Papers
Inference Compilation and Universal Probabilistic Programming
Tuan Anh Le, Atilim Gunes Baydin, Frank Wood
Information Projection and Approximate Inference for Structured Sparse Variables
Rajiv Khanna, Joydeep Ghosh, Rusell Poldrack et al.
Information-theoretic limits of Bayesian network structure learning
Asish Ghoshal, Jean Honorio
Initialization and Coordinate Optimization for Multi-way Matching
Da Tang, Tony Jebara
Label Filters for Large Scale Multilabel Classification
Alexandru Niculescu-Mizil, Ehsan Abbasnejad
Large-Scale Data-Dependent Kernel Approximation
Catalin Ionescu, Alin Popa, Cristian Sminchisescu
Learning Cost-Effective and Interpretable Treatment Regimes
Himabindu Lakkaraju, Cynthia Rudin
Learning from Conditional Distributions via Dual Embeddings
Bo Dai, Niao He, Yunpeng Pan et al.
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions
Asish Ghoshal, Jean Honorio
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
Julien Perolat, Florian Strub, Bilal Piot et al.
Learning Nonparametric Forest Graphical Models with Prior Information
Yuancheng Zhu, Zhe Liu, Siqi Sun
Learning Optimal Interventions
Jonas Mueller, David Reshef, George Du et al.
Learning Structured Weight Uncertainty in Bayesian Neural Networks
Shengyang Sun, Changyou Chen, Lawrence Carin
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields
Youngsuk Park, David Hallac, Stephen Boyd et al.
Learning Theory for Conditional Risk Minimization
Alexander Zimin, Christoph Lampert
Learning Time Series Detection Models from Temporally Imprecise Labels
Roy Adams, Ben Marlin
Learning with Feature Feedback: from Theory to Practice
Stefanos Poulis, Sanjoy Dasgupta
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds
Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai et al.
Less than a Single Pass: Stochastically Controlled Stochastic Gradient
Lihua Lei, Michael Jordan
Linear Convergence of Stochastic Frank Wolfe Variants
Donald Goldfarb, Garud Iyengar, Chaoxu Zhou
Linear Thompson Sampling Revisited
Marc Abeille, Alessandro Lazaric
Linking Micro Event History to Macro Prediction in Point Process Models
Yichen Wang, Xiaojing Ye, Haomin Zhou et al.
Local Group Invariant Representations via Orbit Embeddings
Anant Raj, Abhishek Kumar, Youssef Mroueh et al.
Localized Lasso for High-Dimensional Regression
Makoto Yamada, Takeuchi Koh, Tomoharu Iwata et al.