About: Michal Valko

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Michal Valko Goto Sponge NotDistinct Permalink

An Entity of Type : foaf:Person, within Data Space : data.idref.fr associated with source document(s)

Attributes	Values
type	Person
sameAs	Valko, Michal (19..-....) Valko, Michal (19..-....) Valko, Michal (19..-....)
alternative label	Michał, Valko
Life Event	http://www.idref.fr/22360934X/birth
preferred label	Valko, Michal (19..-....)
name	Michal Valko
dbpedia-owl:citizenship	http://sws.geonames.org/3017382/
Language	http://lexvo.org/id/iso639-3/eng http://lexvo.org/id/iso639-3/fra
Given name	Michal
familyName	Valko
has biographical i...tion (Deprecated)	Doctorat en informatique (University of Pittsburgh, 2011). Habilitation à diriger des recherches (Cachan, Val-de-Marne, École normale supérieure, 2016) Informaticien. Chercheur, Centre de recherche de Paris, Google Deepmind. Chargé de recherche INRIA (en détachement à Google Deepmind), membre de l'équipe Sequential learning (SequeL), Inria Lille - Nord Europe, Université de Lille. Maître de conférences, Département mathématiques, École normale supérieure Paris-Saclay (2024)
gender	male
org:hasMembership	http://www.idref.fr/22360934X/affiliation/3 http://www.idref.fr/22360934X/affiliation/1 http://www.idref.fr/22360934X/affiliation/2
org:memberOf	École normale supérieure Paris-Saclay (Gif-sur-Yvette, Essonne ; 1912-....) Centre Inria de l'Université de Lille Google Deepmind. Centre de recherche Paris
Scopus ID	25642222500
is sameAs of	Valko, Michal (19..-....)
is Thesis advisor of	http://www.sudoc.fr/225040549/id http://www.sudoc.fr/253222427/id http://www.sudoc.fr/259602450/id http://www.sudoc.fr/230619002/id http://www.sudoc.fr/259262528/id http://www.sudoc.fr/254149073/id http://www.sudoc.fr/263274624/id
is Author of	Cheap Bandits Adaptive black-box optimization got easier: HCT only needs local smoothness Analysis of Nyström method with sequential ridge leverage score sampling Gamification of pure exploration for linear bandits Maximum Entropy Semi-Supervised Inverse Reinforcement Learning General parallel optimization without a metric Budgeted online influence maximization Exploiting structure of uncertainty for efficient matroid semi-bandits Efficient second-order online kernel learning with adaptive embedding Rotting bandits are not harder than stochastic ones Revealing graph bandits for maximizing local influence Second-Order Kernel Online Convex Optimization with Adaptive Sketching A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption Fixed-confidence guarantees for Bayesian best-arm identification Trading off rewards and errors in multi-armed bandits Adapting to game trees in zero-sum imperfect information games Black-box optimization of noisy functions with unknown smoothness Improved large-scale graph learning through ridge spectral sparsification Online learning with Erdős-Rényi side-observation graphs Large-scale semi-supervised learning with online spectral graph sparsification Kernel-based reinforcement Learning: A finite-time analysis Best of both worlds: Stochastic & adversarial best-arm identification Improved sleeping bandits with stochastic action sets and adversarial rewards Online learning with noisy side observations Zonotope hit-and-run for efficient sampling from projection DPPs Optimistic optimization of a Brownian Fast sampling from beta-ensembles Online influence maximization under independent cascade model with semi-bandit feedback Planning in Markov Decision Processes with Gap-Dependent Sample Complexity DPPy: Sampling Determinantal Point Processes with Python Compressing the Input for CNNs with the First-Order Scattering Transform MESSI: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning Distributed adaptive sampling for kernel matrix approximation Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning Covariance-adapting algorithm for semi-bandits with application to sparse outcomes Fast active learning for pure exploration in reinforcement learning Spectral bandits Simple regret for infinitely many armed bandits Pliable rejection sampling On two ways to use determinantal point processes for Monte Carlo integration -- Long version Statistical efficiency of Thompson sampling for combinatorial semi-bandits
is primary topic of	http://www.idref.fr/22360934X
is Opponent of	http://www.sudoc.fr/241335523/id http://www.sudoc.fr/262426897/id http://www.sudoc.fr/251029085/id http://www.sudoc.fr/275310361/id http://www.sudoc.fr/267145497/id
is Thesis referee of	http://www.sudoc.fr/250987880/id

Faceted Search & Find service v1.13.91 as of Aug 16 2018

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of May 14 2019, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (70 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software