Papers
Testing and Learning on Distributions with Symmetric Noise Invariance
Ho Chung Law, Christopher Yau, Dino Sejdinovic
Attentional Pooling for Action Recognition
Rohit Girdhar, Deva Ramanan
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Zhaohan Guo, Philip S. Thomas, Emma Brunskill
Straggler Mitigation in Distributed Optimization Through Data Encoding
Can Karakus, Yifan Sun, Suhas Diggavi et al.
Visual Reference Resolution using Attention Memory for Visual Dialog
Paul Hongsuck Seo, Andreas Lehrmann, Bohyung Han et al.
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew G Wilson et al.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Hongyuan Mei, Jason M Eisner
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke, Qi Meng, Thomas Finley et al.
Acceleration and Averaging in Stochastic Descent Dynamics
Walid Krichene, Peter L Bartlett
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Arturs Backurs, Piotr Indyk, Ludwig Schmidt
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello, Michael Tschannen, Gunnar Raetsch et al.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Karol Hausman, Yevgen Chebotar, Stefan Schaal et al.
Gaussian Quadrature for Kernel Features
Tri Dao, Christopher M De Sa, Christopher Ré
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
Søren Dahlgaard, Mathias Knudsen, Mikkel Thorup
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash et al.
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi et al.
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun et al.
Generalization Properties of Learning with Random Features
Alessandro Rudi, Lorenzo Rosasco
Improved Graph Laplacian via Geometric Self-Consistency
Dominique Joncas, Marina Meila, James McQueen
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Zahra Ghodsi, Tianyu Gu, Siddharth Garg
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Ingmar Kanitscheider, Ila Fiete
Coded Distributed Computing for Inverse Problems
Yaoqing Yang, Pulkit Grover, Soummya Kar
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Rowan McAllister, Carl Edward Rasmussen
Inductive Representation Learning on Large Graphs
Will Hamilton, Zhitao Ying, Jure Leskovec