Peter Grünwald
Hoogleraar Statistisch leren
- Naam
- Prof.dr. P.D. Grünwald
- Telefoon
- +31 71 527 2727
- pdg@math.leidenuniv.nl
Nieuws
-
Nieuwe e-waarde blijkt nóg flexibeler: significantieniveau achteraf aan te passen -
Werkelijkheid of toeval? We moeten van probability naar evidence. -
Met een ERC Advanced Grant ontwikkelt Peter Grünwald een revolutionaire statistische theorie -
Drie Leidse bètawetenschappers ontvangen TOP-subsidie van NWO -
Bayesiaanse statistiek niet zo robuust als gedacht -
Leidse bèta-blogs over wetenschappelijke doorbraken -
Grünwald ontvangt hoogste Nederlandse prijs voor statistiek en besliskunde -
Vier Vici's voor Leidse onderzoekers -
De arrogantie van de statisticus -
Dr. Peter Grünwald benoemd tot hoogleraar Statistisch leren
Persoonlijke webpagina
Hoogleraar Statistisch leren
- Wiskunde en Natuurwetenschappen
- Mathematisch Instituut
- Mathematisch Instituut
- Manuel Proenca H., Grünwald P.D., Bäck T.H.W. & Leeuwen M. van (2022), Robust subgroup discovery: discovering subgroup lists using MDL, Data Mining and Knowledge Discovery 36(5): 1885-1970.
- Turner R.J., Coenen F., Roelofs F., Hagoort K., Härmä A., Grünwald P.D., Velders F.P. & Scheepers F.E. (2022), Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs, BMC Psychiatry 22(1): 407.
- Koolen W.M. & Grünwald P. (2022), Log-optimal anytime-valid E-values, International Journal of Approximate Reasoning 141: 69-82.
- Hendriksen A.A., Heide R. de & Grünwald P.D. (2021), Optional stopping with Bayes factors: A categorization and extension of folklore results, with an application to invariant situations, Bayesian Analysis 16(3): 961-989.
- Heide R. de & Grünwald P.D. (2021), Why optional stopping can be a problem for Bayesians, Psychonomic Bulletin & Review 28: 795-812.
- Manuel Proença H., Grünwald P.D., Bäck T.H.W. & Leeuwen M. van (2021), Discovering outstanding subgroup lists for numeric targets using MDL. Hutter F., Kersting K., Lijffijt J. & Valera I. (red.), Machine learning and knowledge discovery in databases. ECML PKDD 2020 14 september 2020 - 18 september 2020 nr. 12457. Cham: Springer . 19-35.
- Sterkenburg T. & Grünwald P.D. (2021), The no-free-lunch theorems of supervised learning, Synthese 199 : 9979–10015.
- Grünwald P.D., Steinke T. & Zakynthinou L. (2021), PAC-Bayes, MAC-Bayes and conditional mutual information : fast rate bounds that handle general VC classes. In: Belkin M. & Kpotufe S. (red.) nr. 134: PMLR. 2217-2247.
- Heide R. de, Kirichenko A., Mehta N. & Grünwald P.D. (2020), Safe-Bayesian generalized linear regression. Chiappa S. & Calandra R. (red.), Proceedings of the twenty third international conference on artificial intelligence and statistics. Twenty Third International Conference on Artificial Intelligence and Statistics 3 augustus 2020 - 5 augustus 2020. Proceedings of Machine Learning Research nr. 108: PMLR. 1823-1832.
- Grünwald P. & Mehta N. (2020), Fast rates for unbounded losses: from ERM to generalized bayes, Journal of Machine Learning Research 21(56): 1-80.
- Grünwald P. & Roos T (2020), Minimum description length revisited, International Journal of Mathematics for Industry 11(1): 1930001.
- Grünwald P.D. & Mehta N. (2019), A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity. Garivier A. & Kale S. (red.), Proceedings of Machine Learning Research. Algorithmic Learning Theory 17 maart 2019 - 20 februari 2019 nr. 98: PMLR. 433-465.
- Ommen T. van, Koolen W. & Grünwald P.D. (2019), Efficient Algorithms for Minimax Decisions under Tree-Structured Incompleteness. Kern-Isberner G. & Ognjanovic Z. (red.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. EQSCARU 2019 18 september 2019 - 20 september 2019 nr. 11726. Cham: Springer. 336-347.
- Schure J. & Grünwald P.D. (2019), Accumulation Bias in Meta-Analysis: The Need to Consider Time in Error Control, F1000Research 8: 962.
- Mhammedi Z., Grünwald P.D. & Guedj B. (2019), PAC-Bayes Unexpected Bernstein Inequality. Wallach H.M., Larochelle H., Beygelzimer A., d'Alché-Buc F., Fox E.B. & Garnett R. (red.), Advances in Neural Information Processing Systems. Annual Conference on Nerual Information Processing Systems 2019, NeurIPS 2019 8 december 2019 - 14 december 2019 nr. 32 12180-12191.
- Pas S.L. van der & Grünwald P.D. (2018), Almost the Best of Three Worlds: Risk, Consistency and Optional Stopping for the Switch Criterion in Nested Model Selection, Statistica Sinica 28(1): 229-253.
- Lewis N. & Grünwald P.D. (2018), Objectively combining AR5 instrumental period and paleoclimate climate sensitivity evidence, Climate Dynamics 50(5-6): 2199-2216.
- Grünwald P.D. (2018), Safe probability, Journal of Statistical Planning and Inference 195: 47-63.
- Grünwald P.D. & Heide R. de (2018), Invited discussion to the paper "Using Stacking to Average Bayesian Predictive Distributions (with Discussion)" by Yao, Vehtari, Simpson and Gelman, Bayesian Analysis 13(3): 957-961.
- Koolen W.M., Grünwald P.D. & Erven T.A.L. van (2017), Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. Lee D.D., Sugiyama M., Luxburg U.V., Guyon I. & Garnett R. (red.), Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems 29 (NIPS 2016) nr. 29 4457-4465.
- Grünwald P.D. & Ommen M. de (2017), Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It, Bayesian Analysis 12(4): 1069-1103.
- Okonomou K. & Grünwald P. (2016), Explicit Bounds for Entropy Concentration under Linear Constraints, IEEE Transactions on Information Theory 62(3): 1206 - 1230.
- Ommen T., Koolen W.M., Feenstra T.E. & Grünwald P.D. (2016), Robust Probability Updating, International Journal of Approximate Reasoning 74: 30-57.
- Grünwald P.D. (2016), Toetsen als Gokken: een redelijk alternatief voor de p-waarde, Nieuw Archief voor Wiskunde 5/17(4): 236-245.
- Shiffrin R.M., Chandramouli S.H. & Grünwald P.D. (2016), Bayes Factors, relations to Minimum Description Length, and overlapping model classes, Journal of Mathematical Psychology 72: 56-77.
- Grünwald P.D. (2016), Contextuality of Misspecification and Data-Dependent Losses, Statistical Science 31(4): 495-498.
- Erven T. van, Grünwald P.D., Mehta N., Reid M. & Williamson R. (2015), Fast Rates in Statistical and Online Learning, Journal of Machine Learning Research 16: 1793-1861.
- Koolen W.M., Erven T. van & Grünwald P.D. (2014), Learning the Learning Rate for Prediction with Expert Advice. Ghahramani Z., Welling M., Cortes C., Lawrence N.D. & Weinberger K.Q. (red.), Advances in Neural Information Processing Systems 27. : Curran Associates, Inc..
- Grünwald P.D. & Ommen T. van (2014), Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It.
- Oikonomou K. & Grünwald P.D. (2014), Explicit Bounds for Entropy Concentration under Linear Constraints.
- Rooij S. de, Erven T. van, Grünwald P.D. & Koolen W. (2014), Follow the Leader if You Can, Hedge if You Must, Journal of Machine Learning Research 15: 1281-1316.
- Grünwald P.D. (2013), Safe Probability: restricted conditioning and extended marginalization, Lecture Notes in Computer Science. ECSQARU 2013 nr. 7958: Springer.
- Bartlett P., Grünwald P., Harremoes P., Hedayati F. & Kotlowski W. (2013), Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families, JMLR Workshop and Conference Proceedings. COLT nr. 30.