Papers
Stochastic and Adversarial Online Learning without Hyperparameters
Ashok Cutkosky, Kwabena A. Boahen
Convolutional Phase Retrieval
Qing Qu, Yuqian Zhang, Yonina Eldar et al.
Learning Overcomplete HMMs
Vatsal Sharan, Sham M. Kakade, Percy Liang et al.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed M. Alaa, Mihaela van der Schaar
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Ziming Zhang, Matthew Brand
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Cameron Musco, David Woodruff
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Liangpeng Zhang, Ke Tang, Xin Yao
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, YI WU, Aviv Tamar et al.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen, Harri Valpola
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.