Papers
Temporal alignment and latent Gaussian process factor inference in population spike trains
Lea Duncker, Maneesh Sahani
Temporal Regularization for Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau et al.
Testing for Families of Distributions via the Fourier Transform
Clément L Canonne, Ilias Diakonikolas, Alistair Stewart
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Yu Ji, Ling Liang, Lei Deng et al.
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
Seonghyeon Nam, Yunji Kim, Seon Joo Kim
The challenge of realistic music generation: modelling raw audio at scale
Sander Dieleman, Aaron van den Oord, Karen Simonyan
The Cluster Description Problem - Complexity Results, Formulations and Approximations
Ian Davidson, Antoine Gourru, S Ravi
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Benjamin Aubin, Antoine Maillard, jean barbier et al.
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson et al.
The Description Length of Deep Learning models
Léonard Blier, Yann Ollivier
The Effect of Network Width on the Performance of Large-batch Training
Lingjiao Chen, Hongyi Wang, Jinman Zhao et al.
The emergence of multiple retinal cell types through efficient coding of natural movies
Samuel Ocko, Jack Lindsey, Surya Ganguli et al.
The Everlasting Database: Statistical Validity at a Fair Price
Blake E Woodworth, Vitaly Feldman, Saharon Rosset et al.
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
Zi Yin, Vin Sachidananda, Balaji Prabhakar
The Importance of Sampling inMeta-Reinforcement Learning
Bradly Stadie, Ge Yang, Rein Houthooft et al.
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
Constantinos Daskalakis, Ioannis Panageas
The Limits of Post-Selection Generalization
Jonathan Ullman, Adam Smith, Kobbi Nissim et al.
The Lingering of Gradients: How to Reuse Gradients Over Time
Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
Jiantao Jiao, Weihao Gao, Yanjun Han
Theoretical guarantees for EM under misspecified Gaussian mixture models
Raaz Dwivedi, nhật Hồ, Koulik Khamaru et al.
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang et al.
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse Krijthe, Marco Loog
The Physical Systems Behind Optimization Algorithms
Lin Yang, Raman Arora, Vladimir braverman et al.
The Price of Fair PCA: One Extra dimension
Samira Samadi, Uthaipon Tantipongpipat, Jamie H Morgenstern et al.
The Price of Privacy for Low-rank Factorization
Jalaj Upadhyay