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Balázs Csanád Csáji
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2020 – today
- 2024
- [j16]Szabolcs Szentpéteri
, Balázs Csanád Csáji
Finite-Sample Identification of Linear Regression Models With Residual-Permuted Sums. IEEE Control. Syst. Lett. 8: 1523-1528 (2024) - [c19]Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji:
Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions. AISTATS 2024: 3439-3447 - [i25]Balázs Csanád Csáji, Bálint Horváth:
Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands. CoRR abs/2401.15791 (2024) - [i24]Szabolcs Szentpéteri, Balázs Csanád Csáji:
Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case. CoRR abs/2401.15792 (2024) - [i23]Algo Carè, Erik Weyer, Balázs Csanád Csáji, Marco C. Campi:
Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency (Extended Version). CoRR abs/2402.11528 (2024) - [i22]Szabolcs Szentpéteri, Balázs Csanád Csáji:
Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums. CoRR abs/2406.05440 (2024) - [i21]Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji:
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits. CoRR abs/2406.05710 (2024) - [i20]Szabolcs Szentpéteri, Balázs Csanád Csáji:
Sample Complexity of the Sign-Perturbed Sums Method. CoRR abs/2409.01243 (2024) - 2023
- [j15]Ambrus Tamás
Robust Independence Tests With Finite Sample Guarantees for Synchronous Stochastic Linear Systems. IEEE Control. Syst. Lett. 7: 2701-2706 (2023) - [j14]Szabolcs Szentpéteri, Balázs Csanád Csáji:
Non-asymptotic state-space identification of closed-loop stochastic linear systems using instrumental variables. Syst. Control. Lett. 178: 105565 (2023) - [i19]Szabolcs Szentpéteri, Balázs Csanád Csáji:
Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables. CoRR abs/2301.12537 (2023) - [i18]Ambrus Tamás, Balázs Csanád Csáji:
Recursive Estimation of Conditional Kernel Mean Embeddings. CoRR abs/2302.05955 (2023) - [i17]Ambrus Tamás, Balázs Csanád Csáji:
Distribution-Free Inference for the Regression Function of Binary Classification. CoRR abs/2308.01835 (2023) - [i16]Ambrus Tamás, Dániel Ágoston Bálint, Balázs Csanád Csáji:
Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems. CoRR abs/2308.02054 (2023) - [i15]Balázs Csanád Csáji, László Györfi, Ambrus Tamás:
On rate-optimal classification from non-private and from private data. CoRR abs/2312.14889 (2023) - 2022
- [j13]Ambrus Tamás
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings. IEEE Control. Syst. Lett. 6: 860-865 (2022) - [j12]Balázs Csanád Csáji
Nonparametric, Nonasymptotic Confidence Bands With Paley-Wiener Kernels for Band-Limited Functions. IEEE Control. Syst. Lett. 6: 3355-3360 (2022) - [i14]Balázs Csanád Csáji, Bálint Horváth:
Nonparametric, Nonasymptotic Confidence Bands with Paley-Wiener Kernels for Band-Limited Functions. CoRR abs/2206.13629 (2022) - 2021
- [j11]Algo Carè
Facing undermodelling in Sign-Perturbed-Sums system identification. Syst. Control. Lett. 153: 104936 (2021) - [i13]Ambrus Tamás, Balázs Csanád Csáji:
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings. CoRR abs/2103.05126 (2021)
2010 – 2019
- 2019
- [j10]Balázs Csanád Csáji
Distribution-free uncertainty quantification for kernel methods by gradient perturbations. Mach. Learn. 108(8-9): 1677-1699 (2019) - [c18]Algo Carè
Parameter-Dependent Poisson Equations: Tools for Stochastic Approximation in a Markovian Framework. CDC 2019: 2259-2264 - [c17]Balázs Csanád Csáji, Ambrus Tamás:
Semi-Parametric Uncertainty Bounds for Binary Classification. CDC 2019: 4427-4432 - [i12]Balázs Csanád Csáji, Ambrus Tamás:
Semi-Parametric Uncertainty Bounds for Binary Classification. CoRR abs/1903.09790 (2019) - [i11]Algo Carè, Balázs Csanád Csáji, Balázs Gerencsér, László Gerencsér, Miklós Rásonyi:
On the Poisson Equation of Parameter-Dependent Markov Chains. CoRR abs/1906.09464 (2019) - 2018
- [j9]Algo Carè
Finite-Sample System Identification: An Overview and a New Correlation Method. IEEE Control. Syst. Lett. 2(1): 61-66 (2018) - [c16]László Gerencsér, Balázs Csanád Csáji, Sotirios Sabanis
Asymptotic Analysis of the LMS Algorithm with Momentum. CDC 2018: 3062-3067 - [i10]Balázs Csanád Csáji, Zsolt Kemény, Gianfranco Pedone, András Kuti, József Váncza:
Wireless Multi-Sensor Networks for Smart Cities: A Prototype System with Statistical Data Analysis. CoRR abs/1807.07818 (2018) - [i9]Balázs Csanád Csáji, Marco C. Campi, Erik Weyer:
Sign-Perturbed Sums: A New System Identification Approach for Constructing Exact Non-Asymptotic Confidence Regions in Linear Regression Models. CoRR abs/1807.08216 (2018) - [i8]Balázs Csanád Csáji:
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models. CoRR abs/1807.08390 (2018) - [i7]Balázs Csanád Csáji, Krisztián Balázs Kis:
Uncertainty Quantification for Kernel Methods. CoRR abs/1812.09632 (2018) - 2017
- [j8]Erik Weyer, Marco Claudio Campi, Balázs Csanád Csáji:
Asymptotic properties of SPS confidence regions. Autom. 82: 287-294 (2017) - 2016
- [c15]Balázs Csanád Csáji:
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models. AISTATS 2016: 296-304 - [c14]Algo Carè
Sign-Perturbed Sums (SPS) with asymmetric noise: Robustness analysis and robustification techniques. CDC 2016: 262-267 - [i6]Balázs Csanád Csáji, András Kovács, József Váncza:
Online Learning for Aggregating Forecasts in Renewable Energy Systems. ERCIM News 2016(107) (2016) - 2015
- [j7]László Monostori, Paul Valckenaers, Alexandre Dolgui
Cooperative control in production and logistics. Annu. Rev. Control. 39: 12-29 (2015) - [j6]Balázs Csanád Csáji, Marco C. Campi, Erik Weyer
Sign-Perturbed Sums: A New System Identification Approach for Constructing Exact Non-Asymptotic Confidence Regions in Linear Regression Models. IEEE Trans. Signal Process. 63(1): 169-181 (2015) - [c13]Balázs Csanád Csáji, Erik Weyer
Closed-loop applicability of the Sign-Perturbed Sums method. CDC 2015: 1441-1446 - [c12]Valerio Volpe, Balázs Csanád Csáji, Algo Carè
Sign-Perturbed Sums (SPS) with instrumental variables for the identification of ARX systems. CDC 2015: 2115-2120 - [i5]László Monostori, Paul Valckenaers, Alexandre Dolgui, Hervé Panetto, Mietek Brdys, Balázs Csanád Csáji:
Cooperative Control in Production and Logistics. CoRR abs/1506.05608 (2015) - 2014
- [j5]Balázs Csanád Csáji, Raphaël M. Jungers, Vincent D. Blondel:
PageRank optimization by edge selection. Discret. Appl. Math. 169: 73-87 (2014) - [c11]Balázs Csanád Csáji, András Kovács, József Váncza:
Adaptive aggregated predictions for renewable energy systems. ADPRL 2014: 1-8 - [c10]Balázs Csanád Csáji, Marco C. Campi, Erik Weyer
Strong consistency of the Sign-Perturbed Sums method. CDC 2014: 3352-3357 - [i4]Balázs Csanád Csáji, László Monostori:
Adaptive Stochastic Resource Control: A Machine Learning Approach. CoRR abs/1401.3434 (2014) - [i3]Balázs Csanád Csáji, Borbála Háy, András Kovács, Gianfranco Pedone, Tibor Révész, József Váncza:
Monitoring and Controlling Energy-positive Public Lighting: The E+grid System. ERCIM News 2014(98) (2014) - 2013
- [c9]Erik Weyer, Balázs Csanád Csáji, Marco C. Campi:
Guaranteed non-asymptotic confidence ellipsoids for FIR systems. CDC 2013: 7162-7167 - 2012
- [c8]Balázs Csanád Csáji, Marco C. Campi, Erik Weyer
Sign-perturbed sums (SPS): A method for constructing exact finite-sample confidence regions for general linear systems. CDC 2012: 7321-7326 - [i2]Balázs Csanád Csáji, Arnaud Browet, Vincent A. Traag, Jean-Charles Delvenne, Etienne Huens, Paul Van Dooren, Zbigniew Smoreda, Vincent D. Blondel:
Exploring the Mobility of Mobile Phone Users. CoRR abs/1211.6014 (2012) - 2011
- [c7]Balázs Csanád Csáji, Erik Weyer
System identification with binary observations by stochastic approximation and active learning. CDC/ECC 2011: 3634-3639 - 2010
- [j4]László Monostori, Balázs Csanád Csáji, Botond Kádár, András Pfeiffer, Elisabeta Ilie Zudor
Towards adaptive and digital manufacturing. Annu. Rev. Control. 34(1): 118-128 (2010) - [c6]Balázs Csanád Csáji, Raphaël M. Jungers, Vincent D. Blondel:
PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation. ALT 2010: 89-103
2000 – 2009
- 2009
- [b1]Balázs Csanád Csáji:
Adaptive Resource Control. Eötvös Loránd University, Hungary, 2009 - [i1]Balázs Csanád Csáji, Raphaël M. Jungers, Vincent D. Blondel:
PageRank Optimization by Edge Selection. CoRR abs/0911.2280 (2009) - 2008
- [j3]Balázs Csanád Csáji, László Monostori:
Adaptive Stochastic Resource Control: A Machine Learning Approach. J. Artif. Intell. Res. 32: 453-486 (2008) - [j2]Balázs Csanád Csáji, László Monostori:
Value Function Based Reinforcement Learning in Changing Markovian Environments. J. Mach. Learn. Res. 9: 1679-1709 (2008) - 2006
- [j1]Balázs Csanád Csáji, László Monostori
Reinforcement learning in a distributed market-based production control system. Adv. Eng. Informatics 20(3): 279-288 (2006) - [c5]Balázs Csanád Csáji, László Monostori:
Adaptive Sampling Based Large-Scale Stochastic Resource Control. AAAI 2006: 815-820 - 2005
- [c4]Balázs Csanád Csáji, László Monostori:
Stochastic Reactive Production Scheduling by Multi-agent Based Asynchronous Approximate Dynamic Programming. CEEMAS 2005: 388-397 - 2004
- [c3]Balázs Csanád Csáji, Josef Küng, Jürgen Palkoska, Roland R. Wagner:
On the Automation of Similarity Information Maintenance in Flexible Query Answering Systems. DEXA 2004: 130-140 - [c2]Marco Gillies, Daniel Ballin, Balázs Csanád Csáji:
Efficient Clothing Fitting from Data. WSCG 2004: 129-136 - 2003
- [c1]Balázs Csanád Csáji, Botond Kádár, László Monostori:
Improving Multi-agent Based Scheduling by Neurodynamic Programming. HoloMAS 2003: 110-123
Coauthor Index
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