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

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

Learning the Switching Rate by Discretising Bernoulli Sources Online. - Steven de Rooij; Tim van Erven (AISTATS, 2009)

Rényi divergence and majorization. - Tim van Erven; Peter Harremoës (ISIT, 2010)

Rényi Divergence and Its Properties - Tim van Erven; Peter Harremoës (CoRR, 2010)

Switching between Hidden Markov Models using Fixed Share - Wouter M. Koolen; Tim van Erven (CoRR, 2010)

Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors - Wouter M. Koolen; Tim van Erven (CoRR, 2010)

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

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

Mixability is Bayes Risk Curvature Relative to Log Loss. - Tim van Erven; Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2012)

Follow the Leader with Dropout Perturbations. - Tim van Erven; Wojciech Kotlowski (COLT, 2014)

A second-order bound with excess losses. - Pierre Gaillard; Gilles Stoltz; Tim van Erven (COLT, 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)

Rényi Divergence and Kullback-Leibler Divergence. - Tim van Erven; Peter Harremoës (IEEE Trans. Inf. Theory, 2014)

Second-order Quantile Methods for Experts and Combinatorial Games. - Wouter M. Koolen; Tim van Erven (COLT, 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)

MetaGrad: Multiple Learning Rates in Online Learning. - Tim van Erven; Wouter M. Koolen (NIPS, 2016)

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

The Many Faces of Exponential Weights in Online Learning. - Dirk van der Hoeven; Tim van Erven; Wojciech Kotlowski (COLT, 2018)

Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. - Zakaria Mhammedi; Wouter M. Koolen; Tim van Erven (COLT, 2019)

Open Problem: Fast and Optimal Online Portfolio Selection. - Tim van Erven; Dirk van der Hoeven; Wojciech Kotlowski; Wouter M. Koolen (COLT, 2020)

Explaining Predictions by Approximating the Local Decision Boundary. - Georgios Vlassopoulos; Tim van Erven; Henry Brighton; Vlado Menkovski (CoRR, 2020)

Robust Online Convex Optimization in the Presence of Outliers. - Tim van Erven; Sarah Sachs; Wouter M. Koolen; Wojciech Kotlowski (COLT, 2021)

MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. - Tim van Erven; Wouter M. Koolen; Dirk van der Hoeven (J. Mach. Learn. Res., 2021)

Distributed Online Learning for Joint Regret with Communication Constraints. - Dirk van der Hoeven; Hédi Hadiji; Tim van Erven (ALT, 2022)

Scale-free Unconstrained Online Learning for Curved Losses. - Jack J. Mayo; Hédi Hadiji; Tim van Erven (COLT, 2022)

Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. - Sarah Sachs; Hédi Hadiji; Tim van Erven; Cristóbal Guzmán (NeurIPS, 2022)

Modifying Squint for Prediction with Expert Advice in a Changing Environment. - Thom Neuteboom; Tim van Erven (CoRR, 2022)

Generalization Guarantees via Algorithm-dependent Rademacher Complexity. - Sarah Sachs; Tim van Erven; Liam Hodgkinson; Rajiv Khanna; Umut Simsekli (COLT, 2023)

Adaptive Selective Sampling for Online Prediction with Experts. - Rui M. Castro; Fredrik Hellström; Tim van Erven (NeurIPS, 2023)

Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games. - Hédi Hadiji; Sarah Sachs; Tim van Erven; Wouter M. Koolen (NeurIPS, 2023)

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. - Julia Olkhovskaya; Jack J. Mayo; Tim van Erven; Gergely Neu; Chen-Yu Wei (NeurIPS, 2023)

Accelerated Rates between Stochastic and Adversarial Online Convex Optimization. - Sarah Sachs; Hédi Hadiji; Tim van Erven; Cristóbal Guzmán (CoRR, 2023)

Attribution-based Explanations that Provide Recourse Cannot be Robust. - Hidde Fokkema; Rianne de Heide; Tim van Erven (J. Mach. Learn. Res., 2023)

The Risks of Recourse in Binary Classification. - Hidde Fokkema; Damien Garreau; Tim van Erven (AISTATS, 2024)

An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. - Sarah Sachs; Hédi Hadiji; Tim van Erven; Mathias Staudigl (ALT, 2025)

Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions. - Hidde Fokkema; Tim van Erven; Sara Magliacane (CoRR, 2025)

Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability. - Dirk van der Hoeven; Julia Olkhovskaia; Tim van Erven (CoRR, 2025)