Papers
Tight Regret Bounds for Bayesian Optimization in One Dimension
Jonathan Scarlett
Time Limits in Reinforcement Learning
Fabio Pardo, Arash Tavakoli, Vitaly Levdik et al.
Topological mixture estimation
Steve Huntsman
To Understand Deep Learning We Need to Understand Kernel Learning
Mikhail Belkin, Siyuan Ma, Soumik Mandal
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li, Di He, Fei Tian et al.
Towards Black-box Iterative Machine Teaching
Weiyang Liu, Bo Dai, Xingguo Li et al.
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao et al.
Towards Fast Computation of Certified Robustness for ReLU Networks
Lily Weng, Huan Zhang, Hongge Chen et al.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou et al.
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
Aviral Kumar, Sunita Sarawagi, Ujjwal Jain
Training Neural Machines with Trace-Based Supervision
Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic et al.
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Andre Barreto, Diana Borsa, John Quan et al.
Transfer Learning via Learning to Transfer
Ying WEI, Yu Zhang, Junzhou Huang et al.
Transformation Autoregressive Networks
Junier Oliva, Avinava Dubey, Manzil Zaheer et al.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli et al.
Tropical Geometry of Deep Neural Networks
Liwen Zhang, Gregory Naitzat, Lek-Heng Lim
Unbiased Objective Estimation in Predictive Optimization
Shinji Ito, Akihiro Yabe, Ryohei Fujimaki
Understanding and Simplifying One-Shot Architecture Search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph et al.
Understanding Generalization and Optimization Performance of Deep CNNs
Pan Zhou, Jiashi Feng
Understanding the Loss Surface of Neural Networks for Binary Classification
SHIYU LIANG, Ruoyu Sun, Yixuan Li et al.
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Aravind Srinivas, Allan Jabri, Pieter Abbeel et al.
Using Inherent Structures to design Lean 2-layer RBMs
Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano et al.