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- Klaudia JomovÃ¡, Zita Jenisova, +5 authors Michal Valko
- Journal of applied toxicology : JAT
- 2011

Arsenic (As) is a toxic metalloid element that is present in air, water and soil. Inorganic arsenic tends to be more toxic than organic arsenic. Examples of methylated organic arsenicals includeâ€¦ (More)

We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of themâ€¦ (More)

- Alexandra Carpentier, Michal Valko
- ICML
- 2015

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples onlyâ€¦ (More)

- Zheng Wen, Branislav Kveton, Michal Valko
- ArXiv
- 2016

- Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang
- AISTATS
- 2010

This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. Weâ€¦ (More)

- Michal Valko, RÃ©mi Munos, Branislav Kveton, TomÃ¡s KocÃ¡k
- ICML
- 2014

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework isâ€¦ (More)

- Alexandra Carpentier, Michal Valko
- NIPS
- 2014

In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way toâ€¦ (More)

- Michal Valko, Alexandra Carpentier, RÃ©mi Munos
- ICML
- 2013

We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locallyâ€¦ (More)

- TomÃ¡s KocÃ¡k, Gergely Neu, Michal Valko, RÃ©mi Munos
- NIPS
- 2014

We consider online learning problems under a a partial observability model capturing situations where the information conveyed to the learner is between full information and bandit feedback. In theâ€¦ (More)

- Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
- UAI
- 2010

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose aâ€¦ (More)