Michal Valko: advisors and students

Academic Ancestry

a person in the record

Michal Valko

Franco-Slovak Artificial Intelligence scientist

Supervised 9 doctoral students at Université de Lille, Inria Lille Nord Europe - Laboratoire CRIStAL - Université de Lille, and Institut Polytechnique de Paris between 2016 and 2025; doctorate at University of Pittsburgh in 2011 under Milos Hauskrecht; Wikidata lists 15 doctoral students. Linked to Wikidata, ORCID, and 10 other registers.

Open the full sheet Cite Data

Two steps up and one down. Dashed lines come from thesis records, solid lines from Wikidata. The full sheet opens at four up and two down.

How to read the markscited by a repository's recordstated by Wikidata or indexed by OpenAlexclaimed on an ORCID list, or inferred

Theses1

cited by a thesis record
Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning
2011 · University of Pittsburgh · doctoral · in English · the thesis's own page ›
also named on this thesis
- cited by a thesis recordMilos Hauskrechtjury member
- cited by a thesis recordJohn Laffertyjury member
- cited by a thesis recordDiane Litmanjury member
- cited by a thesis recordG. Elisabeta Maraijury member
stated by Wikidata
Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning
doctoral thesis, stated by Wikidata
stated by Wikidata
Bandits on Graphs and Structures
doctoral thesis, stated by Wikidata

Advisors1

cited by a thesis recordstated by Wikidata
Milos Hauskrecht“Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning”
2011 · University of Pittsburgh · doctoral

Students9

cited by a thesis record
Lisa Bedin “Avancées dans les Modèles Génératifs : Méthodologies et Applications à la Cardiologie”
2025 · Institut Polytechnique de Paris · doctoral
cited by a thesis record
Omar Darwiche Domingues “Exploration in Reinforcement Learning : Beyond Finite State-Spaces”
2022 · Université de Lille · doctoral
cited by a thesis record
Xuedong Shang “Adaptive methods for optimization in stochastic environments”
2021 · Université de Lille · doctoral
cited by a thesis record
Gautier, Guillaume, Michel, Jean “On sampling determinantal point processes”
2020 · Centrale Lille Institut · doctoral
cited by a thesis record
Pierre Perrault “Apprentissage efficient dans les problèmes de semi-bandits stochastiques combinatoires”
2020 · Université Paris-Saclay · doctoral
cited by a thesis record
Julien Seznec “Sequential machine learning for intelligent tutoring systems”
2020 · Université de Lille · doctoral
cited by a thesis record
Jean-Bastien Grill “Tree search applied to optimization and planning”
2019 · Lille 1 · doctoral
cited by a thesis record
Daniele Calandriello “Efficient Sequential Learning in Structured and Constrained Environments”
2017 · Inria Lille Nord Europe - Laboratoire CRIStAL - Université de Lille · doctoral
cited by a thesis record
Tomáš Kocák “Sequential Learning with Similarities”
2016 · Inria Lille Nord Europe - Laboratoire CRIStAL - Université de Lille · doctoral

Students on Wikidata15

From Wikidata, shown separately from the thesis records.

stated by Wikidata
Pierre Perrault
born 1927
stated by Wikidata
Côme Fiegel
stated by Wikidata
Daniele Calandriello
stated by Wikidata
Daniil Tiapkin
stated by Wikidata
Giorgio Racca
stated by Wikidata
Guillaume Gautier
stated by Wikidata
Guillaume Gautier
stated by Wikidata
Jean Tarbouriech
stated by Wikidata
Jean-Bastien Grill
stated by Wikidata
Julien Seznec
stated by Wikidata
Lisa Bedin
stated by Wikidata
Omar Darwiche Domingues
stated by Wikidata
Pierre Perrault
stated by Wikidata
Tomáš Kocák
stated by Wikidata
Xuedong Shang

The line above and below4 steps

The advisors of their advisors, four steps up at most, in the thesis records and on Wikidata, each kind marked; a person in both is listed once.

1 step up
cited by a thesis recordstated by WikidataMilos Hauskrecht
2 steps up
stated by WikidataPeter Szolovits
3 steps up
stated by WikidataFrederick Burtis Thompson
4 steps up
stated by WikidataAlfred Tarski

Below them, within 3 steps, the record reaches 24 people: 9 cited by thesis records, 15 stated by Wikidata.

Education and career14

From Wikidata, shown separately from the thesis records. Each place opens its Wikidata item.

1992 to 2000
Gymnasium Alejová
studied
2000 to 2005
Comenius University
studied
2003 to 2006
Slovak Academy of Sciences
worked
2005 to 2011
University of Pittsburgh
studied
2005 to 2005
NOVA University Lisbon
studied
2005 to 2011
University of Pittsburgh
worked
2007 to 2009
Carnegie Mellon University
studied
2009 to 2010
Intel
worked
from 2011
Institut National de Recherche en Informatique et en Automatique
worked
from 2014
École normale supérieure Paris-Saclay
worked
2015 to 2016
École normale supérieure Paris-Saclay
studied
2019 to 2024
Google DeepMind
worked
2024 to 2024
Meta Platforms
worked
from 2025
Isara Labs
worked

Works1

2011 · held from 1 repository · a work missing?

cited by HAL/TEL OAI's record
Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning
2011 · doctoral thesis · HAL/TEL OAI

What their ORCID record lists

The works their own ORCID record lists, as they claim them: shown apart from the record's works, never checked or counted with them.

ORCID is asked for the list when this part is opened.

Coauthors in OpenAlex251

People who share works with them in OpenAlex, the most shared first: indexed, and never counted with the lineage.

indexed by OpenAlex
Rémi Munos
47 shared works
indexed by OpenAlex
Alessandro Lazaric
29 shared works
indexed by OpenAlex
Daniele Calandriello
28 shared works
indexed by OpenAlex
Pierre Ménard
23 shared works
indexed by OpenAlex
Yunhao Tang
19 shared works
indexed by OpenAlex
Miloš Hauskrecht
16 shared works
indexed by OpenAlex
Branislav Kveton
14 shared works
indexed by OpenAlex
Mark Rowland
14 shared works
indexed by OpenAlex
Mohammad Gheshlaghi Azar
12 shared works
indexed by OpenAlex
Emilie Kaufmann
11 shared works

OpenAlex lists 241 more people who share a work with them.

Sat beside the lineage8

jury member or referee, not advisor

cited by a thesis record
Pierre Clavier “Robust Reinforcement Learning : Theory and Practice”
jury member · 2024 · Institut Polytechnique de Paris · doctoral
cited by a thesis record
Rémi Jézéquel “Efficient algorithms with fast rates for online learning”
jury member · 2023 · Université Paris sciences et lettres · doctoral
cited by a thesis record
Yoan Russac “Sequential decision problems in non-stationary environments”
jury member · 2022 · Université Paris sciences et lettres · doctoral
cited by a thesis record
Guillaume Salha-Galvan “Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation”
jury member · 2022 · Institut Polytechnique de Paris · doctoral
cited by a thesis record
Jean Tarbouriech “Goal-oriented exploration for reinforcement learning”
jury member · 2022 · Université de Lille · doctoral
cited by a thesis record
Mastane Achab “Contribution à des problèmes statistiques d'ordonnancement et d'apprentissage par renforcement avec aversion au risque”
referee · 2020 · Institut Polytechnique de Paris · doctoral
cited by a thesis record
Stratis Limnios “Etude de Dégénérescence de Graph appliqué à l'Apprentissage Automatique Avancé et Résultats Théoriques relatifs”
jury member · 2020 · Institut Polytechnique de Paris · doctoral
cited by a thesis record
Zhenyu Liao “A random matrix framework for large dimensional machine learning and neural networks”
jury member · 2019 · Université Paris Saclay (COmUE) · doctoral

At a glance

known as: university teacher, computer scientist, researcher, artificial intelligence researcher, entrepreneur, angel investor
fields: machine learning, artificial intelligence, deep learning, game theory, multi-armed bandit, natural language processing, self-supervised learning, large language model, statistics, computer science, reinforcement learning from human feedback, preference learning, Monte Carlo tree search, graph theory, recommender system, online machine learning, kernel method, mathematical optimization
degrees: habilitation to Supervise Research, 2016, Doctor of Philosophy, 2011, Master of Science, 2005
awards: Inria Prime d'excellence scientifique, 2018, Inria Prime d'excellence scientifique, 2014, ICML Top 10 Reviewer Award, 2018, ICML Reviewer Award, 2015, Distinguished Alumni of Comenius University, 2015, Google Best Paper Award, 2010, Homer Warner Award, 2010, Compunetix Best Research Award, 2011, University of Pittsburgh Honors Convocation Recognition, 2009, Andrew Mellon Predoctoral Fellowship, 2008, Forbes Changemakers, 2024, Compunetix Best Research Award, 2008, ICML Outstanding Paper Award, 2023, ASAI AI Social Impact of the Year, 2026
member of: Slovak Mathematicians and Physicists Scientific Society, from 2000, Inria Evaluation Committee, 2014 to 2019, Association for Computing Machinery, Institut National de Recherche en Informatique et en Automatique, 2014 to 2019, European Laboratory for Learning and Intelligent Systems
notable works: Bootstrap your own latent: A new approach to self-supervised Learning, Nash Learning from Human Feedback, BYOL-Explore: Exploration by Bootstrapped Prediction, Large-Scale Representation Learning on Graphs via Bootstrapping, Bandits on Graphs and Structures, Llama 3, Spectral Bandits for Smooth Graph Functions, Finite-Time Analysis of Kernelised Contextual Bandits, Gamification of Pure Exploration for Linear Bandits, The AI Book: Prepare for the future, Adapting to game trees in zero-sum imperfect information games, Human alignment of large language models through online preference optimisation, Generalized preference optimization: A unified approach to offline alignment, Fast rates for maximum entropy exploration, Quantile credit assignment, UCB Momentum Q-learning: Correcting the bias without forgetting, From Dirichlet to Rubin: Optimistic exploration in RL without bonuses, Optimistic posterior sampling for reinforcement learning with few samples, Model-free learning for two-player zero-sum partially observable Markov games, Stochastic shortest path: minimax, parameter-free and towards horizon-free regret, The Llama 3 Herd of Models, A general theoretical paradigm to understand learning from human preferences, Broaden your views for self-supervised video learning, Retrieval-augmented reinforcement learning, Self-Supervised Representation Learning Using Bootstrapped Latent Representations, Reinforcement Learning by Directly Learning an Advantage Function, Reinforcement Learning Using Hindsight to Model Unpredictable Aspects of the Future, Planning in entropy-regularized Markov decision processes and games, Monte-Carlo tree search as regularized policy optimization, Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving, DPPy: Sampling determinantal point processes with Python, Gaussian process optimization with adaptive sketching, Spectral Thompson Sampling, Stochastic Simultaneous Optimistic Optimization, BYOL works even without batch statistics, Local and adaptive mirror descents in extensive-form games, Decoding-time realignment of language models, VA-learning as a more efficient alternative to Q-learning, Curiosity in hindsight: Intrinsic exploration in stochastic environments, Understanding self-predictive learning for reinforcement learning, Demonstration-regularized RL, Unlocking the power of representations in long-term novelty-based exploration, Accelerating Nash learning from human feedback via Mirror Prox, Bayesian policy gradient and actor-critic algorithms, Mine Your Own vieW: Self-supervised learning through across-sample prediction, Maximum Entropy Semi-Supervised Inverse Reinforcement Learning, Conditional Anomaly Detection with Soft Harmonic Functions, Online semi-supervised learning on quantized graphs, Middle-mile logistics through the lens of goal-conditioned reinforcement learning, Revealing graph bandits for maximizing local influence, Influence maximization of informed agents in social networks, Optimal design for reward modeling in RLHF, Understanding the performance gap between online and offline alignment, Preference optimization with multi-sample comparisons, RL-finetuning LLMs from on- and off-policy data with a single algorithm, A new bound on the cumulant generating function of Dirichlet processes, Sharp deviations bounds for Dirichlet weighted sums with application to analysis of Bayesian algorithms, KL-entropy-regularized RL with a generative model is minimax optimal, On the approximation relationship between optimizing ratio of submodular (RS) and difference of submodular (DS) functions, Learning to Act Greedily: Polymatroid Semi-Bandits, The Harder Path: Last iterate convergence for uncoupled Learning in zero-sum games with bandit feedback, rlberry: A Reinforcement Learning Library for Research and Education, Model-free posterior sampling via learning rate randomization, DoMo-AC: Doubly multi-step off-policy actor-critic algorithm, Regularization and variance-weighted regression achieves minimax optimality in linear MDPs: Theory and practice, Half-Hop: A graph upsampling approach for slowing down message passing, Scaling Gaussian process optimization by evaluating a few unique candidates multiple times, Adaptive multi-goal exploration, Marginalized operators for off-policy reinforcement learning, Drop, Swap, and Generate: A self-supervised approach for generating neural activity, A provably efficient sample collection strategy for reinforcement learning, Unifying gradient estimators for meta-reinforcement learning via off-policy evaluation, Fast active learning for pure exploration in reinforcement learning, Revisiting Peng's Q(λ) for modern reinforcement learning, Taylor expansion of discount factors, Online A-optimal design and active linear regression, Kernel-based reinforcement Learning: A finite-time analysis, Game Plan: What AI can do for Football, and What Football can do for AI, A kernel-based approach to non-stationary reinforcement learning in metric spaces, Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited, Sample complexity bounds for stochastic shortest path with a generative model, Adaptive reward-free exploration, Fast sampling from β-ensembles, Density-based bonuses on learned representations for reward-free exploration in deep reinforcement learning, Geometric entropic exploration, Improved sample complexity for incremental autonomous exploration in MDPs, Sampling from a k-DPP without looking at all items, Statistical efficiency of Thompson sampling for combinatorial semi-bandits, Planning in Markov decision processes with gap-dependent sample complexity, Taylor expansion policy optimization, Budgeted online influence maximization, No-regret exploration in goal-oriented reinforcement learning, Improved sleeping bandits with stochastic action sets and adversarial rewards, Stochastic bandits with arm-dependent delays, Near-linear time Gaussian process optimization with adaptive batching and resparsification, Covariance-adapting algorithm for semi-bandits with application to sparse rewards, Fixed-confidence guarantees for Bayesian best-arm identification, Adaptive multi-fidelity optimization with fast learning rates, Derivative-free & order-robust optimisation, A single algorithm for both restless and rested rotting bandits, Reward-free exploration beyond finite-horizon, Spectral Bandits, Multiagent evaluation under incomplete information, Exact sampling of determinantal point processes with sublinear time preprocessing, On two ways to use determinantal point processes for Monte Carlo integration, Scale-free adaptive planning for deterministic dynamics & discounted rewards, Exploiting structure of uncertainty for efficient matroid semi-bandits, A simple dynamic bandit-based algorithm for hyper-parameter tuning, Rotting bandits are not harder than stochastic ones, Active multiple matrix completion with adaptive confidence sets, Finding the bandit in a graph: Sequential search-and-stop, A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption, General parallel optimization without metric, Les processus ponctuels déterminantaux en apprentissage automatique, Optimizing human learning workshop eliciting adaptive sequences for learning (WeASeL), Optimistic optimization of a Brownian, Adaptive black-box optimization got easier: HCT needs only local smoothness, Compressing the input for CNNs with the first-order scattering transform, Improved large-scale graph learning through ridge spectral sparsification, Best of both worlds: Stochastic & adversarial best-arm identification, Efficient second-order online kernel learning with adaptive embedding, Online influence maximization under independent cascade model with semi-bandit feedback, Second-order kernel online convex optimization with adaptive sketching, Zonotope hit-and-run for efficient sampling from projection DPPs, Distributed adaptive sampling for kernel matrix approximation, Trading off rewards and errors in multi-armed bandits, Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning, Pack only the essentials: Adaptive dictionary learning for kernel ridge regression, Rewards and Errors in Multi-armed Bandit for Interactive Education, Pliable rejection sampling, Analysis of Nyström method with sequential ridge leverage scores, Online learning with Erdős-Rényi side-observation graphs, Online learning with noisy side observations, Black-box optimization of noisy functions with unknown smoothness, Simple regret for infinitely many armed bandits, Cheap Bandits, Large-scale semi-supervised learning with online spectral graph sparsification, Efficient Learning by Implicit Exploration in Bandit Problems with Side Observations, Extreme Bandits, Online Combinatorial Optimization with Stochastic Decision Sets and Adversarial Losses, Bandits attack function optimization, Learning from a Single Labeled Face and a Stream of Unlabeled Data, Outlier detection for patient monitoring and alerting, Semi-supervised apprenticeship learning, Identification of microbial and proteomic biomarkers in early childhood caries, Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning, Semi-Supervised Learning with Max-Margin Graph Cuts, Conditional outlier detection for clinical alerting, Online Semi-Supervised Perception: Real-Time Learning without Explicit Feedback, Feature importance analysis for patient management decisions, Distance Metric Learning for Conditional Anomaly Detection, Evidence-based anomaly detection in clinical domains, Feature Selection and Dimensionality Reduction in Genomics and Proteomics, Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers, An analysis of Mirror-Descent Soft Actor-Critic, Language Generation with Replay: A Learning-Theoretic View of Model Collapse, Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier, DiG-bench: Discovery in Games
Wikipedia: in 1 language

stated by WikidataStated by Wikidata, shown apart from the thesis records.

Elsewhere12

identity

stated by WikidataWikipediaMichal Valko
stated by WikidataWikidataQ97454550
cited by a thesis recordORCID0009-0009-2673-0144stated by Wikidata · on their thesis record at HAL/TEL OAI
stated by WikidataVIAF3284171191086658030003
cited by a thesis recordIdRef22360934Xstated by Wikidata · on their thesis record at HAL/TEL OAI
stated by WikidataISNI0000000351150335

papers

stated by WikidataOpenAlexA5106038276
stated by WikidataGoogle ScholarjrazNCQAAAAJ
stated by WikidataDBLP03/5455
stated by WikidataScopus25642222500
stated by WikidataSemantic Scholar1806291

lineage

stated by WikidataMathematics Genealogy Project156876

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Read on 15 November 2026. A citation names the day the record was read, not a version.

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