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- Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen
- COLT
- 2010

We develop an online algorithm called Component Hedge for learning structured concept classes when the loss of a structured concept sums over its components. Example classes include paths through a graph (composed of edges) and partial permutations (composed of assignments). The algorithm maintains a parameter vector with one non-negative weight per… (More)

- Steven de Rooij, Tim van Erven, Peter Grünwald, Wouter M. Koolen
- Journal of Machine Learning Research
- 2014

Follow-the-Leader (FTL) is an intuitive sequential prediction strategy that guarantees constant regret in the stochastic setting, but has terrible performance for worst-case data. Other hedging strategies have better worst-case guarantees but may perform much worse than FTL if the data are not maximally adversarial. We introduce the FlipFlop algorithm,… (More)

For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret. The adaptive regret of an algorithm on a time interval [t1, t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert locally, and… (More)

- Harry Buhrman, Peter T. S. van der Gulik, Steven Kelk, Wouter M. Koolen, Leen Stougie
- IEEE/ACM Transactions on Computational Biology…
- 2011

The genetic code is known to have a high level of error robustness and has been shown to be very error robust compared to randomly selected codes, but to be significantly less error robust than a certain code found by a heuristic algorithm. We formulate this optimization problem as a Quadratic Assignment Problem and use this to formally verify that the code… (More)

- Wouter M. Koolen, Tim van Erven
- COLT
- 2015

We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly… (More)

We consider sequential prediction algorithms that are given the predictions from a set of models as inputs. If the nature of the data is changing over time in that different models predict well on different segments of the data, then adaptivity is typically achieved by mixing into the weights in each round a bit of the initial prior (kind of like a weak… (More)

- Manfred K. Warmuth, Wouter M. Koolen
- COLT
- 2014

A number of online algorithms have been developed that have small additional loss (regret) compared to the best “shifting expert”. In this model, there is a set of experts and the comparator is the best partition of the trial sequence into a small number of segments, where the expert of smallest loss is chosen in each segment. The regret is typically… (More)

231 Curriculum Vitae 235

- Martin Ziegler, Wouter M. Koolen
- Electr. Notes Theor. Comput. Sci.
- 2008

Kolmogorov Complexity constitutes an integral part of computability theory, information theory, and computational complexity theory— in the discrete setting of bits and Turing machines. Over real numbers, on the other hand, the BSS-machine (aka real-RAM) has been established as a major model of computation. This real realm has turned out to exhibit natural… (More)

- Aurélien Garivier, Emilie Kaufmann, Wouter M. Koolen
- COLT
- 2016

We study an original problem of pure exploration in a strategic bandit model motivated by Monte Carlo Tree Search. It consists in identifying the best action in a game, when the player may sample random outcomes of sequentially chosen pairs of actions. We propose two strategies for the fixed-confidence setting: Maximin-LUCB, based on lowerand… (More)