BALÁZS CsANÁD CsÁJI
[ Positions ] [ Degrees ] [ Interests ] [ Teaching ] [ Projects ] [ Visits ] [ Awards ] [ Memberships ] [ Publications ] [ Languages ] [ Students ]
CURRENT POSITIONS |
|
Balázs Csanád Csáji
Senior Researcher
Room K615, Central Building
Phone: (+36) 1-279-6231 |
Adjunct Professor
Room 3.413, South Building
Phone: (+36) 1-381-2202 |
EDUCATION AND DEGREES |
| Ph.D. in Computer Science, Faculty of Informatics, Eötvös Loránd University (ELTE-IK), Budapest, Hungary, 2008 | |||
| Thesis: Adaptive Resource Control: Machine Learning Approaches to Resource Allocation in Uncertain and Changing Environments | |||
| Supervisor: László Monostori, Budapest University of Technology and Economics (BME), Budapest, Hungary | |||
| M.A. in Philosophy, Faculty of Humanities, Eötvös Loránd University (ELTE-BTK), Budapest, Hungary, 2006 | |||
| Thesis: Paradoxes in Rational Collective Decisions (Philosophy of Science & Social Philosophy) | |||
| Supervisor: Miklós Rédei, London School of Economics and Political Science (LSE), London, United Kingdom | |||
| M.Sc. in Mathematics & Computer Science*, Faculty of Science, Eötvös Loránd University (ELTE-TTK), Budapest, Hungary, 2001 | |||
| Thesis: Approximation with Artificial Neural Networks (Machine Learning & Wavelet Analysis) | |||
| Supervisor: Huub ten Eikelder, Eindhoven University of Technology (TU/e), Eindhoven, Netherlands | |||
| * In Hungarian: "programtervező matematikus" (verbatim: "program-designer mathematician") | |||
FIELDS OF INTEREST |
| Computer Science | Machine Learning (Statistical and Reinforcement Learning); Resource Allocation; Randomized Algorithms | ||
| Applied Mathematics | Statistical Inference (Non-Parametric); Probabilistic Models; Stochastic Approximation; Operations Research | ||
| Control Engineering | System Identification (Non-Asymptotic); Stochastic-, Robust- and Adaptive Control; Model Predictive Control | ||
| Analytic Philosophy | Philosophy of Science (Problem of Induction); Interpretations of Probability; Foundations of Mathematics; Logic |
TEACHING AND LECTURES |
| Teaching Activity | |||
| 2024 – | Data Mining and Machine Learning, Lectures for B.Sc. Students in Mathematics, Major Block: Modelling, Department of Probability Theory and Statistics, Institute of Mathematics, Eötvös Loránd University (ELTE) | ||
| 2023 – | Statistical Learning and Kernel Methods, Lectures for M.Sc. and Ph.D. Students in Applied and Pure Mathematics, Department of Probability Theory and Statistics, Institute of Mathematics, Eötvös Loránd University (ELTE) | ||
| 2022 – | Mathematical Statistics, Lectures and Seminars for B.Sc. Students in Applied and Pure Mathematics, Department of Probability Theory and Statistics, Institute of Mathematics, Eötvös Loránd University (ELTE) | ||
| 2021 – | Probability Theory II, Seminars for B.Sc. Students in Appl. Mathematics, Eötvös Loránd University (ELTE) | ||
| 2019 – | Markov Decision Processes and Reinforcement Learning (MDPs & RL), Lectures for M.Sc. and Ph.D. Students in Mathematics, Institute of Mathematics, Budapest University of Technology and Economics (BME); and (from 2022) Department of Probability Theory and Statistics, Institute of Mathematics, Eötvös Loránd University (ELTE) [slides] | ||
| 2019 – 2022 | Mathematical Foundations of Machine Learning (MFML), Lectures for M.Sc. and Ph.D. Students in Applied Mathematics, Institute of Mathematics, Eötvös Loránd University (ELTE) [syllabus] [slides] | ||
| 2015 – 2021 | Stochastic Models and Adaptive Algorithms, Ph.D. School of Computer Science, Eötvös Loránd University (ELTE); and (since 2017) Budapest University of Technology and Economics (BME) [slides] | ||
| 2013 – 2014 | Mathematical Optimization (main organizer), SZTAKI: Institute for Computer Science and Control | ||
| 2012 | Probability and Random Models (ELEN90054, with Girish Nair), School of Engineering, University of Melbourne, Australia | ||
| 2005 – 2006 | Markov Decision Processes (organized by Cs. Szepesvári), SZTAKI: Institute for Computer Science and Control | ||
| 2002 | Theory of Operating Systems, Department of Information Systems, Eötvös Loránd University (ELTE) | ||
| 2000 – 2002 | Programming Methodology, Department of Software Technology, Eötvös Loránd University (ELTE) | ||
| Lecture Slides | |||
| 2023 | The Huge Potential of AI in CAR-T Cell Therapies, Artificial Intelligence and Automation Expo, Budapest, Hungary [slides] | ||
| 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, Hungary [slides] | ||
| 2021 | Stochastic Optimization in Machine Learning: Inhomogeneity, Quantization and Acceleration; Data Analysis and Optimization Seminar, Budapest University of Technology and Economics (BME), Budapest, Hungary [slides] | ||
| 2017 | On the Reliability of Regression Models, Publication Award Seminar, SZTAKI, Budapest, Hungary [slides] | ||
| 2012 | Distribution-Free System Identification: Exact-, Non-Asymptotic Confidence Regions, Faculty of Electrical Engineering, Computer Science, and Mathematics, University of Paderborn, Germany [slides] | ||
| 2010 | Introduction to Markov Decision Processes, Department of Electrical Engineering, University of Melbourne, Australia [slides] | ||
| 2009 | A Machine Learning Approach to Stochastic Resource Control, Poster, DYSCO Study Day, Mons, Belgium [poster] | ||
| 2008 | Learning in Changing Environments: Reinforcement Learning in Environments with Asymptotically Bounded Variation, Gatsby Computational and Theoretical Neuroscience and Machine Learning Unit, University College London (UCL), UK [slides] | ||
| 2006 | Introduction to Off-Policy Learning, MDP Seminar, SZTAKI, Budapest, Hungary [slides] | ||
| 2005 | Introduction to Temporal Difference Learning (Hungarian Slides), MDP Seminar, SZTAKI, Budapest, Hungary [slides] | ||
| 2004 | Intuitionism in Mathematics (Philosophy of Mathematics), HalSzem, ELTE, Budapest, Hungary [pdf] Handout [pdf] | ||
RESEARCH PROJECTS |
| European Projects | |||
| 2020 – 2025 | Arificial 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 | ||
| 2021 – 2024 | AIDPATH: Artificial Intelligence-driven, Decentralized Production for Advanced Therapies in the Hospital, EU H2020 | ||
| 2018 – 2019 | BioManu-II: Biologicalisation in Manufacturing, Fraunhofer-Gesellschaft, Germany | ||
| 2005 – 2009 | Coll-Plexity: Collaborations as Complex Systems (Project Manager for SZTAKI), Nest Program, 6th Framework, EU | ||
| 2004 – 2006 | MultiSens: Cameras as Multifunctional Sensors for Automated Processes, 6th Framework, EU | ||
| 2000 – 2004 | MPA: Modular Plant Architecture, Growth Project, 5th Framework, EU | ||
| National Projects | |||
| 2022 – 2025 | COPROLOGS: Cooperative Production and Logistics Systems to Support a Competitive and Sustainable Economy (Task A1.2: Predictive Methods), TKP2021-NKTA-01, National Programme for Research and Innovation (NKFIH) | ||
| 2018 – 2023 | INEXT: Research on the Prime Exploitation of the Potential Provided by Industrial Digitalisation (Task A.3: Machine Learning in Stochastic Dynamic Systems), National Programme for Research and Innovation (NKFIH) | ||
| 2018 – 2021 | Exloring the Mathematical Foundations of Artificial Intelligence, National Excellence Program (NKP) | ||
| 2017 – 2019 | Markov Decision Processes: Estimation and Approximation Methods (Principal Investigator), KH_17, NKFIH | ||
| 2014 – 2016 | Analytical Module for a Wireless Multi-Sensor Network (Principal Investigator), comissioned by GE Lighting | ||
| 2011 – 2014 | E+Grid: An Embedded System for Optimizing Energy Positive Public Lighting Service, National Development Agency (NFÜ) | ||
| 2008 – 2010 | Production Structures as Complex Adaptive Systems, Hungarian Scientific Research Fund (OTKA) | ||
| 2004 – 2007 | VITAL: Real-Time, Cooperative Enterprises, National Programme for Research and Innovation (NKFP) | ||
| 2005 – 2007 | Modeling, Planning and Control of Distributed, Modular Production Structures, Hungarian Scientific Research Fund (OTKA) | ||
| University Projects | |||
| 2011 – 2012 | Distribution-Free System Identification (Principal Investigator), Australian Research Council (ARC), Australia | ||
| 2009 – 2011 | Algorithms for Change Detection Based on Finite Sample System Identification, Australian Research Council (ARC), Australia | ||
| 2009 | Data Mining in Mobile Networks, Department of Mathematical Engineering, Catholic University of Louvain, Belgium | ||
| 2003 | Learning and Maintaining Similarity Information in Flexible Query Answering Systems, Johannes Kepler University, Austria | ||
| 2002 | Physics Engine for the TARA Graphical Library, Radical Multimedia Lab, British Telecom, United Kingdom | ||
| 2001 | Constructive Approximation with Feed-forward Artificial Neural Networks, Technical University of Eindhoven, Netherlands | ||
| 2000 | Augmented Reality for Parkinson Patients, Eötvös Loránd University (ELTE) and Semmelweis University, Hungary | ||
RESEARCH VISITS |
| Long-term Visits | |||
| 2009 – 2012 | Department of Electrical and Electronic Engineering, University of Melbourne, ARC Research Fellow, 3 years, Australia | ||
| 2008 – 2009 | Department of Mathematical Engineering, Université catholique de Louvain, Research Fellow, 8 months, Belgium | ||
| 2003 | Institute for Applied Knowledge Processing, Johannes Kepler University, CEEPUS Scholarship, 4 months, Austria | ||
| 2002 | Radical Multimedia Lab, BTexact Technologies, British Telecom, IAESTE Exchange Program, 3 months, United Kingdom | ||
| 2001 | Faculty of Mathematics and Computing Science, Technical University of Eindhoven, ERASMUS, 5 months, Netherlands | ||
| Short-term Visits | |||
| 2012 | Department of Information Engineering, University of Brescia, Italy | ||
| 2012 | Faculty of Electrical Engineering, Computer Science, and Mathematics, University of Paderborn, Germany | ||
| 2009 | Department of Electrical Engineering and Computer Science, University of Liège, Belgium | ||
| 2009 | Robot Learning Group, Dalle Molle Institute for Artificial Intelligence (IDSIA), University of Lugano, Switzerland | ||
| 2008 | Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, United Kingdom | ||
| 2008 | Gatsby Computational and Theoretical Neuroscience and Machine Learning Unit, University College London, United Kingdom | ||
| 2008 | Alberta Ingenuity Center for Machine Learning, Department of Computing Science, University of Alberta, Canada | ||
AWARDS AND SCHOLARSHIPS |
| Honors and Awards | |||
| 2022, 2024 | Best Ph.D. Supervisor Award (2x), SZTAKI: Institute for Computer Science and Control | ||
| 2020 | Bolyai Certificate of Merit (for the results of the 2nd Bolyai Fellowship), Hungarian Academy of Sciences (MTA) | ||
| 2019 | Plenary Speaker, 33rd Hungarian Conference on Operations Research (MOK), May 20-22, Szeged (Hungary) | ||
| 2016 | Béla Gyires Prize (Applied Mathematics), Section of Mathematics, Hungarian Academy of Sciences (MTA) | ||
| 2016 | Bolyai Certificate of Merit (for the results of the 1st Bolyai Fellowship), Hungarian Academy of Sciences (MTA) | ||
| 2013 | Outstanding Reviewer, IEEE Transactions on Automatic Control (TAC), IEEE Control Systems Society (CSS) | ||
| 2011 | Discovery Early Career Researcher Award (DECRA, Applied Mathematics), Australian Research Council (ARC) | ||
| 2009 | Finalist (top 5) of the Cor Baayen Award, European Research Consortium for Informatics and Mathematics (ERCIM) | ||
| 2009 | Young Researchers' Award (Mathematical Sciences), Hungarian Academy of Sciences (MTA) | ||
| 2009, 2016, 2018 | Publication Award (3x), SZTAKI: Institute for Computer Science and Control | ||
| 2006 | Young Researchers' Institute Award, SZTAKI: Institute for Computer Science and Control | ||
| 2006 | Best Paper Award, 6th International Workshop on Emergent Synthesis (IWES), University of Tokyo (Japan) | ||
| 2004 | Best Ph.D. Student Award, SZTAKI: Institute for Computer Science and Control | ||
| 2004, 2009, 2015 | Institute Award (3x), SZTAKI: Institute for Computer Science and Control | ||
| 2000 | First Prize, Section of Informatics, Scientific Student Conference (TDK), Eötvös Loránd University (ELTE) | ||
| Grants and Scholarships | |||
| 2020 – 2024 | Supervisor, Cooperative Doctoral Programme (Tamás, A.), National Programme for Research and Innovation (NKFIH) | ||
| 2016 – 2019 | János Bolyai Research Fellowship (2nd term), Hungarian Academy of Sciences (MTA) | ||
| 2012 – 2015 | János Bolyai Research Fellowship (1st term), Hungarian Academy of Sciences (MTA) | ||
| 2011 – 2013 | ARC DECRA Fellowship, Australian Research Council (ARC) | ||
| 2004 – 2007 | Young Researcher Scholarship, Hungarian Academy of Sciences (MTA) | ||
| 2001 – 2004 | Ph.D. Scholarship, Eötvös Loránd University (ELTE) | ||
| 2000 – 2001 | Research Scholarship (Artificial Intelligence), Pázmány-Eötvös Foundation | ||
PROFESSIONAL MEMBERSHIPS |
| 2023 – | Section of Mathematics (member of public body), Hungarian Academy of Sciences (MTA) | ||
| – Scientific Committee: Operations Research (voting right) | |||
| 2019 – | János Bolyai Mathematical Society (BJMT) | ||
| – Vice Chair: Section of Applied Mathematics | |||
| 2015 – | Hungarian Operations Research Society (MOT) | ||
| 2014 – | International Federation of Automatic Control (IFAC) | ||
| – Technical Committee (1.1): Modelling, Identification and Signal Processing | |||
| – Technical Committee (1.2): Adaptive and Learning Systems | |||
| – Technical Committee (5.4): Large Scale Complex Systems | |||
| 2013 – | Institute of Electrical and Electronic Engineers (IEEE), Control Systems Society (CSS) | ||
| – Technical Committee: System Identification and Adaptive Control |
BIBLIOMETRICS |
| All publications: 95+ | Independent citations*: 2500+ | Cumulative impact factor: 65+ | Erdős number**: 3 | ||
| Journal articles: 35 | Book chapters & LNCS/AI: 8 | Conference & workshop papers: 52 | Invited talks: 20+ | ||
| * A [citation] is independent if none of the authors of the citing paper is an author of the cited paper | ** [proof] | ||||
SELECTED PUBLICATIONS |
| Drafts and Preprints | |||
| – | Szentpéteri Sz.; Csáji, B. Cs.: Finite Sample Analysis of Distribution-Free Confidence Ellipsoids for Linear Regression [arxiv] | ||
| – | Tamás, A.; Csáji, B. Cs.: Distribution-Free Inference for the Regression Function of Binary Classification [arxiv] | ||
| – | Carè, A.; Csáji, B. Cs.; Gerencsér, B.; Gerencsér, L.; Rásonyi, M.: Poisson Equations, Lipschitz Continuity and Controlled Queues [arxiv] | ||
| – | Csáji, B. Cs.; Györfi, L.; Tamás, A.; Walk, H.: On Rate-Optimal Partitioning Classification from Observable and from Privatised Data [arxiv] | ||
| Selected Journal Papers | |||
| – | 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), 2025 (accepted) [arxiv] | ||
| – | Szentpéteri Sz.; Csáji, B. Cs.: Sample Complexity of the Sign-Perturbed Sums Method, Automatica, Elsevier, 2024 (accepted; in print) [arxiv] | ||
| – | Tamás, A.; Csáji, B. Cs.: Recursive Estimation of Conditional Kernel Mean Embeddings, Journal of Machine Learning Research (JMLR), Microtome Publishing, Vol. 25, Num. 264, 1-35, 2024 [link] [arxiv] | ||
| – | Szentpéteri Sz.; Csáji, B. Cs.: Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums, IEEE Control Systems Letters (L-CSS), IEEE Press, Vol. 8, 2024, pp. 1523–1528 [arxiv] | ||
| – | Tamás, A.; Bálint, D. Á.; Csáji, B. Cs.: Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems, IEEE Control Systems Letters (L-CSS), IEEE Press, Vol. 7, 2023, pp. 2701–2706 [arxiv] | ||
| – | 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, August 2023, 105565 [arxiv] [link] | ||
| – | Csáji, B. Cs.; Horváth, B.: Nonparametric, Nonasymptotic Confidence Bands with Paley-Wiener Kernels for Band-Limited Functions, IEEE Control Systems Letters (L-CSS), IEEE Press, Vol. 6, 2022, pp. 3355–3360 [arxiv] | ||
| – | Tamás, A.; Csáji, B. Cs.: Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings, IEEE Control Systems Letters (L-CSS), IEEE Press, Vol. 6, 2022, pp. 860–865 [arxiv] | ||
| – | Carè, A.; Campi, M. C.; Csáji, B. Cs.; Weyer, E.: Facing Undermodelling in Sign-Perturbed Sums System Identification, Systems & Control Letters, Elsevier, Vol. 153, July 2021, 104936 [pdf] | ||
| – | Monostori, L.; Csáji, B. Cs.; Egri, P.; Kis, K. B.; Váncza, J.; Ochs, J.; Jung, S.; König, N.; Pieske, S.; Wein, S.; Schmitt, R.; Brecher, C.: Automated Stem Cell Production by Bio-Inspired Control, CIRP Journal of Manufacturing Science and Technology, Elsevier, 2021, 369–379 [pdf] | ||
| – | Csáji, B. Cs.; Kis, K. B.: Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations, Machine Learning, Springer, Special Issue of the European Conference on Machine Learning (ECML PKDD Journal Track), Vol. 108, 2019, pp. 1677–1699 [arxiv] [link] | ||
| – | Weyer, E.; Campi, M. C.; Csáji, B. Cs.: Asymptotic Properties of SPS Confidence Regions, Automatica, Elsevier, Vol. 82, 2017, pp. 287–294 [pdf] | ||
| – | Csáji, B. Cs.; Kemény, Zs.; Pedone, G.; Kuti, A.; Váncza, J.: Wireless Multi-Sensor Networks for Smart Cities: A Prototype System with Statistical Data Analysis, IEEE Sensors Journal, IEEE Press, Vol. 17, Issue 23, 2017, pp. 7667–7676 [arxiv] | ||
| – | 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 (L-CSS), IEEE Press, Vol. 2, No. 1, 2017, pp. 61–66 [pdf] | ||
| – | Kovács, A.; Bátai, R.; Csáji, B. Cs.; Dudás, P.; Háy, B.; Pedone, G.; Révész, T.; Váncza, J.: Intelligent Control for Energy-Positive Street Lighting, Energy: The International Journal, Elsevier, Vol. 114, 2016, pp. 40–51 [pdf] | ||
| – | 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. 69, 2015, pp. 169–181 [pdf] | ||
| – | Monostori, L.; Valckenaers, P.; Dolgui, A.; Panetto, H.; Brdys, M.; Csáji, B. Cs.: Cooperative Control in Production and Logistics, Annual Reviews in Control (ARC): A Journal of IFAC, the International Federation of Automatic Control, Elsevier, Vol. 39, 2015, pp. 12–29 [pdf] | ||
| – | Csáji, B. Cs.; Jungers, R. M.; Blondel, V. D.: PageRank Optimization by Edge Selection, Discrete Applied Mathematics (DAM): The Journal of Combinatorial Algorithms, Informatics and Computational Sciences, Elsevier, Vol. 169, 2014, pp. 73–87 [pdf] | ||
| – | Csáji, B. Cs.; Browet, A.; Traag, V. A.; Delvenne, J-C.; Huens, E.; Van Dooren, P.; Smoreda, Z.; Blondel, V. D.: Exploring the Mobility of Mobile Phone Users, Physica A: Statistical Mechanics and its Applications, Elsevier, Vol. 392, Issue 6, 2013, pp. 1459–1473 [pdf] | ||
| – | 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 [pdf] | ||
| – | Csáji, B. Cs.; Monostori, L.: Value Function Based Reinforcement Learning in Changing Markovian Environments, Journal of Machine Learning Research (JMLR), MIT Press and Microtome Publishing, Vol. 9, 2008, pp. 1679–1709 [pdf] | ||
| Selected Conference Papers | |||
| – | Tamás, A.; Szentpéteri, Sz.; Csáji, B. Cs.: Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits, 28th International Conference on Artificial Intelligence and Statistics (AISTATS), Mai Khao, Thailand, 2025 [arxiv] | ||
| – | Tamás, A.; Szentpéteri, Sz.; Csáji, B. Cs.: Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions, 27th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2–4, 2024, PMLR: Vol. 238 [pdf] [poster] | ||
| – | 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, June 11–13, Procedia CIRP, Elsevier, 2024 [pdf] | ||
| – | Horváth, B.; Csáji, B. Cs.: Nonparametric Simultaneous Confidence Bands: The Case of Known Input Distributions, 23rd European Young Statisticians Meeting (EYSM 2023, virtual mode), Bernoulli Society, Ljubljana, Slovenia, September 11–15, 2023 [pdf] | ||
| – | Szentpéteri, Sz.; Csáji, B. Cs.: Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case, 22nd IFAC World Congress (World Congress of the International Federation of Automatic Control), Yokohama, Japan, July 9–14, 2023 [pdf] | ||
| – | Csáji, B. Cs.; Horváth, B.: Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands, 22nd IFAC World Congress (World Congress of the International Federation of Automatic Control), Yokohama, Japan, July 9–14, 2023 [pdf] | ||
| – | 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 Conference on Digital Enterprise Technologies, 2021, pp. 154–159 [pdf] | ||
| – | 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 (1st Virtual IFAC World Congress), July 11–17, 2020, pp. 7142–7147 [pdf] [slides] | ||
| – | Csáji, B. Cs.; Tamás, A.: Semi-Parametric Uncertainty Bounds for Binary Classification, 58th IEEE Conference on Decision and Control (CDC), Nice, France, December 11–13, 2019, pp. 4427–4432 [pdf] [slides] | ||
| – | 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 Conference on Decision and Control (CDC), Nice, France, December 11–13, 2019, pp. 2259–2264 [pdf] | ||
| – | Csáji, B. Cs.; Kis, K. B.: Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations, 58th European Conference on Machine Learning (ECML PKDD), Würzburg, Germany, September 16–20, 2019 [arxiv] [slides] [poster] | ||
| – | 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, December 17–19, 2018, pp. 3062–3067 [pdf] [slides] | ||
| – | Csáji, B. Cs.: Non-Asymptotic Confidence Regions for Regularized Linear Regression Estimates, 20th European Conference on Mathematics for Industry (ECMI), Finite-Sample System Identification Minisymposium, Budapest, Hungary, June 18–22, 2018, Springer, pp. 605–611 [pdf] | ||
| – | Kolumbán, S.; Csáji, B. Cs.: Towards D-Optimal Input Design for Finite-Sample System Identification, 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July 9–11, 2018, pp. 215–220 [pdf] | ||
| – | Carè, A.; Csáji, B. Cs.; Campi, M. C.; Weyer, E.: Undermodelling Detection with Sign-Perturbed Sums, 20th IFAC World Congress, Toulouse, France, July 9–14, 2017, pp. 2799–2804 [pdf] [slides] | ||
| – | 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, December 12–14, 2016, pp. 262–267 [pdf] | ||
| – | Csáji, B. Cs.: Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models, 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016, pp. 296–304 [pdf] [poster] | ||
| – | 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 Conference on Decision and Control (CDC), Osaka, Japan, December 15–18, 2015, pp. 2115–2120 [arxiv] | ||
| – | Csáji, B. Cs.; Weyer, E.: Closed-Loop Applicability of the Sign-Perturbed Sums Method, 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, December 15–18, 2015, pp. 1441–1446 [pdf] [slides] | ||
| – | Csáji, B. Cs.; Kovács, A.; Váncza, J.: Adaptive Aggregated Predictions for Renewable Energy Systems, 2014 IEEE SSCI Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Orlando, Florida, December 9–12, 2014, pp. 132–139 [pdf] | ||
| – | 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, December 15–17, 2014, pp. 3352–3357 [pdf] | ||
| – | 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, December 10–13, 2013, pp. 7162–7167 [pdf] | ||
| – | 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 Conference on Decision and Control (CDC), Maui, Hawaii, December 10–13, 2012, pp. 7321–7326 [pdf] | ||
| – | 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, July 11–13, 2012, pp. 245–255 [pdf] | ||
| – | 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, July 11–13, 2012, pp. 227–232 [pdf] | ||
| – | 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, July 11–13, 2012, pp. 1185–1190 [pdf] [slides] | ||
| – | Csáji, B. Cs.; Weyer, E.: System Identification with Binary Observations by Stochastic Approximation and Active Learning, 50th IEEE Conference on Decision and Control (CDC) & European Control Conference (ECC), Orlando, Florida, December 12–15, 2011, pp. 3634–3639 [pdf] | ||
| – | Ivanov, T.; Csáji, B. Cs.: Reproducing Kernels Preserving Algebraic Structure: A Duality Approach, 19th International Symposium on Mathematical Theory of Networks and Systems (MTMS), Budapest, Hungary, July 5–9, 2010, pp. 1161–1167 [pdf] | ||
| – | Csáji, B. Cs.; Jungers, R. M.; Blondel, V. D.: PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation, 21st International Conference on Algorithmic Learning Theory (ALT), Canberra, Australia, October 6–8, 2010, pp. 89–103 [pdf] | ||
| – | Csáji, B. Cs.; Monostori, L.: Adaptive Sampling Based Large-Scale Stochastic Resource Control, 21st National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, July 16–20, 2006, pp. 815–820 [pdf] | ||
| Selected Papers in Hungarian | |||
| – | Tamás, A.; Csáji, B. Cs.: Statisztikus tanluláselmélet I: Szupport vektor gépek (Statistical Learning Theory I: Support Vector Machines), Érintő: Elektronikus Matematikai Lapok (Tangent: Electronic Journal of Mathematics, János Bolyai Mathematical Society), Vol. 31, március, 2024 [link] | ||
| – | Csáji, B. Cs.: Antirealizmus a matematikában (Anti-Realism in Mathematics), Érintő: Elektronikus Matematikai Lapok (Tangent: Electronic Journal of Mathematics, János Bolyai Mathematical Society), Vol. 22, December, 2021 [link] | ||
| – | Tamás, A.; Csáji, B. Cs.: Sztochasztikus garanciák bináris klasszifikációhoz (Stochastic Guarantees for Binary Classification), Alkalmazott Matematikai Lapok (Applied Mathematical Journal of the Section of Mathematics, Hungarian Academy of Sciences), Vol. 37, No. 2, 2020 [pdf] | ||
| – | Csáji, B. Cs.: Szimmetria és konfidencia (Symmetry and Confidence), Alkalmazott Matematikai Lapok (Applied Mathematical Journal of the Section of Mathematics, Hungarian Academy of Sciences), Vol. 36, No. 2, 2019, pp. 271–278 [pdf] | ||
| – | Csáji, B. Cs.; Rédei, M.: A racionális demokratikus véleményösszegzés korlátairól (On the Limits of Rational Democratic Judgment Aggregation), Magyar Filozófiai Szemle (Hungarian Philosophical Review), Vol. 55, No. 2, 2011, pp. 97–121 [pdf] | ||
SOFTWARE DEVELOPMENT |
| 2000 – 2002 | Chess AI: Pandora Chess Program, v0.44, C++, Visual Studio, Windows, Freeware [zip] | ||
| 2000 | Educational Software: OCR with Multilayer Perceptrons, Delphi, Windows, Freeware [zip] |
LANGUAGE SKILLS |
| – Hungarian | mother tongue | ||
| – English | fluent; CEFR Level: C1, Certificate in Advanced English (CAE), British Council | ||
| – German | good; CEFR Level: C1, Zentrale Mittelstufenprüfung (ZMP), Goethe Institute |
PHD STUDENTS |
| – | Bálint Horváth, Doctoral School of Mathematics and Computer Science, Budapest University of Technology and Economics (BME) Research topics: kernel regression, simultaneous confidence bands, nonparametric inference, image processing, sequential optimization Awards: Best Ph.D. Student Award (2x), SZTAKI: Institute for Computer Science and Control, 2020 and 2022 |
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| – | Ambrus Tamás, Doctoral School of Mathematics, Institute of Mathematics, Faculty of Science, Eötvös Loránd University (ELTE) Research topics: binary classification, resampling methods, kernel mean embedding, independence tests, stochastic bandits Awards: Best Ph.D. Student Award, SZTAKI, 2021; Young Researchers' Institute Award, SZTAKI, 2024; Cooperative Doctoral Program (KDP) |
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| – | Szabolcs Szentpéteri, Doctoral School of Computer Science, Faculty of Informatics, Eötvös Loránd University (ELTE) Research topics: finite-sample system identification, indirect adaptive control, randomized algorithms, sample complexity Awards: Best Ph.D. Student Award (2x), SZTAKI: Institute for Computer Science and Control, 2023 and 2024 |
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| – | Balázs Miavecz, Doctoral School of Mathematics, Institute of Mathematics, Faculty of Science, Eötvös Loránd University (ELTE) Research topics: reinforcement learning, model predictive control, constraint satisfaction, linear and convex programming |
INFORMATION FOR STUDENTS |
| Opportunities for students, with strong background in mathematics and computer science, interested in the theory of machine learning: | |||
| – | M.Sc. and third year B.Sc. students (studying in Budapest): there are opportunities to do research projects in machine learning, possibly leading to a TDK participation or to a (bachelor's or master's) thesis. Successful students may also have part-time [employment possibilities] at SZTAKI. | ||
| – | Prospective Ph.D. students: as examples of possible research directions, see the thesis topic proposals at ELTE or BME, such as the ones about [statistical learning] and [reinforcement learning]. Strong theoretical background, especially in probability and statistics, is fundamental. | ||
| – | International students applying for Stipendium Hungaricum scholarships: some doctoral schools [require] that the applicants should contact them instead of the potential supervisors. If you do contact me, please, send a detailed CV (with subjects / grades), as well as your master's thesis. | ||
BALÁZS CsANÁD CsÁJI