Papers
The Base Measure Problem and its Solution
Alexey Radul, Boris Alexeev
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
Fergus Simpson, Alexis Boukouvalas, Vaclav Cadek et al.
The Multiple Instance Learning Gaussian Process Probit Model
Fulton Wang, Ali Pinar
The Sample Complexity of Level Set Approximation
François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz
The Sample Complexity of Meta Sparse Regression
Zhanyu Wang, Jean Honorio
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry
Tomohiro Hayase, Ryo Karakida
The Teaching Dimension of Kernel Perceptron
Akash Kumar, Hanqi Zhang, Adish Singla et al.
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers
Mohamed El Amine Seddik, Cosme Louart, Romain COUILLET et al.
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Antti Koskela, Joonas Jälkö, Lukas Prediger et al.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Yingkai Li, Yining Wang, Xi Chen et al.
Top-m identification for linear bandits
Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries
Steve Hanneke, Liu Yang
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
Alexander Camuto, Matthew Willetts, Stephen Roberts et al.
Towards Flexible Device Participation in Federated Learning
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang et al.
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li et al.
Tracking Regret Bounds for Online Submodular Optimization
Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka
Tractable contextual bandits beyond realizability
Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey
Training a Single Bandit Arm
Eren Ozbay, Vijay Kamble
Transforming Gaussian Processes With Normalizing Flows
Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch et al.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang et al.
Understanding Gradient Clipping In Incremental Gradient Methods
Jiang Qian, Yuren Wu, Bojin Zhuang et al.
Understanding Robustness in Teacher-Student Setting: A New Perspective
Zhuolin Yang, Zhaoxi Chen, Tiffany Cai et al.
Understanding the wiring evolution in differentiable neural architecture search
Sirui Xie, Shoukang Hu, Xinjiang Wang et al.