Papers
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K. Ho, Robert Loftin et al.
iSurvive: An Interpretable, Event-time Prediction Model for mHealth
Walter H. Dempsey, Alexander Moreno, Christy K. Scott et al.
Iterative Machine Teaching
Weiyang Liu, Bo Dai, Ahmad Humayun et al.
Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Mehrtash Harandi, Mathieu Salzmann, Richard Hartley
Just Sort It! A Simple and Effective Approach to Active Preference Learning
Lucas Maystre, Matthias Grossglauser
Kernelized Support Tensor Machines
Lifang He, Chun-Ta Lu, Guixiang Ma et al.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang et al.
Language Modeling with Gated Convolutional Networks
Yann N. Dauphin, Angela Fan, Michael Auli et al.
Large-Scale Evolution of Image Classifiers
Esteban Real, Sherry Moore, Andrew Selle et al.
Latent Feature Lasso
Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang et al.
Latent Intention Dialogue Models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom et al.
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Manzil Zaheer, Amr Ahmed, Alexander J. Smola
Lazifying Conditional Gradient Algorithms
Gábor Braun, Sebastian Pokutta, Daniel Zink
Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman et al.
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
Learning Continuous Semantic Representations of Symbolic Expressions
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli et al.
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Matthew D. Hoffman
Learning Determinantal Point Processes with Moments and Cycles
John Urschel, Victor-Emmanuel Brunel, Ankur Moitra et al.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui et al.
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Ahmed M. Alaa, Scott Hu, Mihaela Schaar
Learning Gradient Descent: Better Generalization and Longer Horizons
Kaifeng Lv, Shunhua Jiang, Jian Li
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Hongteng Xu, Dixin Luo, Hongyuan Zha
Learning Hierarchical Features from Deep Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, Anshul Kundaje