I am a research scientist at Google DeepMind, London.
I am interested in design and analysis of learning algorithms, with emphasis on theories of generalization, uncertainty estimation, and concentration inequalities.
You can contact me at firstname.lastname@gmail.com
Publications
To Believe or Not to Believe Your LLM
Y. Abbasi-Yadkori, I. Kuzborskij, A. György, Cs. Szepesvári
Conference on Neural Information Processing Systems (NeurIPS), 2024
(to appear).
[PDF]
[Bibtex]
@inproceedings{yadkori2024tobelieve, title="To Believe or Not to Believe Your LLM", author={Yasin Abbasi-Yadkori and Ilja Kuzborskij and András György and Csaba Szepesvári}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2024} }
Better-than-KL PAC-Bayes Bounds
I. Kuzborskij,
K.-S. Jun,
Y. Wu,
K. Jang,
and F. Orabona
Conference on Learning Theory (COLT), 2024.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2024better, title={Better-than-KL {PAC-Bayes} Bounds}, author={Kuzborskij, Ilja and Jun, Kwang-Sung and Wu, Yulian and Jang, Kyoungseok and Orabona, Francesco}, booktitle={Conference on Learning Theory (COLT)}, year={2024}, }
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
Y. Deng,
I. Kuzborskij,
and M. Mahdavi
Conference on Neural Information Processing Systems (NeurIPS), 2023.
[PDF]
[Bibtex]
@inproceedings{deng2023mixture, title={Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation}, author={Deng, Yuyang and Kuzborskij, Ilja and Mahdavi, Mehrdad}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2023} }
Tighter PAC-Bayes Bounds Through Coin-Betting
K. Jang,
K.-S. Jun,
I. Kuzborskij,
and F. Orabona
Conference on Learning Theory (COLT), 2023.
[PDF]
[Bibtex]
@inproceedings{jang2023tighter, title={Tighter {PAC-Bayes} Bounds Through Coin-Betting}, author={Jang, Kyoungseok and Jun, Kwang-Sung and Kuzborskij, Ilja and Orabona, Francesco}, booktitle={Conference on Learning Theory (COLT)}, year={2023}, }
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
D. Richards and I. Kuzborskij
Conference on Neural Information Processing Systems (NeurIPS), 2021.
[PDF]
[Bibtex]
@inproceedings{richards2021stability, title={Stability \& Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel}, author={Richards, D. and Kuzborskij, I.}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2021} }
On the Role of Optimization in Double Descent: A Least Squares Study
I. Kuzborskij,
Cs. Szepesvári,
O. Rivasplata,
A. Rannen Triki,
and
R. Pascanu
Conference on Neural Information Processing Systems (NeurIPS), 2021.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2021role, title={On the Role of Optimization in Double Descent: A Least Squares Study}, author={Ilja Kuzborskij and Csaba Szepesvári and Omar Rivasplata and Amal Rannen-Triki and Razvan Pascanu}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2021} }
A Distribution-dependent Analysis of Meta Learning
M. Konobeev, I. Kuzborskij, and Cs. Szepesvári
International Conference on Machine Learning (ICML), 2021.
[PDF]
[Bibtex]
[Code]
@inproceedings{konobeev2020statistical, title = "{A Distribution-dependent Analysis of Meta Learning}", author = {Konobeev, Mikhail and Kuzborskij, Ilja and Szepesvári, Csaba}, booktitle = {International Conference on Machine Learning}, year = {2021} }
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
I. Kuzborskij,
C. Vernade,
A. György, and
Cs. Szepesvári
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
[PDF]
[Bibtex]
[Code]
@InProceedings{pmlr-v130-kuzborskij21a, title = { Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting }, author = {Kuzborskij, Ilja and Vernade, Claire and Gyorgy, Andras and Szepesvari, Csaba}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, year = {2021} }
Locally-Adaptive Nonparametric Online Learning
I. Kuzborskij and N. Cesa-Bianchi
Conference on Neural Information Processing Systems (NeurIPS), 2020.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2020locally, title = {Locally-{A}daptive {N}onparametric {O}nline {L}earning}, author = {Kuzborskij, Ilja and Cesa-Bianchi, Nicol\`{o}}, booktitle= {Neural Information Processing Systems (NeurIPS)}, year = {2020} }
PAC-Bayes Analysis Beyond the Usual Bounds
O. Rivasplata
I. Kuzborskij,
Cs. Szepesvári, and
J. Shawe-Taylor
Conference on Neural Information Processing Systems (NeurIPS), 2020.
[PDF]
[Bibtex]
@inproceedings{rivasplata2020pac, title = {{PAC}-{B}ayes {A}nalysis {B}eyond the {U}sual {B}ounds}, author={Rivasplata, O. and Kuzborskij, I. and Szepesv{á}ri, Cs. and Shawe-Taylor, J.} booktitle= {Neural Information Processing Systems (NeurIPS)}, year = {2020} }
Distribution-Dependent Analysis of Gibbs-ERM Principle
I. Kuzborskij, N. Cesa-Bianchi, and Cs. Szepesvári
Conference on Learning Theory (COLT), 2019.
[PDF]
[Bibtex]
@InProceedings{pmlr-v99-kuzborskij19a, title = {Distribution-Dependent Analysis of Gibbs-ERM Principle}, author = {Kuzborskij, Ilja and Cesa-Bianchi, Nicol\`{o} and Szepesvári, Csaba}, booktitle = {Proceedings of the Thirty-Second Conference on Learning Theory}, pages = {2028--2054}, year = {2019}, editor = {Beygelzimer, Alina and Hsu, Daniel}, volume = {99}, series = {Proceedings of Machine Learning Research}, address = {Phoenix, USA}, month = {25--28 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v99/kuzborskij19a/kuzborskij19a.pdf}, url = {http://proceedings.mlr.press/v99/kuzborskij19a.html} }
Efficient Linear Bandits through Matrix Sketching
I. Kuzborskij, L. Cella, and N. Cesa-Bianchi
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2019efficient, title = {Efficient {L}inear {B}andits through {M}atrix {S}ketching}, author = {Kuzborskij, Ilja and Cella, Leonardo and Cesa-Bianchi, Nicol\`{o}}, booktitle = {Proceedings of Machine Learning Research}, pages = {177--185}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, address = {}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/kuzborskij19a/kuzborskij19a.pdf}, url = {http://proceedings.mlr.press/v89/kuzborskij19a.html} }
Data-Dependent Stability of Stochastic Gradient Descent
I. Kuzborskij and C. H. Lampert
International Conference on Machine Learning (ICML), 2018.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2017data, title={{D}ata-{D}ependent {S}tability of {S}tochastic {G}radient {D}escent}, author={I. Kuzborskij and C. H. Lampert}, booktitle = {International Conference on Machine Learning (ICML)}, year={2018} }
Nonparametric Online Regression while Learning the Metric
I. Kuzborskij and N. Cesa-Bianchi
Advances in Neural Information Processing Systems (NeurIPS), 2017.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2017nonparametric, title={{N}onparametric {O}nline {R}egression while {L}earning the {M}etric}, author={I. Kuzborskij and N. Cesa-Bianchi}, booktitle= {Neural Information Processing Systems (NIPS)}, year={2017} }
Fast Rates by Transferring from Auxiliary Hypotheses
I. Kuzborskij and F. Orabona
Machine Learning, September, 2016.
[PDF]
[Bibtex]
@article{kuzborskij2016fast, author={I. Kuzborskij and F. Orabona}, title={Fast {R}ates by {T}ransferring from {A}uxiliary {H}ypotheses}, journal="Machine Learning", year=2016, pages="1--25", issn="1573-0565", doi="10.1007/s10994-016-5594-4", url="http://dx.doi.org/10.1007/s10994-016-5594-4" }
I. Kuzborskij, F. Orabona, and B. Caputo
Computer Vision and Image Understanding, 2016.
[PDF] [Bibtex]
@article{kuzborskij2016scalable, author = {I. Kuzborskij and F. Orabona and B. Caputo}, title = {Transfer {L}earning through {G}reedy {S}ubset {S}election}, journal = "Computer Vision and Image Understanding ", volume = "", number = "", pages = " - ", year = "2016", issn = "1077-3142", doi = "http://dx.doi.org/10.1016/j.cviu.2016.09.003", url = "http://www.sciencedirect.com/science/article/pii/S1077314216301370", }
When Naïve Bayes Nearest Neighbors Meet Convolutional Neural Networks
I. Kuzborskij, F.M. Carlucci, and B. Caputo
Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[PDF]
[Bibtex]
[Code]
[Supplementary material]
@inproceedings{kuzborskij2016when, title={{W}hen {N}aive {B}ayes {N}earest {N}eighbours {M}eet {C}onvolutional {N}eural {N}etworks}, author={Kuzborskij, I. and Carlucci, F. M. and Caputo, B.}, booktitle={Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on}, year={2016} }
Transfer Learning through Greedy Subset Selection
(best paper award)
I. Kuzborskij, F. Orabona, and B. Caputo
International Conference on Image Analysis and Processing (ICIAP), 2015.
[PDF]
[Bibtex]
[Code]
@inproceedings{kuzborskij2015transfer, author = {I. Kuzborskij and F. Orabona and B. Caputo}, title = {Transfer Learning Through Greedy Subset Selection}, booktitle = {Image Analysis and Processing - {ICIAP} 2015 - 18th International Conference, Proceedings, Part {I}}, pages = {3--14}, year = {2015}, }
Stability and Hypothesis Transfer Learning
I. Kuzborskij and F. Orabona
International Conference on Machine Learning (ICML), 2013.
[PDF]
[Bibtex]
[Errata]
@inproceedings{kuzborskij2013stability, author = {I. Kuzborskij and F. Orabona}, title = {Stability and {H}ypothesis {T}ransfer {L}earning}, booktitle = {International Conference on Machine Learning (ICML)}, pages = {942--950}, year = {2013} }
From N to N+1: Multiclass Transfer Incremental Learning
I. Kuzborskij, F. Orabona, and B. Caputo
Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
[PDF]
[Bibtex]
[Code]
[Supplementary material]
@inproceedings{kuzborskij2013from, title = {From {N} to {N}+1: {M}ulticlass {T}ransfer {I}ncremental {L}earning}, author = {Kuzborskij, I. and Orabona, F. and Caputo, B.}, booktitle={Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on}, pages={3358--3365}, year={2013}, organization={IEEE} }
Other topics
Characterization of a Benchmark Database for Myoelectric Movement Classification.
M. Atzori, A. Gijsberts, I. Kuzborskij, S. Elsig, A.-G. Mittaz Hager, O. Deriaz,
C. Castellini,
H. Muller,
B. Caputo
Transactions on Neural Systems and Rehabilitation Engineering, 2015.
[PDF]
[Bibtex]
@article{atzori2015characterization, title={Characterization of a {B}enchmark {D}atabase for {M}yoelectric {M}ovement {C}lassification}, author={Atzori, M. and Gijsberts, A. and Kuzborskij, I. and Elsig, S. and Mittaz Hager, A.-G. and Deriaz, O. and Castellini, C. and Muller, H. and Caputo, B.}, journal={Neural Systems and Rehabilitation Engineering, IEEE Transactions on}, volume={23}, number={1}, pages={73--83}, year={2015}, publisher={IEEE} }
On the Challenge of Classifying 52 Hand Movements from Surface Electromyography.
I. Kuzborskij, A. Gijsberts, B. Caputo
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012.
[PDF]
[Bibtex]
@inproceedings{kuzborskij2012challenge, title={On the {C}hallenge of {C}lassifying 52 {H}and {M}ovements from {S}urface {E}lectromyography}, author={Kuzborskij, I. and Gijsberts, A. and Caputo, B.}, booktitle={Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE}, pages={4931--4937}, year={2012}, organization={IEEE} }
Tech reports
Efron-Stein PAC-Bayesian Inequalities
I. Kuzborskij and Cs. Szepesvári
arXiv. September, 2019.
[PDF]
[Bibtex]
@misc{kuzborskij2019efron, title = {Efron-{S}tein {PAC}-{B}ayesian {I}nequalities}, author = {Kuzborskij, Ilja and Szepesvári, Csaba}, howpublished = {arXiv:1909.01931}, year = {2019}, pdf = {https://arxiv.org/pdf/1909.01931}, url = {https://arxiv.org/abs/1909.01931} }
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
I. Kuzborskij and Cs. Szepesvári
arXiv. December, 2022.
[PDF]
[Bibtex]
@misc{kuzborskij2022learning, title = {Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks}, author = {Kuzborskij, Ilja and Szepesvári, Csaba}, howpublished = {arXiv:2212.13848}, year = {2022}, pdf = {https://arxiv.org/pdf/2212.13848}, url = {https://arxiv.org/abs/2212.13848} }
Mitigating LLM Hallucinations via Conformal Abstention
Y. Abbasi-Yadkori, I. Kuzborskij, D. Stutz, A. György, A. Fisch, A. Doucet,
I. Beloshapka, W.-H. Weng, Y.-Y. Yang, Cs. Szepesvári, A. T. Cemgil, N. Tomasev
arXiv. April, 2024.
[PDF]
[Bibtex]
@misc{yadkori2024mitigating, title={Mitigating LLM Hallucinations via Conformal Abstention}, author={Yasin Abbasi Yadkori and Ilja Kuzborskij and David Stutz and András György and Adam Fisch and Arnaud Doucet and Iuliya Beloshapka and Wei-Hung Weng and Yao-Yuan Yang and Csaba Szepesvári and Ali Taylan Cemgil and Nenad Tomasev}, year={2024}, eprint={2405.01563}, archivePrefix={arXiv}, primaryClass={cs.LG} }