Papers
The ensmallen library for flexible numerical optimization
Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu et al.
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
Thompson Sampling Algorithms for Cascading Bandits
Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan
Tighter Risk Certificates for Neural Networks
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor et al.
Towards a Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic et al.
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
James-A. Goulet, Luong Ha Nguyen, Saeid Amiri
Transferability of Spectral Graph Convolutional Neural Networks
Ron Levie, Wei Huang, Lorenzo Bucci et al.
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization
Yingfan Wang, Haiyang Huang, Cynthia Rudin et al.
Unfolding-Model-Based Visualization: Theory, Method and Applications
Yunxiao Chen, Zhiliang Ying, Haoran Zhang
Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces
László Györfi, Roi Weiss
Unlinked Monotone Regression
Fadoua Balabdaoui, Charles R. Doss, Cécile Durot
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao et al.
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Sebastian Schulze, Cong Lu et al.
V-statistics and Variance Estimation
Zhengze Zhou, Lucas Mentch, Giles Hooker
Wasserstein barycenters can be computed in polynomial time in fixed dimension
Jason M Altschuler, Enric Boix-Adsera
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis
What Causes the Test Error? Going Beyond Bias-Variance via ANOVA
Licong Lin, Edgar Dobriban
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
When random initializations help: a study of variational inference for community detection
Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee
(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin et al.
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Martin Takac et al.
A Convex Parametrization of a New Class of Universal Kernel Functions
Brendon K. Colbert, Matthew M. Peet
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
Rachel Ward, Xiaoxia Wu, Leon Bottou