Papers
4,122 papers found
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
Nikita Zozoulenko, Thomas Cass, Lukas Gonon
Instability, Computational Efficiency and Statistical Accuracy
Nhat Ho, Koulik Khamaru, Raaz Dwivedi et al.
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
Seunghoon Paik, Michael Celentano, Alden Green et al.
Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data
Thomas Chen, Patrícia Muñoz Ewald
Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds
Nathanaël Munier, Emmanuel Soubies, Pierre Weiss
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang, Xin Guo, Shao-Bo Lin
Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method
Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton et al.
Latent Process Models for Functional Network Data
Peter W. MacDonald, Elizaveta Levina, Ji Zhu
Learning causal graphs via nonlinear sufficient dimension reduction
Eftychia Solea, Bing Li, Kyongwon Kim
Learning conditional distributions on continuous spaces
Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal
Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
Ye Tian, Yuqi Gu, Yang Feng
Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play
Zelai Xu, Chao Yu, Yancheng Liang et al.
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
Michael Sucker, Jalal Fadili, Peter Ochs
Learning with Linear Function Approximations in Mean-Field Control
Erhan Bayraktar, Ali Devran Kara
Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound
Bo Xue, Ji Cheng, Fei Liu et al.
Lightning UQ Box: Uncertainty Quantification for Neural Networks
Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski et al.
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas
Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors
Gaoyu Wu, Jelena Bradic, Kean Ming Tan et al.
Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang et al.
Locally Private Causal Inference for Randomized Experiments
Yuki Ohnishi, Jordan Awan
Losing Momentum in Continuous-time Stochastic Optimisation
Kexin Jin, Jonas Latz, Chenguang Liu et al.
Manifold Fitting under Unbounded Noise
Zhigang Yao, Yuqing Xia