Papers
Graph Sparsification Approaches for Laplacian Smoothing
Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
Chun-Liang Li, Kirthevasan Kandasamy, Barnabas Poczos et al.
How to Learn a Graph from Smooth Signals
Vassilis Kalofolias
Improper Deep Kernels
Uri Heinemann, Roi Livni, Elad Eban et al.
Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh et al.
Inference for High-dimensional Exponential Family Graphical Models
Jialei Wang, Mladen Kolar
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Michael Herman, Tobias Gindele, Jörg Wagner et al.
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation
Sujith Ravi, Qiming Diao
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