Papers
The Impact of Regularization on High-dimensional Logistic Regression
Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
The Implicit Bias of AdaGrad on Separable Data
Qian Qian, Xiaoyuan Qian
The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry P Vetrov
The Label Complexity of Active Learning from Observational Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Shuang Li, Gongguo Tang, Michael B Wakin
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
The Option Keyboard: Combining Skills in Reinforcement Learning
Andre Barreto, Diana Borsa, Shaobo Hou et al.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
Theoretical evidence for adversarial robustness through randomization
Rafael Pinot, Laurent Meunier, Alexandre Araujo et al.
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic DOS SANTOS, Kevin Scaman
The Parameterized Complexity of Cascading Portfolio Scheduling
Eduard Eiben, Robert Ganian, Iyad Kanj et al.
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
Vladimir V. Kniaz, Vladimir Knyaz, Fabio Remondino
The Randomized Midpoint Method for Log-Concave Sampling
Ruoqi Shen, Yin Tat Lee
The spiked matrix model with generative priors
Benjamin Aubin, Bruno Loureiro, Antoine Maillard et al.
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Rong Ge, Sham M. Kakade, Rahul Kidambi et al.
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
Arash Ardakani, Zhengyun Ji, Amir Ardakani et al.
The Thermodynamic Variational Objective
Vaden Masrani, Tuan Anh Le, Frank Wood
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Rajat Sen, Hsiang-Fu Yu, Inderjit S Dhillon
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
Pooria Joulani, András György, Csaba Szepesvari
Thinning for Accelerating the Learning of Point Processes
Tianbo Li, Yiping Ke
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
Pratyusha Sharma, Deepak Pathak, Abhinav Gupta
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Daniel Tao et al.
Thompson Sampling and Approximate Inference
My Phan, Yasin Abbasi Yadkori, Justin Domke
Thompson Sampling for Multinomial Logit Contextual Bandits
Min-hwan Oh, Garud Iyengar
Thompson Sampling with Information Relaxation Penalties
Seungki Min, Costis Maglaras, Ciamac C. Moallemi