Papers
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Michela Meister, Tamas Sarlos, David Woodruff
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
PHUONG_HA NGUYEN, Lam Nguyen, Marten van Dijk
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh et al.
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Yuan Cao, Quanquan Gu
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Surbhi Goel, Sushrut Karmalkar, Adam Klivans
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Aditya Sharad Golatkar, Alessandro Achille, Stefano Soatto
Time-series Generative Adversarial Networks
Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar
Topology-Preserving Deep Image Segmentation
Xiaoling Hu, Fuxin Li, Dimitris Samaras et al.
Total Least Squares Regression in Input Sparsity Time
Huaian Diao, Zhao Song, David Woodruff et al.
Toward a Characterization of Loss Functions for Distribution Learning
Nika Haghtalab, Cameron Musco, Bo Waggoner
Towards Automatic Concept-based Explanations
Amirata Ghorbani, James Wexler, James Y Zou et al.
Towards a Zero-One Law for Column Subset Selection
Zhao Song, David Woodruff, Peilin Zhong
Towards closing the gap between the theory and practice of SVRG
Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi et al.
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
Yuanzhi Li, Colin Wei, Tengyu Ma
Towards Hardware-Aware Tractable Learning of Probabilistic Models
Laura I Galindez Olascoaga, Wannes Meert, Nimish Shah et al.
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Alexander Mott, Daniel Zoran, Mike Chrzanowski et al.
Towards modular and programmable architecture search
Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon et al.
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Towards Practical Alternating Least-Squares for CCA
Zhiqiang Xu, Ping Li
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Tianyi Liu, Minshuo Chen, Mo Zhou et al.
Training Image Estimators without Image Ground Truth
Zhihao Xia, Ayan Chakrabarti
Training Language GANs from Scratch
Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca et al.
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Clarice Poon, Jingwei Liang
Transductive Zero-Shot Learning with Visual Structure Constraint
Ziyu Wan, Dongdong Chen, Yan Li et al.
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Ximei Wang, Ying Jin, Mingsheng Long et al.