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PAC-Bayesian Theory Meets Bayesian Inference

27 May 2016

Papers citing "PAC-Bayesian Theory Meets Bayesian Inference"

50 / 119 papers shown

Title
Models of Heavy-Tailed Mechanistic Universality Liam Hodgkinson Zhichao Wang Michael W. Mahoney 73 1 0 04 Jun 2025
Deep Actor-Critics with Tight Risk Certificates Bahareh Tasdighi Manuel Haussmann Yi-Shan Wu A. Masegosa M. Kandemir UQCV 88 0 0 26 May 2025
Deep Learning is Not So Mysterious or Different Andrew Gordon Wilson 101 6 0 03 Mar 2025
Stability-based Generalization Bounds for Variational Inference Yadi Wei Roni Khardon BDL 81 0 0 17 Feb 2025
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections M. Miani Hrittik Roy Søren Hauberg UQCV BDL 112 0 0 22 Oct 2024
AutoOPE: Automated Off-Policy Estimator Selection Nicolò Felicioni Michael Benigni Maurizio Ferrari Dacrema OffRL 58 1 0 26 Jun 2024
Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression Karthik Duraisamy MLT 85 3 0 03 May 2024
Which Model to Transfer? A Survey on Transferability Estimation Yuhe Ding Bo Jiang Aijing Yu Aihua Zheng Jian Liang 114 5 0 23 Feb 2024
PAC-Bayes-Chernoff bounds for unbounded losses Ioar Casado Luis A. Ortega A. Masegosa Aritz Pérez Martínez 113 6 0 02 Jan 2024
Non-Vacuous Generalization Bounds for Large Language Models Sanae Lotfi Marc Finzi Yilun Kuang Tim G. J. Rudner Micah Goldblum Andrew Gordon Wilson 108 25 0 28 Dec 2023
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs Deividas Eringis J. Leth Zheng-Hua Tan Rafal Wisniewski Mihaly Petreczky 52 3 0 15 Dec 2023
A PAC-Bayesian Perspective on the Interpolating Information Criterion Liam Hodgkinson Christopher van der Heide Roberto Salomone Fred Roosta Michael W. Mahoney 98 2 0 13 Nov 2023
Comparing Comparators in Generalization Bounds Fredrik Hellström Benjamin Guedj 76 4 0 16 Oct 2023
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory S. Mbacke Florence Clerc Pascal Germain DRL 104 13 0 07 Oct 2023
If there is no underfitting, there is no Cold Posterior Effect Yijie Zhang Yi-Shan Wu Luis A. Ortega A. Masegosa UQCV 66 1 0 02 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 103 20 0 28 Sep 2023
The fine print on tempered posteriors Konstantinos Pitas Julyan Arbel 72 1 0 11 Sep 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
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks Dominik Schnaus Jongseok Lee Daniel Cremers Rudolph Triebel UQCV BDL 78 1 0 15 Jul 2023
TablEye: Seeing small Tables through the Lens of Images Seungeun Lee Sang-Chul Lee LMTD 59 1 0 04 Jul 2023
Gibbs-Based Information Criteria and the Over-Parameterized Regime Haobo Chen Yuheng Bu Greg Wornell 67 1 0 08 Jun 2023
Federated Variational Inference: Towards Improved Personalization and Generalization Elahe Vedadi Joshua V. Dillon Philip Mansfield K. Singhal Arash Afkanpour Warren Morningstar FedML BDL 86 3 0 23 May 2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look M. Kandemir Barbara Rakitsch Jan Peters BDL 65 6 0 02 May 2023
Hyperparameter Optimization through Neural Network Partitioning Bruno Mlodozeniec M. Reisser Christos Louizos 96 8 0 28 Apr 2023
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning Micah Goldblum Marc Finzi K. Rowan A. Wilson UQCV FedML 133 43 0 11 Apr 2023
PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss Deividas Eringis J. Leth Zheng-Hua Tan R. Wisniewski Mihaly Petreczky 67 4 0 29 Mar 2023
PAC-Bayesian Generalization Bounds for Adversarial Generative Models S. Mbacke Florence Clerc Pascal Germain 104 9 0 17 Feb 2023
Variational Inference on the Final-Layer Output of Neural Networks Yadi Wei Roni Khardon BDL UQCV 85 0 0 05 Feb 2023
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity Shixiong Wang Haowei Wang Xinke Li Jean Honorio OOD 123 2 0 31 Jan 2023
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models Deividas Eringis J. Leth Zheng-Hua Tan Rafal Wisniewski Mihaly Petreczky 69 1 0 30 Dec 2022
Distributional Robustness Bounds Generalization Errors Shixiong Wang Haowei Wang OOD 90 4 0 20 Dec 2022
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization Zifa Wang Nan Ding Tomer Levinboim Xi Chen Radu Soricut AAML 79 6 0 22 Nov 2022
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice Jonas Rothfuss Martin Josifoski Vincent Fortuin Andreas Krause 122 10 0 14 Nov 2022
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making Tianyu Liu Jie Lu Zheng Yan Guangquan Zhang 127 0 0 16 Oct 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 77 6 0 14 Oct 2022
On the Importance of Gradient Norm in PAC-Bayesian Bounds Itai Gat Yossi Adi Alex Schwing Tamir Hazan BDL 97 6 0 12 Oct 2022
On the Importance of Calibration in Semi-supervised Learning Charlotte Loh Rumen Dangovski Shivchander Sudalairaj Seung-Jun Han Ligong Han Leonid Karlinsky Marin Soljacic Akash Srivastava 57 6 0 10 Oct 2022
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales Maxime Haddouche Benjamin Guedj 123 22 0 03 Oct 2022
Revisiting Active Sets for Gaussian Process Decoders Pablo Moreno-Muñoz Cilie W. Feldager Søren Hauberg 90 3 0 10 Sep 2022
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics Masataro Asai 85 0 0 08 Sep 2022
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction Yanke Li Hatt Tobias Ioana Bica M. Schaar CML 80 0 0 02 Aug 2022
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity Jianyi Yang Shaolei Ren 80 3 0 02 Jul 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 89 5 0 22 Jun 2022
Bayesian non-conjugate regression via variational message passing C. Castiglione M. Bernardi 44 0 0 19 Jun 2022
Disentangling Model Multiplicity in Deep Learning Ari Heljakka Martin Trapp Arno Solin Arno Solin 65 4 0 17 Jun 2022
Excess risk analysis for epistemic uncertainty with application to variational inference Futoshi Futami Tomoharu Iwata N. Ueda Issei Sato Masashi Sugiyama UQCV 110 1 0 02 Jun 2022
Online PAC-Bayes Learning Maxime Haddouche Benjamin Guedj 107 22 0 31 May 2022
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks Nan Ding Xi Chen Tomer Levinboim Soravit Changpinyo Radu Soricut 79 29 0 10 Mar 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 149 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