v1v2 (latest)

On the marginal likelihood and cross-validation

21 May 2019

Edwin Fong

Chris Holmes

UQCV

ArXiv (abs)PDF HTML

Papers citing "On the marginal likelihood and cross-validation"

49 / 49 papers shown

Title
Distributional Training Data Attribution Bruno Mlodozeniec Isaac Reid Sam Power David M. Krueger Murat Erdogdu Richard E. Turner Roger B. Grosse TDI OOD 35 0 0 15 Jun 2025
Asymptotics for parametric martingale posteriors Edwin Fong Andrew Yiu 47 0 0 23 Oct 2024
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling Hyungi Lee G. Nam Edwin Fong Juho Lee BDL 75 6 0 12 Mar 2024
Bayesian Regression Markets Thomas Falconer J. Kazempour Pierre Pinson 136 3 0 23 Oct 2023
Spectral information criterion for automatic elbow detection Luca Martino Roberto San Millán-Castillo E. Morgado 70 9 0 17 Aug 2023
Universal and Automatic Elbow Detection for Learning the Effective Number of Components in Model Selection Problems E. Morgado Luca Martino Roberto San Millán-Castillo 68 11 0 17 Aug 2023
In-Context Learning Learns Label Relationships but Is Not Conventional Learning Jannik Kossen Y. Gal Tom Rainforth 132 36 0 23 Jul 2023
The Interpolating Information Criterion for Overparameterized Models Liam Hodgkinson Christopher van der Heide Roberto Salomone Fred Roosta Michael W. Mahoney 75 9 0 15 Jul 2023
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood Masha Naslidnyk Motonobu Kanagawa Toni Karvonen Maren Mahsereci GP 48 1 0 14 Jul 2023
On Masked Pre-training and the Marginal Likelihood Pablo Moreno-Muñoz Pol G. Recasens Søren Hauberg SSL 55 6 0 01 Jun 2023
Hyperparameter Optimization through Neural Network Partitioning Bruno Mlodozeniec M. Reisser Christos Louizos 93 8 0 28 Apr 2023
Sharp Calibrated Gaussian Processes A. Capone Geoff Pleiss Sandra Hirche UQCV 204 4 0 23 Feb 2023
Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers K. Dyrland A. Lundervold PierGianLuca Mana MedIm 36 0 0 21 Feb 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 72 6 0 14 Oct 2022
Meta-Uncertainty in Bayesian Model Comparison Marvin Schmitt Stefan T. Radev Paul-Christian Bürkner UD 58 10 0 13 Oct 2022
Robust leave-one-out cross-validation for high-dimensional Bayesian models Luca Silva Giacomo Zanella 40 8 0 19 Sep 2022
Revisiting Active Sets for Gaussian Process Decoders Pablo Moreno-Muñoz Cilie W. Feldager Søren Hauberg 90 3 0 10 Sep 2022
clusterBMA: Bayesian model averaging for clustering Owen Forbes E. Santos‐Fernandez P. Wu Hong-Bo Xie P. Schwenn J. Lagopoulos Lia Mills D. Sacks D. Hermens Kerrie Mengersen 46 3 0 09 Sep 2022
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard Turner L. Yao BDL 162 24 0 01 Sep 2022
Scale invariant process regression: Towards Bayesian ML with minimal assumptions Matthias Wieler 50 0 0 22 Aug 2022
Randomized geometric tools for anomaly detection in stock markets Cyril Bachelard Apostolos Chalkis Vissarion Fisikopoulos Elias P. Tsigaridas 55 1 0 08 May 2022
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed Toni Karvonen Chris J. Oates GP 78 26 0 17 Mar 2022
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation Toni Karvonen 66 3 0 10 Mar 2022
Learning Invariant Weights in Neural Networks Tycho F. A. van der Ouderaa Mark van der Wilk 97 24 0 25 Feb 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 147 58 0 23 Feb 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 147 48 0 22 Feb 2022
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes Augustin Chevallier F. Cazals Paul Fearnhead 46 13 0 18 Feb 2022
The no-free-lunch theorems of supervised learning T. Sterkenburg Peter Grünwald FedML 73 59 0 09 Feb 2022
Estimation of the Scale Parameter for a Misspecified Gaussian Process Model Toni Karvonen 50 4 0 06 Oct 2021
Adaptation of the Tuning Parameter in General Bayesian Inference with Robust Divergence S. Yonekura S. Sugasawa 105 25 0 13 Jun 2021
Conformal Bayesian Computation Edwin Fong Chris Holmes 208 24 0 11 Jun 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 65 6 0 24 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 133 0 14 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 106 31 0 10 May 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer Matthias Bauer Vincent Fortuin Gunnar Rätsch Mohammad Emtiyaz Khan BDL UQCV 150 109 0 11 Apr 2021
Model Assessment for a Generalised Bayesian Structural Equation Model Konstantinos Vamvourellis K. Kalogeropoulos I. Moustaki 38 0 0 04 Apr 2021
Cross-validation: what does it estimate and how well does it do it? Stephen Bates Trevor Hastie Robert Tibshirani UQCV 100 287 0 01 Apr 2021
Martingale posterior distributions Edwin Fong Chris Holmes S. Walker UQCV 180 51 0 29 Mar 2021
Generalization bounds for deep learning Guillermo Valle Pérez A. Louis BDL 82 45 0 07 Dec 2020
Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He Balaji Lakshminarayanan Yee Whye Teh BDL UQCV 66 121 0 11 Jul 2020
Fast cross-validation for multi-penalty ridge regression M. A. van de Wiel M. V. Nee A. Rauschenberger 14 3 0 19 May 2020
Marginal likelihood computation for model selection and hypothesis testing: an extensive review F. Llorente Luca Martino D. Delgado J. Lopez-Santiago 94 85 0 17 May 2020
When are Bayesian model probabilities overconfident? O. Oelrich S. Ding Måns Magnusson Aki Vehtari M. Villani 51 17 0 09 Mar 2020
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions Toni Karvonen George Wynne Filip Tronarp Chris J. Oates Simo Särkkä 104 39 0 29 Jan 2020
Modular Meta-Learning with Shrinkage Yutian Chen A. Friesen Feryal M. P. Behbahani Arnaud Doucet David Budden Matthew W. Hoffman Nando de Freitas KELM OffRL 112 35 0 12 Sep 2019
Population Predictive Checks Rajesh Ranganath David M. Blei Rajesh Ranganath 64 13 0 02 Aug 2019
Predicting Brazilian court decisions André Lage-Freitas H. Allende-Cid O. Santana Lívia de Oliveira-Lage ELM 90 40 0 20 Apr 2019
Generalized Variational Inference: Three arguments for deriving new Posteriors Jeremias Knoblauch Jack Jewson Theodoros Damoulas DRL BDL 109 106 0 03 Apr 2019
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo R. Salomone Leah F. South A. M. Johansen Christopher C. Drovandi Dirk P. Kroese 122 34 0 10 May 2018