Papers
Stable ResNet
Soufiane Hayou, Eugenio Clerico, Bobby He et al.
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference
Sean Plummer, Shuang Zhou, Anirban Bhattacharya et al.
Stochastic Bandits with Linear Constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett et al.
Stochastic Linear Bandits Robust to Adversarial Attacks
Ilija Bogunovic, Arpan Losalka, Andreas Krause et al.
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji et al.
Taming heavy-tailed features by shrinkage
Ziwei Zhu, Wenjing Zhou
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun Ding, Ziyang Wu et al.
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller, Guillaume Rabusseau, John Terilla
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.