Research author
Michal Valko
24 AI research papers in the One9Founders library, with summaries and links to original sources.
Papers by Michal Valko
Page 1 of 2Conditional outlier detection for clinical alerting
Milos Hauskrecht, Michal Valko, Shyam Visweswaran, et al.
Distance metric learning for conditional anomaly detection
Michal Valko, Milos Hauskrecht
Revealing graph bandits for maximizing local influence
Alexandra Carpentier, Michal Valko
Trading off rewards and errors in multi-armed bandits
Akram Erraqabi, Alessandro Lazaric, Michal Valko, et al.
Semi-supervised learning with max-margin graph cuts
Branislav Kveton, Michal Valko, Ali Rahimi, et al.
Large-scale semi-supervised learning with online spectral graph sparsification
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Efficient learning by implicit exploration in bandit problems with side observations
Tomas Kocak, Gergely Neu, Michal Valko, et al.
Extreme bandits
Alexandra Carpentier, Michal Valko
Stochastic simultaneous optimistic optimization
Michal Valko, Alexandra Carpentier, Rémi Munos
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Pliable rejection sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier, et al.
On two ways to use determinantal point processes for Monte Carlo integration
Guillaume Gautier, Rémi Bardenet, Michal Valko
Planning in entropy-regularized Markov decision processes and games
Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, et al.
Budgeted Online Influence Maximization
Pierre Perrault, Jennifer Healey, Zheng Wen, et al.
Spectral bandits for smooth graph functions
Michal Valko, Rémi Munos, Branislav Kveton, et al.
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Peter L. Bartlett, Victor Gabillon, Jennifer Healey, et al.
Adaptive multi-fidelity optimization with fast learning rates
Come Fiegel, Victor Gabillon, Michal Valko
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
Jean Tarbouriech, Matteo Pirotta, Michal Valko, et al.
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, et al.
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
Come Fiegel, Pierre Menard, Tadashi Kozuno, et al.