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101 résultats

	Pour les 101 documents Envoyer sur ORCID RSS ATOM Exporter BibTeX XML-TEI CSV RTF EndNote PDF HTML Export avancé	Page : Page précédente 1 2 3 4 Page suivante	triés par Pertinence Auteur A→Z Auteur Z→A Titre A→Z Titre Z→A Date de publication croissante Date de publication décroissante Date de dépôt croissante Date de dépôt décroissante

		A kernel-based approach to non-stationary reinforcement learning in metric spaces Omar D Domingues , Pierre Ménard , Matteo Pirotta , Emilie Kaufmann , Michal Valko International Conference on Artificial Intelligence and Statistics, Apr 2021, San Diego / Virtual, United States Communication dans un congrès hal-03289026v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Budgeted online influence maximization Pierre Perrault , Jennifer Healey , Zheng Wen , Michal Valko International Conference on Machine Learning, 2020, Vienna, Austria Communication dans un congrès hal-02904278v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		DPPy: Sampling Determinantal Point Processes with Python Guillaume Gautier , Rémi Bardenet , Michal Valko Journal of Machine Learning Research, 2019 Article dans une revue hal-01879424v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Reward-free exploration beyond finite-horizon Jean Tarbouriech , Matteo Pirotta , Michal Valko , Alessandro Lazaric ICML 2020 Workshop on Theoretical Foundations of Reinforcement Learning, 2020, Vienna, France Communication dans un congrès hal-03288970v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Stochastic bandits with arm-dependent delays Anne Gael Manegueu , Claire Vernade , Alexandra Carpentier , Michal Valko International Conference on Machine Learning, 2020, Vienna, Austria Communication dans un congrès hal-02950116v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Distance Metric Learning for Conditional Anomaly Detection Michal Valko , Milos Hauskrecht Twenty-First International Florida Artificial Intelligence Research Society Conference, May 2008, Coconut Grove, Florida, United States Communication dans un congrès hal-00643244v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Distributed adaptive sampling for kernel matrix approximation Daniele Calandriello , Alessandro Lazaric , Michal Valko International Conference on Artificial Intelligence and Statistics, 2017, Fort Lauderdale, United States Communication dans un congrès hal-01482760v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Finite-Time Analysis of Kernelised Contextual Bandits Michal Valko , Nathan Korda , Rémi Munos , Ilias Flaounas , Nello Cristianini Uncertainty in Artificial Intelligence, Jul 2013, Bellevue, United States Communication dans un congrès hal-00826946v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Pliable rejection sampling Akram Erraqabi , Michal Valko , Alexandra Carpentier , Odalric-Ambrym Maillard International Conference on Machine Learning, Jun 2016, New York City, United States Communication dans un congrès hal-01322168v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Derivative-free & order-robust optimisation Victor Gabillon , Rasul Tutunov , Michal Valko , Haitham Bou Ammar International Conference on Artificial Intelligence and Statistics, Aug 2020, Palermo / Virtual, Italy Communication dans un congrès hal-03288939v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Learning from a Single Labeled Face and a Stream of Unlabeled Data Branislav Kveton , Michal Valko 10th IEEE International Conference on Automatic Face and Gesture Recognition, Apr 2013, Shanghai, China Communication dans un congrès hal-00749197v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Bandits attack function optimization Philippe Preux , Rémi Munos , Michal Valko IEEE Congress on Evolutionary Computation, Jul 2014, Beijing, China Communication dans un congrès hal-00978637v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Second-Order Kernel Online Convex Optimization with Adaptive Sketching Daniele Calandriello , Alessandro Lazaric , Michal Valko International Conference on Machine Learning, 2017, Sydney, Australia Communication dans un congrès hal-01537799v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Kernel-based reinforcement Learning: A finite-time analysis Omar D Domingues , Pierre Ménard , Matteo Pirotta , Emilie Kaufmann , Michal Valko International Conference on Machine Learning, Jul 2021, Vienna / Virtual, Austria Communication dans un congrès hal-02541790v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		No-regret exploration in goal-oriented reinforcement learning Jean Tarbouriech , Evrard Garcelon , Michal Valko , Matteo Pirotta , Alessandro Lazaric International Conference on Machine Learning, 2020, Vienna / Virtual, Austria Communication dans un congrès hal-03287824v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sampling from a k-DPP without looking at all items Daniele Calandriello , Michał Dereziński , Michal Valko Neural Information Processing Systems, 2020, Montréal, Canada Communication dans un congrès hal-03287832v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Active multiple matrix completion with adaptive confidence sets Andrea Locatelli , Alexandra Carpentier , Michal Valko International Conference on Artificial Intelligence and Statistics, 2019, Okinawa, Japan Communication dans un congrès hal-02387468v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Fixed-confidence guarantees for Bayesian best-arm identification Xuedong Shang , Rianne de Heide , Emilie Kaufmann , Pierre Ménard , Michal Valko International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy Communication dans un congrès hal-02330187v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Gamification of pure exploration for linear bandits Rémy Degenne , Pierre Ménard , Xuedong Shang , Michal Valko ICML 2020 - International Conference on Machine Learning, Aug 2020, Vienna / Virtual, Austria Communication dans un congrès hal-02884330v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting Michal Valko , Hamed Valizadegan , Branislav Kveton , Gregory Cooper , Milos Hauskrecht The 28th International Conference on Machine Learning Workshop on Machine Learning for Global Challenges, Jun 2011, Seattle, United States Communication dans un congrès hal-00642313v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Semi-Supervised Learning with Max-Margin Graph Cuts Branislav Kveton , Michal Valko , Ali Rahimi , Ling Huang International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna, Sardinia, Italy Communication dans un congrès hal-00642891v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Zonotope hit-and-run for efficient sampling from projection DPPs Guillaume Gautier , Rémi Bardenet , Michal Valko International Conference on Machine Learning, 2017, Sydney, Australia Communication dans un congrès hal-01526577v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Planning in Markov Decision Processes with Gap-Dependent Sample Complexity Anders Jonsson , Emilie Kaufmann , Pierre Ménard , Omar D Domingues , Edouard Leurent , et al. Neural Information Processing Systems, 2020, Vancouver, France Communication dans un congrès hal-02863486v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Rotting bandits are not harder than stochastic ones Julien Seznec , Andrea Locatelli , Alexandra Carpentier , Alessandro Lazaric , Michal Valko International Conference on Artificial Intelligence and Statistics, 2019, Naha, Japan Communication dans un congrès hal-01936894v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		A single algorithm for both restless and rested rotting bandits Julien Seznec , Pierre Menard , Alessandro Lazaric , Michal Valko International Conference on Artificial Intelligence and Statistics, Aug 2020, Palermo / Virtual, Italy Communication dans un congrès hal-03287835v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		On two ways to use determinantal point processes for Monte Carlo integration Guillaume Gautier , R. Bardenet , Michal Valko NEGDEPML 2019 - ICML Workshop on Negative Dependence in ML, Jun 2019, Long Beach, CA, United States Communication dans un congrès hal-02160382v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Bandits on graphs and structures Michal Valko Machine Learning [stat.ML]. École normale supérieure de Cachan - ENS Cachan, 2016 HDR tel-01359757v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Maximum Entropy Semi-Supervised Inverse Reinforcement Learning Julien Audiffren , Michal Valko , Alessandro Lazaric , Mohammad Ghavamzadeh International Joint Conference on Artificial Intelligence, Jul 2015, Bueons Aires, Argentina Communication dans un congrès hal-01146187v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Online A-optimal design and active linear regression Xavier Fontaine , Pierre Perrault , Michal Valko , Vianney Perchet International Conference on Machine Learning, Jul 2021, Vienna / Virtual, Austria Communication dans un congrès hal-03289318v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Adaptive multi-fidelity optimization with fast learning rates Côme Fiegel , Victor Gabillon , Michal Valko International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy Communication dans un congrès hal-03288879v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More

Filtrer vos résultats

A kernel-based approach to non-stationary reinforcement learning in metric spaces

Budgeted online influence maximization

DPPy: Sampling Determinantal Point Processes with Python

Reward-free exploration beyond finite-horizon

Stochastic bandits with arm-dependent delays

Distance Metric Learning for Conditional Anomaly Detection

Distributed adaptive sampling for kernel matrix approximation

Finite-Time Analysis of Kernelised Contextual Bandits

Pliable rejection sampling

Derivative-free & order-robust optimisation

Learning from a Single Labeled Face and a Stream of Unlabeled Data

Bandits attack function optimization

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Kernel-based reinforcement Learning: A finite-time analysis

No-regret exploration in goal-oriented reinforcement learning

Sampling from a k-DPP without looking at all items

Active multiple matrix completion with adaptive confidence sets

Fixed-confidence guarantees for Bayesian best-arm identification

Gamification of pure exploration for linear bandits

Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting

Semi-Supervised Learning with Max-Margin Graph Cuts

Zonotope hit-and-run for efficient sampling from projection DPPs

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity

Rotting bandits are not harder than stochastic ones

A single algorithm for both restless and rested rotting bandits

On two ways to use determinantal point processes for Monte Carlo integration

Bandits on graphs and structures

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning

Online A-optimal design and active linear regression

Adaptive multi-fidelity optimization with fast learning rates