Papers
LDA Topic Model with Soft Assignment of Descriptors to Words
Daphna Weinshall, Gal Levi, Dmitri Hanukaev
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines
Kihyuk Sohn, Guanyu Zhou, Chansoo Lee et al.
Learning an Internal Dynamics Model from Control Demonstration
Matthew Golub, Steven Chase, Byron Yu
Learning Connections in Financial Time Series
Gartheeban Ganeshapillai, John Guttag, Andrew Lo
Learning Convex QP Relaxations for Structured Prediction
Jeremy Jancsary, Sebastian Nowozin, Carsten Rother
Learning Fair Representations
Rich Zemel, Yu Wu, Kevin Swersky et al.
Learning from Human-Generated Lists
Kwang-Sung Jun, Jerry Zhu, Burr Settles et al.
Learning Hash Functions Using Column Generation
Xi Li, Guosheng Lin, Chunhua Shen et al.
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Arnak Dalalyan, Mohamed Hebiri, Katia Meziani et al.
Learning invariant features by harnessing the aperture problem
Roland Memisevic, Georgios Exarchakis
Learning Linear Bayesian Networks with Latent Variables
Animashree Anandkumar, Daniel Hsu, Adel Javanmard et al.
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space
Javier Almingol, Lui Montesano, Manuel Lopes
Learning Optimally Sparse Support Vector Machines
Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro
Learning Policies for Contextual Submodular Prediction
Stephane Ross, Jiaji Zhou, Yisong Yue et al.
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression
Toby Hocking, Guillem Rigaill, Jean-Philippe Vert et al.
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation
Hema Koppula, Ashutosh Saxena
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models
Umut Simsekli, Ali Taylan Cemgil, Yusuf Kenan Yilmaz
Learning the Structure of Sum-Product Networks
Robert Gens, Domingos Pedro
Learning Triggering Kernels for Multi-dimensional Hawkes Processes
Ke Zhou, Hongyuan Zha, Le Song
Learning with Marginalized Corrupted Features
Laurens Maaten, Minmin Chen, Stephen Tyree et al.
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
Cijo Jose, Prasoon Goyal, Parv Aggrwal et al.
Local Low-Rank Matrix Approximation
Joonseok Lee, Seungyeon Kim, Guy Lebanon et al.
Loss-Proportional Subsampling for Subsequent ERM
Paul Mineiro, Nikos Karampatziakis
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
Tamara Broderick, Brian Kulis, Michael Jordan
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
James Bergstra, Daniel Yamins, David Cox