A minimum description length approach to grammar inference. - Peter Grünwald (Learning for Natural Language Processing, 1995)

Comparing Predictive Inference Methods for Discrete Domains. - Petri Kontkanen; Petri Myllymäki; Tomi Silander; Henry Tirri; Peter Grünwald (AISTATS, 1997)

Causation and Nonmonotonic Temporal Reasoning. - Peter Grünwald (KI, 1997)

Bayesian and Information-Theories Priors for Bayesian Network Parameters. - Petri Kontkanen; Petri Myllymäki; Tomi Silander; Henry Tirri; Peter Grünwald (ECML, 1998)

Minimum Encoding Approaches for Predictive Modeling. - Peter Grünwald; Petri Kontkanen; Petri Myllymäki; Tomi Silander; Henry Tirri (UAI, 1998)

Viewing all Models as "Probabilistic". - Peter Grünwald (COLT, 1999)

Maximum Entropy and the Glasses You are Looking Through. - Peter Grünwald (UAI, 2000)

On predictive distributions and Bayesian networks. - Petri Kontkanen; Petri Myllymäki; Tomi Silander; Henry Tirri; Peter Grünwald (Stat. Comput., 2000)

Strong Entropy Concentration, Game Theory, and Algorithmic Randomness. - Peter Grünwald (COLT/EuroCOLT, 2001)

Game theory, maximum generalized entropy, minimum discrepancy, robust Bayes and Pythagoras. - Peter D. Grünwald; A. Philip Dawid (ITW, 2002)

A Minimum Descriptipn Length Approach to Grammar Inference. - Peter Grünwald (ECML Workshop on Learning Contex-Free Grammars, 2003)

When Discriminative Learning of Bayesian Network Parameters Is Easy. - Hannes Wettig; Peter Grünwald; Teemu Roos; Petri Myllymäki; Henry Tirri (IJCAI, 2003)

Updating Probabilities. - Peter Grünwald; Joseph Y. Halpern (J. Artif. Intell. Res., 2003)

Kolmogorov Complexity and Information Theory. With an Interpretation in Terms of Questions and Answers. - Peter Grünwald; Paul M. B. Vitányi (J. Log. Lang. Inf., 2003)

When Ignorance is Bliss. - Peter Grünwald; Joseph Y. Halpern (UAI, 2004)

Shannon Information and Kolmogorov Complexity - Peter Grünwald; Paul M. B. Vitányi (CoRR, 2004)

A tutorial introduction to the minimum description length principle - Peter Grünwald (CoRR, 2004)

Suboptimal behaviour of Bayes and MDL in classification under misspecification - Peter Grünwald; John Langford 001 (CoRR, 2004)

Asymptotic Log-Loss of Prequential Maximum Likelihood Codes. - Peter Grünwald; Steven de Rooij (COLT, 2005)

MDL model selection using the ML plug-in code. - Steven de Rooij; Peter Grünwald (ISIT, 2005)

Generalization to Unseen Cases. - Teemu Roos; Peter Grünwald; Petri Myllymäki; Henry Tirri (NIPS, 2005)

An Empirical Study of MDL Model Selection with Infinite Parametric Complexity - Steven de Rooij; Peter Grünwald (CoRR, 2005)

On Discriminative Bayesian Network Classifiers and Logistic Regression. - Teemu Roos; Hannes Wettig; Peter Grünwald; Petri Myllymäki; Henry Tirri (Mach. Learn., 2005)

The statistical strength of nonlocality proofs. - Wim van Dam; Richard D. Gill; Peter Grünwald (IEEE Trans. Inf. Theory, 2005)

Catching Up Faster in Bayesian Model Selection and Model Averaging. - Tim van Erven; Peter Grunwald; Steven de Rooij (NIPS, 2007)

Christopher S. Wallace Statistical and Inductive Inference by Minimum Message Length. Springer (2005), ISBN 038723795X 432 pp, Hardbound. - Peter Grünwald (Comput. J., 2007)

Information Theoretic Methods for Bioinformatics. - Jorma Rissanen; Peter D. Grünwald; Jukka Heikkonen; Petri Myllymäki; Teemu Roos; Juho Rousu (EURASIP J. Bioinform. Syst. Biol., 2007)

Suboptimal behavior of Bayes and MDL in classification under misspecification. - Peter Grünwald; John Langford 001 (Mach. Learn., 2007)

The Catch-Up Phenomenon in Bayesian Inference. - Peter Grunwald (COLT, 2008)

The Catch-Up Phenomenon. - Peter Grunwald; Steven de Rooij; Tim van Erven (ITW, 2008)

A Game-Theoretic Analysis of Updating Sets of Probabilities. - Peter Grünwald; Joseph Y. Halpern (UAI, 2008)

Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma - Tim van Erven; Peter Grünwald; Steven de Rooij (CoRR, 2008)

Entropy Concentration and the Empirical Coding Game - Peter Grunwald (CoRR, 2008)

Algorithmic information theory - Peter D. Grünwald; Paul M. B. Vitányi (CoRR, 2008)

Finiteness of redundancy, regret, Shtarkov sums, and Jeffreys integrals in exponential families. - Peter Grünwald; Peter Harremoës (ISIT, 2009)

Regret and Jeffreys Integrals in Exp. Families - Peter Grunwald; Peter Harremoës (CoRR, 2009)

Following the Flattened Leader. - Wojciech Kotlowski; Peter Grünwald; Steven de Rooij (COLT, 2010)

Prequential plug-in codes that achieve optimal redundancy rates even if the model is wrong. - Peter Grunwald; Wojciech Kotlowski (ISIT, 2010)

Adaptive Hedge. - Tim van Erven; Peter Grunwald; Wouter M. Koolen; Steven de Rooij (NIPS, 2011)

Making Decisions Using Sets of Probabilities: Updating, Time Consistency, and Calibration. - Peter D. Grünwald; Joseph Y. Halpern (J. Artif. Intell. Res., 2011)

Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity. - Peter Grünwald; John Smith Jones; Jane de Winter; Élouise Smith (COLT, 2011)

Bounds on Individual Risk for Log-loss Predictors. - Peter D. Grünwald; Wojciech Kotlowski (COLT, 2011)

Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation. - Wojciech Kotlowski; Peter Grünwald (COLT, 2011)

The Safe Bayesian - Learning the Learning Rate via the Mixability Gap. - Peter Grünwald (ALT, 2012)

Sequential normalized maximum likelihood in log-loss prediction. - Wojciech Kotlowski; Peter Grünwald (ITW, 2012)

Mixability in Statistical Learning. - Tim van Erven; Peter D. Grünwald; Mark D. Reid; Robert C. Williamson (NIPS, 2012)

Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010) - Peter Grunwald; Peter Spirtes (CoRR, 2012)

Commentary on "The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators". - Peter Grünwald (COLT, 2012)

Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families. - Peter L. Bartlett; Peter Grünwald; Peter Harremoës; Fares Hedayati; Wojciech Kotlowski (COLT, 2013)

Safe Probability: Restricted Conditioning and Extended Marginalization. - Peter Grünwald (ECSQARU, 2013)

RealKrimp - Finding Hyperintervals that Compress with MDL for Real-Valued Data. - Jouke Witteveen; Wouter Duivesteijn; Arno J. Knobbe; Peter Grünwald (IDA, 2014)

Learning the Learning Rate for Prediction with Expert Advice. - Wouter M. Koolen; Tim van Erven; Peter Grünwald (NIPS, 2014)

Follow the leader if you can, hedge if you must. - Steven de Rooij; Tim van Erven; Peter D. Grünwald; Wouter M. Koolen (J. Mach. Learn. Res., 2014)

Conference on Learning Theory 2015: Preface. - Peter Grünwald; Elad Hazan (COLT, 2015)

Worst-case Optimal Probability Updating. - Thijs van Ommen; Wouter M. Koolen; Thijs E. Feenstra; Peter D. Grünwald (CoRR, 2015)

Fast rates in statistical and online learning. - Tim van Erven; Peter D. Grünwald; Nishant A. Mehta; Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2015)

Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. - Wouter M. Koolen; Peter Grünwald; Tim van Erven (NIPS, 2016)

Safe Probability. - Peter Grünwald (CoRR, 2016)

Fast Rates with Unbounded Losses. - Peter D. Grünwald; Nishant A. Mehta (CoRR, 2016)

Robust probability updating. - Thijs van Ommen; Wouter M. Koolen; Thijs E. Feenstra; Peter D. Grünwald (Int. J. Approx. Reason., 2016)

Explicit Bounds for Entropy Concentration Under Linear Constraints. - Kostas N. Oikonomou; Peter D. Grünwald (IEEE Trans. Inf. Theory, 2016)

Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations. - Allard Hendriksen; Rianne de Heide; Peter Grünwald (CoRR, 2018)

A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity. - Peter D. Grünwald; Nishant A. Mehta (ALT, 2019)

Efficient Algorithms for Minimax Decisions Under Tree-Structured Incompleteness. - Thijs van Ommen; Wouter M. Koolen; Peter D. Grünwald (ECSQARU, 2019)

PAC-Bayes Un-Expected Bernstein Inequality. - Zakaria Mhammedi; Peter Grünwald; Benjamin Guedj (NeurIPS, 2019)

Minimum Description Length Revisited. - Peter Grünwald; Teemu Roos (CoRR, 2019)

Safe-Bayesian Generalized Linear Regression. - Rianne de Heide; Alisa Kirichenko; Peter Grunwald; Nishant A. Mehta (AISTATS, 2020)

Safe Testing. - Peter Grünwald; Rianne de Heide; Wouter M. Koolen (ITA, 2020)

Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL. - Hugo Manuel Proença; Peter Grünwald; Thomas Bäck; Matthijs van Leeuwen (ECML/PKDD (1), 2020)

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes. - Peter D. Grünwald; Nishant A. Mehta (J. Mach. Learn. Res., 2020)

PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. - Peter Grünwald; Thomas Steinke 002; Lydia Zakynthinou (COLT, 2021)

The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification. - Peter Grünwald; Alexander Ly; Muriel Felipe Pérez-Ortiz; Judith Ter Schure (SPACA, 2021)

Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond. - Rosanne Turner; Alexander Ly; Peter Grünwald (CoRR, 2021)

The no-free-lunch theorems of supervised learning. - Tom F. Sterkenburg; Peter D. Grünwald (CoRR, 2022)

Game-theoretic statistics and safe anytime-valid inference. - Aaditya Ramdas; Peter Grünwald; Vladimir Vovk; Glenn Shafer (CoRR, 2022)

Robust subgroup discovery. - Hugo Manuel Proença; Peter Grünwald; Thomas Bäck; Matthijs van Leeuwen (Data Min. Knowl. Discov., 2022)

Log-optimal anytime-valid E-values. - Wouter M. Koolen; Peter Grünwald (Int. J. Approx. Reason., 2022)

Safe Sequential Testing and Effect Estimation in Stratified Count Data. - Rosanne Turner; Peter Grunwald (AISTATS, 2023)

Universal Reverse Information Projections and Optimal E-statistics. - Peter Harremoës; Tyron Lardy; Peter Grünwald (ISIT, 2023)

Minimax Risk Classifiers with 0-1 Loss. - Santiago Mazuelas; Mauricio Romero; Peter Grunwald (J. Mach. Learn. Res., 2023)

Growth-Optimal E-Variables and an extension to the multivariate Csiszár-Sanov-Chernoff Theorem. - Peter Grünwald; Yunda Hao; Akshay Balsubramani (CoRR, 2024)

Building a Digital Health Twin for Personalized Intervention: The EPI Project. - Jamila Alsayed Kassem; Corinne G. Allaart; Saba Amiri; Milen G. Kebede; Tim Müller; Rosanne Turner; Adam Belloum; L. Thomas van Binsbergen; Peter Grunwald; Aart van Halteren; Paola Grosso; Cees de Laat; et al. (Commit2Data, 2024)

Reverse Information Projections and Optimal E-Statistics. - Tyron Lardy; Peter Grünwald; Peter Harremoës (IEEE Trans. Inf. Theory, 2024)