Education and degrees
2009
defense: 2008
defense: 2008
Ph.D. in Computer Science, Faculty of Informatics, ELTE (Eötvös Loránd University)
Thesis: Adaptive Resource Control: Machine Learning Approaches to Resource Allocation in Uncertain and Changing Environments
Supervisor: László Monostori (SZTAKI and BME)
2006
M.A. in Philosophy, Faculty of Humanities, ELTE (Eötvös Loránd University)
Thesis: The Problems of Judgment Aggregation: On the Limits of Rational Collective Decision Making
Supervisor: Miklós Rédei (London School of Economics and Political Science, UK)
2001
M.Sc. in Mathematics & Computer Science*, Faculty of Science, ELTE (Eötvös Loránd University)
Thesis: Approximation with Artificial Neural Networks (machine learning and wavelet analysis) — 1000+ citations
Supervisor: Huub ten Eikelder (Eindhoven University of Technology, Netherlands)
* In Hungarian: programtervező matematikus (verbatim: “program-designer mathematician”).
Fields of interest
Applied
mathematics
mathematics
Mathematical statistics & Operations research
regression, nonparametric inference, time series analysis; stochastic approximation; uncertain optimization
regression, nonparametric inference, time series analysis; stochastic approximation; uncertain optimization
Computer
science
science
Machine learning & Distributed systems
statistical learning theory, kernel methods, reinforcement learning, stochastic bandits; randomized algorithms
statistical learning theory, kernel methods, reinforcement learning, stochastic bandits; randomized algorithms
Control
theory
theory
System identification & Robust control
finite sample methods, recursive identification; stochastic, robust and adaptive control; model predictive control
finite sample methods, recursive identification; stochastic, robust and adaptive control; model predictive control
Analytic
philosophy
philosophy
Philosophy of science & Formal logic
problem of induction, interpretations of probability, philosophy of mathematics; metatheory
problem of induction, interpretations of probability, philosophy of mathematics; metatheory
Positions and research visits
Appointments and employment
2021 –
Adjunct professor (part-time), ELTE, Department of Probability Theory and Statistics, Institute of Mathematics, Budapest
2004 –
Senior researcher (Research fellow until 2009), HUN-REN SZTAKI (formerly MTA SZTAKI), EMI Research Laboratory, Budapest
2009 – 2012
ARC research fellow, 3 years, University of Melbourne, Department of Electrical and Electronic Engineering, Australia
2008 – 2009
Postdoctoral researcher, 8 months, Université catholique de Louvain, Department of Mathematical Engineering, Belgium
2001 – 2004
Ph.D. student on a state scholarship, ELTE, Faculty of Informatics, Budapest
2002
Research intern (via IAESTE), 3 months, British Telecom, BTexact Technologies, Radical Multimedia Lab, United Kingdom
Study and exchange scholarships
2003
CEEPUS scholarship, 4 months, Johannes Kepler Universität, Institute for Applied Knowledge Processing, Austria
2001
ERASMUS scholarship, 5 months, Eindhoven University of Technology, Faculty of Mathematics and Computing Science, Netherlands
Short-term research visits
2025
Nanyang Technological University, Singapore Centre for 3D Printing, Singapore
2023
Hitachi Yokohama Research Laboratory, Production Engineering Laboratory, Japan
2012
Università degli Studi di Brescia, Department of Information Engineering, Italy
2012
Universität Paderborn, Faculty of Electrical Engineering, Computer Science and Mathematics, Germany
2009
Université de Liège, Department of Electrical Engineering and Computer Science, Belgium
2009
Università della Svizzera italiana, Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
2008
University of Edinburgh, Institute of Perception, Action and Behaviour, United Kingdom
2008
University College London, Gatsby Computational Neuroscience Unit, United Kingdom
2007
University of Alberta, Alberta Ingenuity Centre for Machine Learning, Canada
Teaching
2024 –
Data Mining and Machine Learning (modeling block, with András Lukács), lectures for B.Sc. students, ELTE Inst. of Math.
2023 –
Statistical Learning Theory and Kernel Methods, lectures for M.Sc. and Ph.D. students, ELTE Inst. of Math.
2022 –
Mathematical Statistics (probability major block), lectures and seminars for B.Sc. students, ELTE Inst. of Math.
2021 –
Probability Theory II (probability major block), seminars for B.Sc. students, ELTE Inst. of Math.
2019 –
Markov Decision Processes and Reinforcement Learning, for M.Sc. and Ph.D. students, BME Inst. of Math., and ELTE since 2022
2019 – 2022
Mathematical Foundations of Machine Learning, for M.Sc. and Ph.D. students, ELTE Inst. of Math.
2015 – 2021
Stochastic Models and Adaptive Algorithms, Doctoral School of Computer Science,
ELTE, and since 2017, BME
2013 – 2014
Mathematical Optimization, main organizer and lecturer, seminar series, SZTAKI
2012
Probability and Random Models (ELEN90054, with Girish Nair), School of Engineering, University of Melbourne, Australia
2005 – 2006
Markov Decision Processes, regular speaker, internal seminar series organized by Csaba Szepesvári, SZTAKI
2002
Theory of Operating Systems, seminars, ELTE, Department of Information Systems
2000 – 2002
Programming Methodology, seminars, ELTE, Department of Software Technology
Supervision
Current Ph.D. students
2026 –
Balázs Szabados, ELTE, Doctoral School of Mathematics
Statistical learning theory, kernel methods, explainability
2025 –
Viktor Lázár, ELTE, Doctoral School of Computer Science
Unsupervised learning, dimensionality reduction, uncertainty quantification
2024 –
Balázs Miavecz, ELTE, Doctoral School of Mathematics
Reinforcement learning, robust linear and convex optimization
Completed doctorates
2026
Bálint Horváth, BME, Doctoral School of Mathematics and Computer Science
Thesis: Nonparametric Simultaneous Confidence Bands with Paley–Wiener Kernels; distinction: summa cum laude
Awards: Best Ph.D. Student Award (2×), SZTAKI, 2020 and 2022
2026
Szabolcs Szentpéteri, ELTE, Doctoral School of Computer Science
Thesis: Distribution-Free System Identification with Non-Asymptotic Guarantees; distinction: summa cum laude
Awards: Best Ph.D. Student Award (2×), SZTAKI, 2023 and 2024; Young Researchers' Institute Award, SZTAKI, 2025; Gyula Farkas Memorial Prize, BJMT (János Bolyai Mathematical Society), 2026
2025
Ambrus Tamás, ELTE, Doctoral School of Mathematics
Thesis: Stochastic Guarantees for Statistical Learning Methods; distinction: summa cum laude
Awards: Best Ph.D. Student Award, SZTAKI, 2021; Young Researchers' Institute Award, SZTAKI, 2024; Cooperative Doctoral Programme (KDP); Gyula Farkas Memorial Prize, BJMT (János Bolyai Mathematical Society), 2026
Research projects
Project leadership (principal investigator or institutional lead)
2026 – 2030
Robust Uncertainty Quantification for Learning and Control, PI, ADVANCED research project, no. 153390, NKFIH
2017 – 2019
Markov Decision Processes: Estimation and Approximation Methods, PI, KH_17 research project, no. 125698, NKFIH
2014 – 2016
Analytical Module for a Wireless Multi-Sensor Network, PI, industrial project commissioned by GE Lighting
2011 – 2012
Distribution-Free System Identification, PI, DECRA project, no. DE120102601, ARC (Australian Research Council)
2005 – 2009
Coll-Plexity: Collaborations as Complex Systems, project manager for SZTAKI, FP6-2003-NEST-Path-012781, EU
Major project participation
2020 – 2025
Artificial Intelligence National Laboratory (task: mathematical foundations of AI), RRF-2.3.1-21-2022-00004, EU
2020 – 2025
Autonomous Systems National Laboratory (task: identification and control), RRF-2.3.1-21-2022-00002, EU
2022 – 2025
COPROLOGS: Cooperative Production and Logistics Systems (task A1.2: predictive methods), TKP2021-NKTA-01, NKFIH
2021 – 2024
AIDPATH: Artificial Intelligence-driven, Decentralized Production for Advanced Therapies in the Hospital (task: RL), EU H2020
2018 – 2023
INEXT: Research on the Exploitation of the Potential Provided by Industrial Digitalisation (task A.3: ML), NKFIH
2018 – 2021
Exploring the Mathematical Foundations of Artificial Intelligence (task: UQ, SA), 2018-1.2.1-NKP-2018-0008, NKFIH
2018 – 2019
BioManu-II: Biologicalisation in Manufacturing (task: automated stem cell production), Fraunhofer-Gesellschaft, Germany
2011 – 2014
E+Grid: An Embedded System for Optimizing Energy-Positive Public Lighting (task: MPC), NFÜ (National Development Agency)
2009 – 2011
Algorithms for Change Detection Based on Finite Sample System Identification, ARC (Australian Research Council)
2008 – 2010
Production Structures as Complex Adaptive Systems, OTKA
2005 – 2007
Modeling, Planning and Control of Distributed, Modular Production Structures (task: stochastic resource allocation), OTKA
2004 – 2007
VITAL: Real-Time, Cooperative Enterprises (task: scheduling), NKFP
2004 – 2006
MultiSens: Cameras as Multifunctional Sensors for Automated Processes, 6th Framework Programme, EU
2000 – 2004
MPA: Modular Plant Architecture, Growth Programme, 5th Framework Programme, EU
Awards, honors and scholarships
Honors and awards
2026
Keynote speaker, 25th European Young Statisticians Meeting (EYSM), University of Vilnius, Lithuania, Bernoulli Society
2022, 2024
Best Ph.D. Supervisor Award (2×), SZTAKI (Institute for Computer Science and Control)
2019
Plenary speaker, 33rd Hungarian Conference on Operations Research (MOK), Szeged, MOT (Hung. Op. Res. Soc.)
2016
Béla Gyires Prize (applied mathematics), Section of Mathematics, MTA (Hungarian Academy of Sciences)
2016, 2020
Bolyai Certificate of Merit (2×, for the outstanding results of the 1st and 2nd Bolyai fellowships), MTA
2013
Outstanding Reviewer, IEEE Transactions on Automatic Control, editorial board of TAC, IEEE Control Systems Society
2011
Discovery Early Career Researcher Award (DECRA, applied mathematics), ARC (Australian Research Council), Australia
2009
Finalist of the Cor Baayen Award (top 5), ERCIM (European Research Consortium for Informatics and Mathematics)
2009
Young Researchers' Award (mathematical sciences), MTA (Hungarian Academy of Sciences)
2009, 2016,
2018, 2025
2018, 2025
Publication Award (4×), SZTAKI (Institute for Computer Science and Control)
2006
Best Paper Award, 6th International Workshop on Emergent Synthesis (IWES), University of Tokyo, Japan
2006
Young Researchers' Institute Award, SZTAKI (Institute for Computer Science and Control)
2004
Best Ph.D. Student Award, SZTAKI (Institute for Computer Science and Control)
2004, 2009,
2015
2015
Institute Award (3×), SZTAKI (Institute for Computer Science and Control)
2000
First Prize, Scientific Students' Conference (TDK), section of informatics, ELTE
Grants and scholarships
2020 – 2024
Supervisor, Cooperative Doctoral Programme (Ambrus Tamás), NKFIH
2012 – 2015
2016 – 2019
2016 – 2019
János Bolyai Research Fellowship (2×), MTA (Hungarian Academy of Sciences)
2011 – 2013
ARC DECRA Fellowship, ARC (Australian Research Council), Australia
2004 – 2007
Young Researcher Scholarship, MTA (Hungarian Academy of Sciences)
2001 – 2004
Ph.D. Scholarship, ELTE, Doctoral School of Computer Science
2000 – 2001
Research Scholarship, Pázmány–Eötvös Foundation
Professional memberships
2025 –
ELLIS (European Laboratory for Learning and Intelligent Systems)
Research area: machine learning theory
2023 –
MTA (Hungarian Academy of Sciences)
Member of the public body, Section of Mathematics
Scientific Committee on Operations Research (with voting right)
2019 –
BJMT (János Bolyai Mathematical Society)
Vice Chair of the Section of Applied Mathematics
2015 –
MOT (Hungarian Operations Research Society)
2014 –
IFAC (International Federation of Automatic Control)
Technical Committee 1.1: Modelling, Identification and Signal Processing
Technical Committee 1.2: Adaptive and Learning Systems
Technical Committee 1.6 (formerly 5.4): Large Scale Complex Systems
2013 –
IEEE (Institute of Electrical and Electronics Engineers)Senior Member, Control Systems Society
Technical Committee on System Identification and Adaptive Control
Academic service
Editorial board
2017 –
Alkalmazott Matematikai Lapok (Journal of Applied Mathematics), Section of Mathematics, MTA (Hungarian Academy of Sciences)
International program committees
2027
International Program Committee, 21st IFAC Symposium on System Identification (SYSID), Lyon, France
2022
International Program Committee, 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS), Casablanca, Morocco
2021
International Program Committee, 17th IFAC Symposium on Information Control Problems in Manufacturing, Budapest
2019
International Program Committee, 15th IFAC Symposium on Large Scale Complex Systems, Delft, Netherlands
2019
International Program Committee, 13th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS), Winchester, UK
Conference organization and chairing
2024
Organizer of the Industrial Mathematics session, BJMT Applied Mathematics Conference, Szeged
2023
Session chair: System Identification (regular session), 22nd IFAC World Congress, Yokohama, Japan
2018
Minisymposium organizer: Finite-Sample System Identification, 20th Eur. Conf. on Mathematics for Industry (ECMI), Budapest
2018
Session chair: Estimation IV (regular session), 57th IEEE Conference on Decision and Control (CDC), Miami Beach, Florida, USA
2016
Organizing committee, BJMT Applied Mathematics Conference, Győr
2015
Session co-chair: System Identification (regular session), 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan
2008
Special session organizer (with László Monostori): Complex Adaptive Systems, 17th IFAC World Congress, Seoul, South Korea
Reviewing
Journals
Regular reviewer for JMLR (Journal of Machine Learning Research), IEEE TAC (Transactions on Automatic Control), Automatica, SICON (SIAM Journal on Control and Optimization), and IEEE TSP (Transactions on Signal Processing).
full list
IEEE Transactions on Automation Science and Engineering · IEEE Journal of Selected Topics in Signal Processing · IEEE Transactions on Signal and Information Processing over Networks · IEEE Transactions on Wireless Communications · Systems & Control Letters · IEEE Control Systems Letters · Control Systems · Journal of Process Control · Information Sciences · Advanced Engineering Informatics · Discrete Applied Mathematics · Internet Mathematics · Journal of Mathematics in Industry · Journal of the Operational Research Society · Omega: The International Journal of Management Science · European Journal of Industrial Engineering · Central European Journal of Operations Research · Asia-Pacific Journal of Operational ResearchConferences
Program committees and reviewing for NeurIPS, ICML, ICLR, AISTATS, UAI, IJCAI, the IEEE CDC (Conference on Decision and Control), the IFAC World Congress and IFAC SYSID (Symposium on System Identification).
full list
American Control Conference (ACC) · European Control Conference (ECC) · Australian Control Conference (AUCC) · IEEE Conference on Control Technology and Applications (CCTA) · International Symposium on Mathematical Theory of Networks and Systems (MTNS) · IEEE International Conference on Communications (ICC) · IEEE Symposium Series on Computational Intelligence (SSCI) · International Conference on Control, Decision and Information Technologies (CoDIT)Grants
Proposal reviewer for the MTA (Hungarian Academy of Sciences, Momentum and János Bolyai Research Fellowships), the NKFIH (National Research, Development and Innovation Office, OTKA and other research grants), the ANR (Agence Nationale de la Recherche, France), the ISF (Israel Science Foundation) and the ARC (Australian Research Council).
Bibliometrics
100+All publications
40Journal articles
54Conference & workshop papers
8Book chapters,
LNCS / LNAI
LNCS / LNAI
27Q1 papers
18D1 papers
1000+Independent citations (MTMT)
3000+Google Scholar citations
22h-index
40i10-index
30+Invited talks
2Erdős number
A citation is independent if no author of the citing paper is an author of the cited one.
Selected publications
Preprints
preprint
Tamás, A.; Csáji, B. Cs.: Resampled Confidence Regions with Exponential Shrinkage for Binary Classification
Selected journal papers
2026
Csáji, B. Cs.; Györfi, L.; Tamás, A.; Walk, H.: On Rate-Optimal Partitioning Classification from Observable and from Privatised Data, Transactions on Machine Learning Research (TMLR), 2026; with J2C (Journal-to-Conference) certification also presented at the 40th Annual Conference on Neural Information Processing Systems (NeurIPS) 2026.
2026
Carè, A.; Csáji, B. Cs.; Gerencsér, B.; Gerencsér, L.; Rásonyi, M.: Stochastic Approximation in a Markovian Framework Revisited: Lipschitz Continuity of the Poisson Equation, Mathematics of Control, Signals, and Systems (MCSS), Special Issue:
Data-Driven Systems and Control: Analysis, Modelling, Optimisation, and Stochasticity,
Springer, Vol. 38, 2026, pp. 291–333.
2025
Szentpéteri, Sz.; Csáji, B. Cs.: Finite Sample Analysis of Distribution-Free Confidence Ellipsoids for Linear Regression, IEEE Transactions on Signal Processing, Vol. 73, 2025, pp. 2896–2911.
2025
Carè, A.; Weyer, E.; Csáji, B. Cs.; Campi, M. C.: Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency, SIAM Journal on Control and Optimization (SICON), Vol. 62, No. 3, 2025, pp. 1902–1928.
2025
Horváth, B.; Csáji, B. Cs.: Single Image Inpainting and Super-Resolution with Simultaneous Uncertainty Guarantees by Universal Reproducing Kernels, Machine Learning, Springer Nature, Vol. 114, 2025.
2025
Szentpéteri, Sz.; Csáji, B. Cs.: Sample Complexity of the Sign-Perturbed Sums Method, Automatica, Elsevier, Vol. 178, August 2025, Paper number: 112020.
2024
Tamás, A.; Csáji, B. Cs.: Recursive Estimation of Conditional Kernel Mean Embeddings, Journal of Machine Learning Research (JMLR), Vol. 25, No. 264, 2024, pp. 1–35.
2023
Szentpéteri, Sz.; Csáji, B. Cs.: Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables, Systems & Control Letters, Elsevier, Vol. 178, 2023, Paper number: 105565.
2022
Csáji, B. Cs.; Horváth, B.: Nonparametric, Nonasymptotic Confidence Bands with Paley–Wiener Kernels for Band-Limited Functions, IEEE Control Systems Letters, Vol. 6, 2022, pp. 3355–3360.
2019
Csáji, B. Cs.; Kis, K. B.: Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations, Machine Learning, Springer Nature, Special issue of ECML PKDD journal track, Vol. 108, 2019, pp. 1677–1699.
2017
Weyer, E.; Campi, M. C.; Csáji, B. Cs.: Asymptotic Properties of SPS Confidence Regions, Automatica, Elsevier, Vol. 82, August 2017, pp. 287–294.
5 more selected journal papers
2017
Carè, A.; Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Finite-Sample System Identification: An Overview and a New Correlation Method, IEEE Control Systems Letters, Vol. 2, No. 1, 2017, pp. 61–66.
2015
Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Sign-Perturbed Sums: A New System Identification Approach for Constructing Exact Non-Asymptotic Confidence Regions in Linear Regression Models, IEEE Transactions on Signal Processing, IEEE Press, Vol. 63, 2015, pp. 169–181.
2014
Csáji, B. Cs.; Jungers, R. M.; Blondel, V. D.: PageRank Optimization by Edge Selection, Discrete Applied Mathematics (DAM), Elsevier, Vol. 169, 2014, pp. 73–87.
2008
Csáji, B. Cs.; Monostori, L.: Adaptive Stochastic Resource Control: A Machine Learning Approach, Journal of Artificial Intelligence Research (JAIR), AAAI Press, Vol. 32, 2008, pp. 453–486.
2008
Csáji, B. Cs.; Monostori, L.: Value Function Based Reinforcement Learning in Changing Markovian Environments, Journal of Machine Learning Research (JMLR), Vol. 9, 2008, pp. 1679–1709.
Selected conference papers
2026
Szentpéteri, Sz.; Kovács, P.; Csáji, B. Cs.: Non-Asymptotic Confidence Regions for Separable Nonlinear Models, 65th IEEE Conference on Decision and Control (CDC), Honolulu, Hawaii, 2026 (accepted).
2025
Tamás, A.; Szentpéteri, Sz.; Csáji, B. Cs.: Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits, 28th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Mai Khao, Phuket, Thailand, 2025.
2024
Tamás, A.; Szentpéteri, Sz.; Csáji, B. Cs.: Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions, 27th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, 2024,
2024
Szentpéteri, Sz.; Kis, K. B.; Egri, P.; Sanges, C.; Danhof, S.; Mestermann, K.; Hudecek, M.; Navarro Velázquez, S.; Juan, M.; Csáji, B. Cs.: Reinforcement Learning Based Resource Management for CAR T-Cell Therapies, 6th CIRP Conference on Biomanufacturing (BioM), Dresden, Germany, 2024, Procedia CIRP, Elsevier.
2023
Horváth, B.; Csáji, B. Cs.: Nonparametric Simultaneous Confidence Bands: The Case of Known Input Distributions, 23rd European Young Statisticians Meeting (EYSM), Bernoulli Society, Ljubljana, Slovenia, 2023.
2023
Szentpéteri, Sz.; Csáji, B. Cs.: Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case, 22nd IFAC World Congress (International Federation of Automatic Control), Yokohama, Japan, 2023.
2023
Csáji, B. Cs.; Horváth, B.: Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands, 22nd IFAC World Congress (International Federation of Automatic Control), Yokohama, Japan, 2023.
2021
Kis, K. B.; Csempesz, J.; Csáji, B. Cs.: A Simultaneous Localization and Mapping Algorithm for Sensors with Low Sampling Rate and its Application to Autonomous Mobile Robots, 10th CIRP Conf. on Digital Enterprise Tech., 2021, pp. 154–159.
2020
Csáji, B. Cs.; Kis, K. B.; Kovács, A.: A Sampling-and-Discarding Approach to Stochastic Model Predictive Control for Renewable Energy Systems, 21st IFAC World Congress, 2020, pp. 7142–7147.
2019
Csáji, B. Cs.; Tamás, A.: Semi-Parametric Uncertainty Bounds for Binary Classification, 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019, pp. 4427–4432.
20 more selected conference papers
2019
Carè, A.; Csáji, B. Cs.; Gerencsér, B.; Gerencsér, L.; Rásonyi, M.: Parameter-Dependent Poisson Equations: Tools for Stochastic Approximation in a Markovian Framework, 58th IEEE Conf. on Decision and Control (CDC), Nice, France, 2019.
2018
Gerencsér, L.; Csáji, B. Cs.; Sabanis, S.: Asymptotic Analysis of the LMS Algorithm with Momentum, 57th IEEE Conference on Decision and Control (CDC), Miami Beach, Florida, 2018, pp. 3062–3067.
2018
Kolumbán, S.; Csáji, B. Cs.: Towards D-Optimal Input Design for Finite-Sample System Identification, 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, 2018, pp. 215–220.
2018
Csáji, B. Cs.: Non-Asymptotic Confidence Regions for Regularized Linear Regression Estimates, 20th European Conference on Mathematics for Industry (ECMI), Finite-Sample System Identification, Budapest, 2018, Springer, pp. 605–611.
2017
Carè, A.; Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Undermodelling Detection with Sign-Perturbed Sums, 20th IFAC World Congress, Toulouse, France, 2017, pp. 2799–2804.
2016
Carè, A.; Csáji, B. Cs.; Campi, M. C.: Sign-Perturbed Sums (SPS) with Asymmetric Noise: Robustness Analysis and Robustification Techniques, 55th IEEE Conference on Decision and Control (CDC), Las Vegas, Nevada, 2016, pp. 262–267.
2016
Csáji, B. Cs.: Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models, 19th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Cádiz, Spain, 2016.
2015
Volpe, V.; Csáji, B. Cs.; Carè, A.; Weyer, E.; Campi, M. C.: Sign-Perturbed Sums (SPS) with Instrumental Variables for the Identification of ARX Systems, 54th IEEE Conf. on Decision and Control (CDC), Osaka, Japan, 2015, pp. 2115–2120.
2015
Csáji, B. Cs.; Weyer, E.: Closed-Loop Applicability of the Sign-Perturbed Sums Method, 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, 2015, pp. 1441–1446.
2014
Csáji, B. Cs.; Kovács, A.; Váncza, J.: Adaptive Aggregated Predictions for Renewable Energy Systems, IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL, SSCI), Orlando, Florida, 2014, pp. 132–139.
2014
Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Strong Consistency of the Sign-Perturbed Sums Method, 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, California, 2014, pp. 3352–3357.
2013
Weyer, E.; Csáji, B. Cs.; Campi, M. C.: Guaranteed Non-Asymptotic Confidence Ellipsoids for FIR Systems, 52nd IEEE Conference on Decision and Control (CDC), Florence, Italy, 2013, pp. 7162–7167.
2012
Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Sign-Perturbed Sums (SPS): A Method for Constructing Exact Finite-Sample Confidence Regions for General Linear Systems, 51st IEEE Conf. on Decision and Control (CDC), Maui, Hawaii, 2012, pp. 7321–7326.
2012
Campi, M. C.; Csáji, B. Cs.; Garatti, S.; Weyer, E.: Certified System Identification: Towards Distribution-Free Results, 16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, 2012, pp. 245–255.
2012
Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Non-Asymptotic Confidence Regions for the Least-Squares Estimate, 16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, 2012, pp. 227–232.
2012
Csáji, B. Cs.; Weyer, E.: Recursive Estimation of ARX Systems Using Binary Sensors with Adjustable Thresholds, 16th IFAC Symposium on System Identification (SYSID), Brussels, Belgium, 2012, pp. 1185–1190.
2011
Csáji, B. Cs.; Weyer, E.: System Identification with Binary Observations by Stochastic Approximation and Active Learning, 50th IEEE Conf. on Decision and Control (CDC) and European Control Conf. (ECC), Orlando, Florida, 2011, pp. 3634–3639.
2010
Ivanov, T.; Csáji, B. Cs.: Reproducing Kernels Preserving Algebraic Structure: A Duality Approach, 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS), Budapest, 2010, pp. 1161–1167.
2010
Csáji, B. Cs.; Jungers, R. M.; Blondel, V. D.: PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation, 21st Int. Conf. on Algorithmic Learning Theory (ALT), Canberra, Australia, 2010, pp. 89–103.
2006
Csáji, B. Cs.; Monostori, L.: Adaptive Sampling Based Large-Scale Stochastic Resource Control, 21st National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, 2006, pp. 815–820.
Selected papers in Hungarian
2024
Tamás, A.; Csáji, B. Cs.: Statisztikus tanuláselmélet I: Szupport vektor gépek (Statistical Learning Theory I: Support Vector Machines), Érintő: Elektronikus Matematikai Lapok, János Bolyai Mathematical Society, Vol. 31, 2024.
2021
Csáji, B. Cs.: Antirealizmus a matematikában (Anti-Realism in Mathematics), Érintő: Elektronikus Matematikai Lapok, János Bolyai Mathematical Society, Vol. 22, 2021.
2020
Tamás, A.; Csáji, B. Cs.: Sztochasztikus garanciák bináris klasszifikációhoz (Stochastic Guarantees for Binary Classification), Alkalmazott Matematikai Lapok, Hungarian Academy of Sciences, Vol. 37, No. 2, 2020.
2019
Csáji, B. Cs.: Szimmetria és konfidencia (Symmetry and Confidence), Alkalmazott Matematikai Lapok, Hungarian Academy of Sciences, Vol. 36, No. 2, 2019, pp. 271–278.
2011
Csáji, B. Cs.; Rédei, M.: A racionális demokratikus véleményösszegzés korlátairól (On the Limits of Rational Democratic Judgment Aggregation), Magyar Filozófiai Szemle, Vol. 55, No. 2, 2011, pp. 97–121.
The complete list of publications is available in the MTMT database and on Google Scholar.
Selected invited talks
2026
Robust Uncertainty Quantification: From Resampling and Ranking to Stochastic Bandits, keynote, 25th European Young Statisticians Meeting (EYSM), Bernoulli Society for Math. Statistics and Probability,
Vilnius University, Lithuania, July 8, 2026.
2026
Robust Uncertainty Quantification for Learning and Control, HUN-REN AI Symposium (Scaling Intelligent Solutions for Tomorrow's Challenges), Budapest University of Technology and Economics (BME), Budapest, May 22, 2026.
2026
Resampled Median-of-Means for Heavy-Tailed Bandits, Artificial Intelligence from a Mathematical Perspective, Section of Mathematics, Hungarian Academy of Sciences (MTA), part of the MTA 200 Bicentenary Celebration Series, Ceremonial Hall of the Hungarian Academy of Sciences, Main Building of MTA, Budapest, January 6, 2026.
2025
A Sampling-and-Discarding Approach to Stochastic Model Predictive Control, Seminar of the Singapore Centre for 3D Printing, Nanyang Technological University (NTU), Singapore, October 10, 2025.
2025
Robust Inference with Kernels, Hungarian Machine Learning Meeting, Organized by the Artificial Intelligence National Laboratory (MILAB), European Youth Centre, Budapest, August 12, 2025.
2025
Resampled Median-of-Means for Heavy-Tailed Bandits, Exploring Uncertainty: Stochastic Systems, Risk Management, and Machine Learning, on the occasion of György Michaletzky's 75th birthday, ELTE Institute of Mathematics and SZTAKI (Institute for Computer Science and Control), Budapest, June 18, 2025.
2025
Robust Uncertainty Quantification (nonparametric, distribution-free, nonasymptotic confidence bands), AI Symposium, jointly organized by HUN-REN and the Nanyang Technological University (NTU), Várkert Bazár, Budapest, May 22, 2025.
2024
Certified Machine Learning: From Mathematical Foundations to Applications, Extracting Actionable Knowledge in the Presence of Uncertainty, joint meeting of ERCIM and the Japan Science and Technology Agency, SZTAKI, Budapest, October 14, 2024.
2024
Distribution-Free Uncertainty Quantification for Band-Limited Functions, Probability and Statistics Seminar, ELTE Institute of Mathematics, Budapest, April 12, 2024.
2023
The Role of Kernels in the Mathematical Foundations of Machine Learning, The Many Faces of Operations Research, Hungarian Science Festival series, Section of Mathematics, Scientific Committee on Operations Research, Hungarian Academy of Sciences (MTA), Budapest, November 22, 2023.
22 more selected talks
2023
Machine Learning: From Theory to Practice, seminar at the Production Engineering Laboratory, Hitachi Yokohama Research Laboratory, Yokohama, Japan, July 14, 2023.
2023
The Huge Potential of AI in CAR-T Cell Therapies, Artificial Intelligence and Autonomous Systems Expo, Artificial Intelligence and Autonomous Systems National Laboratories, Hungarian Railway Museum, Budapest, February 14, 2023.
2022
Distribution-Free Guarantees for Kernel Methods, Mathematical Modeling Seminar, BME Institute of Mathematics, Budapest, September 6, 2022.
2022
On the Mathematical Foundations of Machine Learning, General Assembly of the János Bolyai Mathematical Society, Alfréd Rényi Institute of Mathematics, Budapest, May 7, 2022.
2021
Distribution-Free Inference by Resampling, Beauty of Stochastics: Workshop in Honour of László Gerencsér's 75th Birthday, SZTAKI, Budapest, October 18, 2021.
2021
Uncertainty Quantification and Kernels: Distribution-Free Inference for Regression and Classification, Deep Learning Seminar, Artificial Intelligence National Laboratory, Alfréd Rényi Institute of Mathematics, Budapest, June 23, 2021.
2021
Stochastic Optimization in Machine Learning: Inhomogeneity, Quantization and Acceleration, Probability and Statistics Seminar, ELTE Institute of Mathematics, Budapest, April 12, 2021.
2021
Stochastic Optimization in Machine Learning: Inhomogeneity, Quantization and Acceleration, Data Analysis and Optimization Seminar, Dept. of Analysis and Operations Research, BME Institute of Mathematics, Budapest, January 21, 2021.
2019
Statistical Learning Theory: Classification and Regression with Stochastic Guarantees, plenary talk, 33rd Hungarian Conference on Operations Research (MOK), MOT, BJMT and GMT, Szeged, June 19, 2019.
2018
Markov döntési folyamatok és sztochasztikus bandita problémák (Markov Decision Processes and Stochastic Bandit Problems), Machine Learning Seminar, Alfréd Rényi Institute of Mathematics, Budapest, November 26, 2018.
2015
Finite Sample System Identification: Exact, Distribution-Free Confidence Regions, Mathematical Modeling Seminar, BME Institute of Mathematics, Budapest, October 13, 2015.
2015
Markov Decision Problems: Model Estimation, Adaptive Prediction and Robust Control, Department of Operations Research and Actuarial Sciences, Corvinus University of Budapest, April 21, 2015.
2010
Introduction to Markov Decision Processes, Signals and Systems Colloquium, Department of Electrical and Electronic Engineering, University of Melbourne, Australia, April 29, 2010.
2009
On Parameter Uncertainties of Markov Decision Processes, Department of Electrical Engineering and Computer Science, Université de Liège, Belgium, April 28, 2009.
2009
Reinforcement Learning in Time-Varying Environments, Robot Learning Group, Dalle Molle Institute for Artificial Intelligence Research (IDSIA), University of Lugano, Switzerland, April 21, 2009.
2009
On Parameter Uncertainties of Markov Decision Processes, Department of Mathematical Engineering, Université catholique de Louvain, Belgium, January 16, 2009.
2008
On Parameter Uncertainties of Markov Decision Processes, Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, United Kingdom, December 4, 2008.
2008
Learning in Changing Environments: Reinforcement Learning in Environments with Asymptotically Bounded Variation, Gatsby Comp. and Theor. Neuroscience and Machine Learning Unit, University College London, UK, September 24, 2008.
2008
Reinforcement Learning in Varying Environments with Applications in Resource Allocation, Department of Mathematical Engineering, Université catholique de Louvain, Louvain-la-Neuve, Belgium, June 24, 2008.
2007
Adaptive Resource Control: Machine Learning Approaches to Stochastic Resource Allocation, Artificial Intelligence Seminar, Alberta Ingenuity Centre for Machine Learning, University of Alberta, Edmonton, Canada, October 26, 2007.
2007
Learning in Varying Environments: Reinforcement Learning in Environments with Asymptotically Bounded Variation, special lecture, Alberta Ingenuity Centre for Machine Learning, University of Alberta, Edmonton, Canada, October 24, 2007.
2001
Constructive Approximation with Artificial Neural Networks, Faculty of Mathematics and Computer Science, Eindhoven University of Technology, Netherlands, June 6, 2001.
Languages
Hungarian
Mother tongue
English
Fluent, CEFR level C1; Certificate in Advanced English (CAE), British Council
German
Advanced, CEFR level C1; Zentrale Mittelstufenprüfung (ZMP), Goethe Institute