Papers

• Quantitative Gaussian-Process limits of Tensor Programs (2026)
  Andrea Agazzi et al.
  4.39
• On the Order-Conditional Optimality of Gaffke's Bound (2026)
  George Bissias et al.
  4.39
• Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning (2025)
  Amirhossein Mollaali et al.
  4.36
• Thompson Sampling Is 2-Competitive for Mistakes (2026)
  Mark Sellke et al.
  3.51
• Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics (2026)
  Vignesh Tirukkonda et al.
  3.51
• The Tractability Landscape of Sampling with Inexact Scores (2026)
  Anming Gu et al.
  3.51
• Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory (2025)
  Runtong Pan et al.
  2.87
• Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance (2026)
  Pei-Hsuan Hsia et al.
  2.00
• Variance-Reduced Conditional Gradient Methods under Markovian Sampling for Nonconvex Composite Optimization (2026)
  Zhaojun Peng
  2.00
• Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields? (2026)
  Daniel Kua et al.
  2.00
• Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations (2026)
  Artur Miroszewski
  1.89
• Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model (2025)
  Jeffrey van der Voort et al.
  1.61
• A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods (2025)
  Somjit Roy et al.
  1.17
• Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes (2024)
  Jeremiah Hauth et al.
  0.44
• Generalization Bounds for Sparse Random Feature Expansions (2021)
  Abolfazl Hashemi et al.
  0.00
• Generative models and Bayesian inversion using Laplace approximation (2022)
  Manuel Marschall et al.
  —
• A Riemannian Stochastic Representation for Quantifying Model Uncertainties in Molecular Dynamics Simulations (2022)
  Hao Zhang and Johann Guilleminot
  —