Papers
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
Zhiheng Chen, Guanhua Fang, Wen Yu
Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation
Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen
Universal Online Convex Optimization Meets Second-order Bounds
Lijun Zhang, Yibo Wang, Guanghui Wang et al.
Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu, Rahul Mazumder
skglm: Improving scikit-learn for Regularized Generalized Linear Models
Badr Moufad, Pierre-Antoine Bannier, Quentin Bertrand et al.
Losing Momentum in Continuous-time Stochastic Optimisation
Kexin Jin, Jonas Latz, Chenguang Liu et al.
Latent Process Models for Functional Network Data
Peter W. MacDonald, Elizaveta Levina, Ji Zhu
Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.
On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
Andrea Perin, Stephane Deny
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Michal Dereziński, Daniel LeJeune, Deanna Needell et al.
Deep Generative Models: Complexity, Dimensionality, and Approximation
Kevin Wang, Hongqian Niu, Yixin Wang et al.
ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam et al.
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl et al.
Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses
Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini
Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space
Romain Verdière, Clémentine Prieur, Olivier Zahm
Finite Expression Method for Solving High-Dimensional Partial Differential Equations
Senwei Liang, Haizhao Yang
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
Ziwen Wang, Yancheng Yuan, Jiaming Ma et al.
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu, Ruiqi Liu, Zuofeng Shang et al.
Optimal and Efficient Algorithms for Decentralized Online Convex Optimization
Yuanyu Wan, Tong Wei, Bo Xue et al.
Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning
Dennis Chemnitz, Maximilian Engel
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska et al.
Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
Céline Comte, Matthieu Jonckheere, Jaron Sanders et al.
On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm
Huan Li, Yiming Dong, Zhouchen Lin
Categorical Semantics of Compositional Reinforcement Learning
Georgios Bakirtzis, Michail Savvas, Ufuk Topcu