Papers
Neural Dynamic Programming for Musical Self Similarity
Christian Walder, Dongwoo Kim
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan et al.
Transfer Learning via Learning to Transfer
Ying WEI, Yu Zhang, Junzhou Huang et al.
Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Robin Vogel, Aurélien Bellet, Stéphan Clémençon
Clustering Semi-Random Mixtures of Gaussians
Aravindan Vijayaraghavan, Pranjal Awasthi
Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh et al.
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Arash Vahdat, William Macready, Zhengbing Bian et al.
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli et al.
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu et al.
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Stephen Tu, Benjamin Recht
Invariance of Weight Distributions in Rectified MLPs
Russell Tsuchida, Fred Roosta, Marcus Gallagher
StrassenNets: Deep Learning with a Multiplication Budget
Michael Tschannen, Aran Khanna, Animashree Anandkumar
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Manolis Tsakiris, Rene Vidal
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu Trinh, Andrew Dai, Thang Luong et al.
Convergent Tree Backup and Retrace with Function Approximation
Ahmed Touati, Pierre-Luc Bacon, Doina Precup et al.
Adversarial Regression with Multiple Learners
Liang Tong, Sixie Yu, Scott Alfeld et al.
Importance Weighted Transfer of Samples in Reinforcement Learning
Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta et al.
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian, Teng Zhang, James Zou
Decoupling Gradient-Like Learning Rules from Representations
Philip Thomas, Christoph Dann, Emma Brunskill
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Mattias Teye, Hossein Azizpour, Kevin Smith
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Adrien Taylor, Bryan Van Scoy, Laurent Lessard
Chi-square Generative Adversarial Network
Chenyang Tao, Liqun Chen, Ricardo Henao et al.
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Chao Tao, Saúl Blanco, Yuan Zhou
Black Box FDR
Wesley Tansey, Yixin Wang, David Blei et al.