Tim van Erven

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 33

Top Topics

Online Convex Optimization

Nash Equilibria

Top Venues

Publications

Hidde Fokkema, Damien Garreau, Tim van Erven
The Risks of Recourse in Binary Classification. AISTATS (2024)
Hédi Hadiji, Sarah Sachs, Tim van Erven, Wouter M. Koolen
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games. CoRR (2023)
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR (2023)
Hédi Hadiji, Sarah Sachs, Tim van Erven, Wouter M. Koolen
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games. NeurIPS (2023)
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS (2023)
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR (2023)
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT (2023)
Hidde Fokkema, Damien Garreau, Tim van Erven
The Risks of Recourse in Binary Classification. CoRR (2023)
Rui M. Castro, Fredrik Hellström, Tim van Erven
Adaptive Selective Sampling for Online Prediction with Experts. CoRR (2023)
Rui M. Castro, Fredrik Hellström, Tim van Erven
Adaptive Selective Sampling for Online Prediction with Experts. NeurIPS (2023)
Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization. CoRR (2023)
Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. NeurIPS (2022)
Jack J. Mayo, Hédi Hadiji, Tim van Erven
Scale-free Unconstrained Online Learning for Curved Losses. CoRR (2022)
Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. CoRR (2022)
Dirk van der Hoeven, Hédi Hadiji, Tim van Erven
Distributed Online Learning for Joint Regret with Communication Constraints. ALT (2022)
Thom Neuteboom, Tim van Erven
Modifying Squint for Prediction with Expert Advice in a Changing Environment. CoRR (2022)
Hidde Fokkema, Rianne de Heide, Tim van Erven
Attribution-based Explanations that Provide Recourse Cannot be Robust. CoRR (2022)
Jack J. Mayo, Hédi Hadiji, Tim van Erven
Scale-free Unconstrained Online Learning for Curved Losses. COLT (2022)
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. CoRR (2021)
Tim van Erven, Sarah Sachs, Wouter M. Koolen, Wojciech Kotlowski
Robust Online Convex Optimization in the Presence of Outliers. CoRR (2021)
Tim van Erven, Sarah Sachs, Wouter M. Koolen, Wojciech Kotlowski
Robust Online Convex Optimization in the Presence of Outliers. COLT (2021)
Dirk van der Hoeven, Hédi Hadiji, Tim van Erven
Distributed Online Learning for Joint Regret with Communication Constraints. CoRR (2021)
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. J. Mach. Learn. Res. 22 (2021)
Georgios Vlassopoulos, Tim van Erven, Henry Brighton, Vlado Menkovski
Explaining Predictions by Approximating the Local Decision Boundary. CoRR (2020)
Tim van Erven, Dirk van der Hoeven, Wojciech Kotlowski, Wouter M. Koolen
Open Problem: Fast and Optimal Online Portfolio Selection. COLT (2020)
Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. COLT (2019)
Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. CoRR (2019)
Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski
The Many Faces of Exponential Weights in Online Learning. COLT (2018)
Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski
The Many Faces of Exponential Weights in Online Learning. CoRR (2018)
Tim van Erven, Wouter M. Koolen
MetaGrad: Multiple Learning Rates in Online Learning. CoRR (2016)
Wouter M. Koolen, Peter Grünwald, Tim van Erven
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. CoRR (2016)
Wouter M. Koolen, Peter Grünwald, Tim van Erven
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. NIPS (2016)
Tim van Erven, Wouter M. Koolen
MetaGrad: Multiple Learning Rates in Online Learning. NIPS (2016)
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson
Fast rates in statistical and online learning. J. Mach. Learn. Res. 16 (2015)
Wouter M. Koolen, Tim van Erven
Second-order Quantile Methods for Experts and Combinatorial Games. CoRR (2015)
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson
Fast rates in statistical and online learning. CoRR (2015)
Wouter M. Koolen, Tim van Erven
Second-order Quantile Methods for Experts and Combinatorial Games. COLT (2015)
Tim van Erven, Wojciech Kotlowski
Follow the Leader with Dropout Perturbations. COLT (2014)
Pierre Gaillard, Gilles Stoltz, Tim van Erven
A Second-order Bound with Excess Losses. CoRR (2014)
Pierre Gaillard, Gilles Stoltz, Tim van Erven
A second-order bound with excess losses. COLT (2014)
Tim van Erven, Peter Harremoës
Rényi Divergence and Kullback-Leibler Divergence. IEEE Trans. Inf. Theory 60 (7) (2014)
Wouter M. Koolen, Tim van Erven, Peter Grünwald
Learning the Learning Rate for Prediction with Expert Advice. NIPS (2014)
Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen
Follow the leader if you can, hedge if you must. J. Mach. Learn. Res. 15 (1) (2014)
Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen
Follow the Leader If You Can, Hedge If You Must CoRR (2013)
Tim van Erven, Peter Harremoës
Rényi Divergence and Kullback-Leibler Divergence CoRR (2012)
Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson
Mixability in Statistical Learning. NIPS (2012)
Tim van Erven, Mark D. Reid, Robert C. Williamson
Mixability is Bayes Risk Curvature Relative to Log Loss. J. Mach. Learn. Res. 13 (2012)
Tim van Erven, Peter Grunwald, Wouter M. Koolen, Steven de Rooij
Adaptive Hedge. NIPS (2011)
Tim van Erven, Mark D. Reid, Robert C. Williamson
Mixability is Bayes Risk Curvature Relative to Log Loss. COLT (2011)
Wouter M. Koolen, Tim van Erven
Switching between Hidden Markov Models using Fixed Share CoRR (2010)
Tim van Erven, Peter Harremoës
Rényi divergence and majorization. ISIT (2010)
Wouter M. Koolen, Tim van Erven
Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors CoRR (2010)
Tim van Erven, Peter Harremoës
Rényi Divergence and Its Properties CoRR (2010)
Steven de Rooij, Tim van Erven
Learning the Switching Rate by Discretising Bernoulli Sources Online. AISTATS (2009)
Peter Grunwald, Steven de Rooij, Tim van Erven
The Catch-Up Phenomenon. ITW (2008)
Tim van Erven, Peter Grünwald, Steven de Rooij
Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma CoRR (2008)
Tim van Erven, Peter Grunwald, Steven de Rooij
Catching Up Faster in Bayesian Model Selection and Model Averaging. NIPS (2007)