I am a research scientist at DeepMind, London.
I am interested in problem-dependent (distribution/sample/instance-dependent) analysis and design of learning algorithms in
statistical, non-stochastic online, partial feedback, and nonparametric prediction models.
Previously, I spent one great year as a postdoc with Nicolò Cesa-Bianchi.
Before that I finished PhD at EPFL and Idiap Research Institute, advised by Barbara Caputo and Francesco Orabona.
You can contact me at firstname.lastname@gmail.com
Tech reports
I. Kuzborskij and Cs. Szepesvári. Efron-Stein PAC-Bayesian Inequalities
arXiv. September 2019.
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[ 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} }
I. Kuzborskij and Cs. Szepesvári.
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
arXiv. December 2022.
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[ 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} }
K. Jang,
K.-S. Jung,
I. Kuzborskij,
and F. Orabona.
Tighter PAC-Bayes Bounds Through Coin-Betting
arXiv. February 2023.
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[ BibTex ]
@misc{jang2023tighter, title={Tighter PAC-Bayes Bounds Through Coin-Betting}, author={Jang, Kyoungseok and Jun, Kwang-Sung and Kuzborskij, Ilja and Orabona, Francesco}, howpublished={arXiv:2302.05829}, year={2023}, pdf = {https://arxiv.org/pdf/2302.05829}, url = {https://arxiv.org/abs/2302.05829} }
Publications
Machine Learning
D. Richards and I. Kuzborskij.
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
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} }
I. Kuzborskij,
Cs. Szepesvári,
O. Rivasplata,
A. Rannen Triki,
and
R. Pascanu.
On the Role of Optimization in Double Descent: A Least Squares Study
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} }
M. Konobeev, I. Kuzborskij, and Cs. Szepesvári.
A Distribution-dependent Analysis of Meta Learning
International Conference on Machine Learning (ICML) 2021.
[ PDF ]
[ Supplementary / Code ]
[ BibTex ]
@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} }
I. Kuzborskij,
C. Vernade,
A. György, and
Cs. Szepesvári.
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
International Conference on Artificial Intelligence and Statistics (AISTATS) 2021.
[ PDF (revised version) ]
[ Code ]
[ BibTex ]
@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} }
I. Kuzborskij and N. Cesa-Bianchi. Locally-Adaptive Nonparametric Online Learning
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} }
O. Rivasplata
I. Kuzborskij,
Cs. Szepesvári, and
J. Shawe-Taylor.
PAC-Bayes Analysis Beyond the Usual Bounds
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} }
I. Kuzborskij, N. Cesa-Bianchi, and Cs. Szepesvári. Distribution-Dependent Analysis of Gibbs-ERM Principle.
Conference on Learning Theory (COLT), Phoenix, AZ, USA. June 2019.
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[ 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} }
I. Kuzborskij, L. Cella, and N. Cesa-Bianchi. Efficient Linear Bandits through Matrix Sketching.
International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Japan. April 2019.
[ PDF (revised version) ]
[ 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} }
I. Kuzborskij and C. H. Lampert. Data-Dependent Stability of Stochastic Gradient Descent.
International Conference on Machine Learning (ICML), Stockholm, Sweden. July 2018.
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[ 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} }
I. Kuzborskij and N. Cesa-Bianchi. Nonparametric Online Regression while Learning the Metric.
Advances in Neural Information Processing Systems, Long Beach, CA, USA. December 2017.
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[ 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} }
I. Kuzborskij and F. Orabona. Fast Rates by Transferring from Auxiliary Hypotheses.
Machine Learning, September 2016.
[ PDF ]
[ Link ]
[ 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. Scalable Greedy Algorithms for Transfer Learning.
Computer Vision and Image Understanding, 2016.
[ PDF ]
[ Link ]
[ 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", }
I. Kuzborskij, F.M. Carlucci, and B. Caputo. When Naïve Bayes Nearest Neighbors Meet Convolutional Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. June 2016.
[ PDF ]
[ Supplementary ]
[ BibTex ]
[ Code ]
@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} }
I. Kuzborskij, F. Orabona, and B. Caputo. Transfer Learning through Greedy Subset Selection. Best Paper Award
International Conference on Image Analysis and Processing (ICIAP), Genova, Italy. September 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}, }
I. Kuzborskij and F. Orabona. Stability and Hypothesis Transfer Learning.
International Conference on Machine Learning (ICML), Atlanta, GA, USA. June 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} }
I. Kuzborskij, F. Orabona, and B. Caputo. From N to N+1: Multiclass Transfer Incremental Learning.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA. June 2013.
[ PDF ]
[ BibTex ]
[ Code ]
[ Supplement ]
@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
M. Atzori, A. Gijsberts, I. Kuzborskij, S. Elsig, A.-G. Mittaz Hager, O. Deriaz,
C. Castellini,
H. Muller,
B. Caputo.
Characterization of a Benchmark Database for Myoelectric Movement Classification.
IEEE 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} }
I. Kuzborskij, A. Gijsberts, B. Caputo.
On the Challenge of Classifying 52 Hand Movements from Surface Electromyography.
International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA. August 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} }