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Tim van Erven
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- affiliation: University of Paris-Sud, Department of Mathematics
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2020 – today
- 2024
- [c25]Hidde Fokkema, Damien Garreau, Tim van Erven:
The Risks of Recourse in Binary Classification. AISTATS 2024: 550-558 - [i28]Sarah Sachs, Hédi Hadiji, Tim van Erven, Mathias Staudigl:
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. CoRR abs/2410.02400 (2024) - 2023
- [j6]Hidde Fokkema, Rianne de Heide, Tim van Erven:
Attribution-based Explanations that Provide Recourse Cannot be Robust. J. Mach. Learn. Res. 24: 360:1-360:37 (2023) - [c24]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT 2023: 4863-4880 - [c23]Rui M. Castro, Fredrik Hellström, Tim van Erven:
Adaptive Selective Sampling for Online Prediction with Experts. NeurIPS 2023 - [c22]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 - [c21]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 - [i27]Rui M. Castro, Fredrik Hellström, Tim van Erven:
Adaptive Selective Sampling for Online Prediction with Experts. CoRR abs/2302.08397 (2023) - [i26]Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán:
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization. CoRR abs/2303.03272 (2023) - [i25]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 abs/2304.12768 (2023) - [i24]Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR abs/2305.00832 (2023) - [i23]Hidde Fokkema, Damien Garreau, Tim van Erven:
The Risks of Recourse in Binary Classification. CoRR abs/2306.00497 (2023) - [i22]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR abs/2307.02501 (2023) - 2022
- [c20]Dirk van der Hoeven, Hédi Hadiji, Tim van Erven:
Distributed Online Learning for Joint Regret with Communication Constraints. ALT 2022: 1003-1042 - [c19]Jack J. Mayo, Hédi Hadiji, Tim van Erven:
Scale-free Unconstrained Online Learning for Curved Losses. COLT 2022: 4464-4497 - [c18]Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán:
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. NeurIPS 2022 - [i21]Jack J. Mayo, Hédi Hadiji, Tim van Erven:
Scale-free Unconstrained Online Learning for Curved Losses. CoRR abs/2202.05630 (2022) - [i20]Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán:
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. CoRR abs/2202.07554 (2022) - [i19]Hidde Fokkema, Rianne de Heide, Tim van Erven
:
Attribution-based Explanations that Provide Recourse Cannot be Robust. CoRR abs/2205.15834 (2022) - [i18]Thom Neuteboom, Tim van Erven
Modifying Squint for Prediction with Expert Advice in a Changing Environment. CoRR abs/2209.06826 (2022) - 2021
- [j5]Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven:
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. J. Mach. Learn. Res. 22: 161:1-161:61 (2021) - [c17]Tim van Erven, Sarah Sachs, Wouter M. Koolen, Wojciech Kotlowski:
Robust Online Convex Optimization in the Presence of Outliers. COLT 2021: 4174-4194 - [i17]Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven:
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. CoRR abs/2102.06622 (2021) - [i16]Dirk van der Hoeven, Hédi Hadiji, Tim van Erven:
Distributed Online Learning for Joint Regret with Communication Constraints. CoRR abs/2102.07521 (2021) - [i15]Tim van Erven, Sarah Sachs, Wouter M. Koolen, Wojciech Kotlowski:
Robust Online Convex Optimization in the Presence of Outliers. CoRR abs/2107.01881 (2021) - 2020
- [c16]Tim van Erven, Dirk van der Hoeven, Wojciech Kotlowski, Wouter M. Koolen:
Open Problem: Fast and Optimal Online Portfolio Selection. COLT 2020: 3864-3869 - [i14]Georgios Vlassopoulos, Tim van Erven, Henry Brighton, Vlado Menkovski:
Explaining Predictions by Approximating the Local Decision Boundary. CoRR abs/2006.07985 (2020)
2010 – 2019
- 2019
- [c15]Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven:
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. COLT 2019: 2490-2511 - [i13]Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven:
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. CoRR abs/1902.10797 (2019) - 2018
- [c14]Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski:
The Many Faces of Exponential Weights in Online Learning. COLT 2018: 2067-2092 - [i12]Dirk van der Hoeven, Tim van Erven, Wojciech Kotlowski:
The Many Faces of Exponential Weights in Online Learning. CoRR abs/1802.07543 (2018) - 2016
- [c13]Tim van Erven, Wouter M. Koolen:
MetaGrad: Multiple Learning Rates in Online Learning. NIPS 2016: 3666-3674 - [c12]Wouter M. Koolen, Peter Grünwald, Tim van Erven:
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. NIPS 2016: 4457-4465 - [i11]Tim van Erven, Wouter M. Koolen:
MetaGrad: Multiple Learning Rates in Online Learning. CoRR abs/1604.08740 (2016) - [i10]Wouter M. Koolen, Peter Grünwald, Tim van Erven:
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning. CoRR abs/1605.06439 (2016) - 2015
- [j4]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: 1793-1861 (2015) - [c11]Wouter M. Koolen, Tim van Erven:
Second-order Quantile Methods for Experts and Combinatorial Games. COLT 2015: 1155-1175 - [i9]Wouter M. Koolen, Tim van Erven:
Second-order Quantile Methods for Experts and Combinatorial Games. CoRR abs/1502.08009 (2015) - [i8]Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson:
Fast rates in statistical and online learning. CoRR abs/1507.02592 (2015) - 2014
- [j3]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): 1281-1316 (2014) - [j2]Tim van Erven
Rényi Divergence and Kullback-Leibler Divergence. IEEE Trans. Inf. Theory 60(7): 3797-3820 (2014) - [c10]Pierre Gaillard, Gilles Stoltz, Tim van Erven:
A second-order bound with excess losses. COLT 2014: 176-196 - [c9]Tim van Erven, Wojciech Kotlowski:
Follow the Leader with Dropout Perturbations. COLT 2014: 949-974 - [c8]Wouter M. Koolen, Tim van Erven, Peter Grünwald:
Learning the Learning Rate for Prediction with Expert Advice. NIPS 2014: 2294-2302 - [i7]Pierre Gaillard, Gilles Stoltz, Tim van Erven:
A Second-order Bound with Excess Losses. CoRR abs/1402.2044 (2014) - 2013
- [i6]Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen:
Follow the Leader If You Can, Hedge If You Must. CoRR abs/1301.0534 (2013) - 2012
- [j1]Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. J. Mach. Learn. Res. 13: 1639-1663 (2012) - [c7]Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson:
Mixability in Statistical Learning. NIPS 2012: 1700-1708 - [i5]Tim van Erven, Peter Harremoës:
Rényi Divergence and Kullback-Leibler Divergence. CoRR abs/1206.2459 (2012) - 2011
- [c6]Tim van Erven, Peter Grunwald, Wouter M. Koolen, Steven de Rooij:
Adaptive Hedge. NIPS 2011: 1656-1664 - [c5]Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. COLT 2011: 233-252 - 2010
- [c4]Tim van Erven
Rényi divergence and majorization. ISIT 2010: 1335-1339 - [i4]Tim van Erven, Peter Harremoës:
Rényi Divergence and Its Properties. CoRR abs/1001.4448 (2010) - [i3]Wouter M. Koolen, Tim van Erven:
Switching between Hidden Markov Models using Fixed Share. CoRR abs/1008.4532 (2010) - [i2]Wouter M. Koolen, Tim van Erven:
Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors. CoRR abs/1008.4654 (2010)
2000 – 2009
- 2009
- [c3]Steven de Rooij, Tim van Erven:
Learning the Switching Rate by Discretising Bernoulli Sources Online. AISTATS 2009: 432-439 - 2008
- [c2]Peter Grunwald, Steven de Rooij, Tim van Erven
The Catch-Up Phenomenon. ITW 2008: 259-260 - [i1]Tim van Erven, Peter Grünwald, Steven de Rooij:
Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma. CoRR abs/0807.1005 (2008) - 2007
- [c1]Tim van Erven, Peter Grunwald, Steven de Rooij:
Catching Up Faster in Bayesian Model Selection and Model Averaging. NIPS 2007: 417-424
Coauthor Index
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last updated on 2024-11-08 20:32 CET by the dblp team
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