan, Jan Leike, Marcus Hutter
Low-Rank and Sparse Structure Pursuit via Alternating Minimization
Quanquan Gu, Zhaoran Wang Wang, Han Liu
Low-Rank Approximation of Weighted Tree Automata
Guillaume Rabusseau, Borja Balle, Shay Cohen
Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models
Lee H. Dicker, Murat A. Erdogdu
Model-based Co-clustering for High Dimensional Sparse Data
Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Multi-Level Cause-Effect Systems
Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona
Multiresolution Matrix Compression
Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor
Nearly Optimal Classification for Semimetrics
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
New Resistance Distances with Global Information on Large Graphs
Canh Hao Nguyen, Hiroshi Mamitsuka
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information
Changwei Hu, Piyush Rai, Lawrence Carin
Nonparametric Budgeted Stochastic Gradient Descent
Trung Le, Vu Nguyen, Tu Dinh Nguyen et al.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu et al.
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Kevin Jamieson, Ameet Talwalkar
No Regret Bound for Extreme Bandits
Robert Nishihara, David Lopez-Paz, Leon Bottou
NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning
Eli Meirom, Pavel Kisilev