Francesco Orabona
B francesco@orabona.com

Associate Professor at KAUSTÍ http://francesco.orabona.com

Curriculum Vitae
Francesco Orabona is an Associate Professor at King Abdullah University of Science and Technology, Saudi
Arabia. He received his PhD in “Bioengineering, Materials Engineering, and Robotics” from the University of
Genoa, Italy, in 2007. His main research interests are theory and applications of machine learning and online
learning algorithms.
As a result of his activities, Dr. Orabona has published more than 80 papers in scientific journals, conferences,and book chapters. His work explores both practical and theoretical aspects.
Journal Papers
Conference and Workshop Papers
H-index
Citations

17
66
37
4863

Published between 2007 and 2023
Published between 2005 and 2023
Source: Google Scholar (6 Aug 2023)
Source: Google Scholar (6 Aug 2023)

Affiliations
7/23–Present KAUST, (SA), Associate Professor in the CEMSE division.
9/22–6/23 Boston University, (MA, USA), Secondary affiliation with the Faculty of Computing & Data
Sciences.
5/21–6/23 Boston University, (MA, USA), Associate Professor in the Department of ECE.
2/20–8/22 Boston University, (MA, USA), Founding Faculty Member of the Faculty of Computing &Data Sciences.
9/19–present Boston University, (MA, USA), Secondary affiliation with the Division of Systems
Engineering.
9/18–present
9/18–present
9/18–5/21
9/16–8/18
10/14–8/16
10/11–9/14
2/11–4/11
1/10–9/11
4/07–12/09
3/06–4/06
1/04–3/07

Boston University, (MA, USA), Courtesy affiliation with the CS Department.
Boston University, (MA, USA), Data Science Faculty Fellow.
Boston University, (MA, USA), Assistant Professor in the Department of ECE.
Stony Brook University, (NY, USA), Assistant Professor in the CS Department.
Yahoo Labs, (New York, NY, USA), Senior Research Scientist.
Toyota Technological Institute, (Chicago, IL, USA), Research Assistant Professor.
Technion, (Haifa, Israel), Visiting Researcher.
University of Milan, (Milan, Italy), Postdoctoral Researcher.
Idiap Research Institute, (Martigny, Switzerland), Postdoctoral Researcher.
Helsinki University of Technology, (Helsinki, Finland), Visiting Researcher.
University of Genoa, (Italy), Research Assistant.

Education
Apr 2007 University of Genoa, (Italy), PhD in “Bioengineering, Materials Engineering, and Robotics”.
Jul 2003 University of Naples “Federico II”, (Italy), Laurea Degree (BS+MS) in Electrical Engineering, 110/110 “magna cum laude”.

AwardsFeb 2021 NSF CAREER Award
Dec 2017 Bell Labs Prize Finalist, 9 out of more than 350 hundred proposals
Feb 2017 Google Research Award

Sept 2015 Best Paper Award in the International Conference on Image Analysis and Processing
2008 & 2009 Ranked first in the ImageCLEF Medical Annotation Task (www.imageclef.org)
June 2005 Best Paper Award in International Workshop on Attention and Performance in Computational
Vision (in CVPR 2005)
1992 Ranked first in the “LEGO World Cup”

Research Funding2021-2026 NSF, CAREER: “Parameter-free Optimization Algorithms for Machine Learning”, PI, Total$583,898.
2020-2025 NSF (TRIPODS), Collaborative Research: “Foundations of Data Science Institute”, PI,
Total $15M, BU $346,540.
2019-2022 NSF, Collaborative Research: “New Representations for Learning Algorithms and Secure
Computation”, Co-PI, Total $400,000, BU $199,994.
2018-2020 NSF (TRIPODS), Collaborative Research: “TRIPODS Institute for Optimization andLearning”, PI, Total $1,484,152, BU $288,482.
2017-2018 Google Research Award, “Parameter-free Stochastic Minimization of Non-convex Functions”,
PI, Total $41,500.

Publications (* denotes alphabetical ordering of the authors)Peer Reviewed Journals- [J17] Z. Zhuang, M. Liu, A. Cutkosky, and F. Orabona. “Understanding AdamW through Proximal
Methods and Scale-Freeness”. In: Transactions on Machine Learning Research (2022). url: https ://openreview.net/forum?id=IKhEPWGdwK* [J16] N. Cesa-Bianchi and F. Orabona. “Online Learning Algorithms”. In: Annual Review of Statistics and
Its Application 8 (2021)- [J15] T. Hazan, F. Orabona, A. D. Sarwate, S. Maji, and T. S. Jaakkola. “High Dimensional Inferencewith Random Maximum A-Posteriori Perturbations”. In: IEEE Trans. Information Theory 65.10 (2019),
pp. 6539–6560* [J14] F. Orabona and D. Pál. “Scale-free online learning”. In: Theoretical Computer Science 716 (2018).
Special Issue on ALT 2015, pp. 50–69- [J13] K.-S. Jun, F. Orabona, S. Wright, and R. Willett. “Online Learning for Changing Environments Using
Coin Betting”. In: Electron. J. Statist. 11.2 (2017), pp. 5282–5310- [J12] I. Kuzborskij, F. Orabona, and B. Caputo. “Scalable Greedy Algorithms for Transfer Learning”. In:
Computer Vision and Image Understanding 156 (Mar. 2017), pp. 174–185- [J11] I. Kuzborskij and F. Orabona. “Fast Rates by Transferring from Auxiliary Hypotheses”. In: Machine
Learning 106 (2 2017), pp. 171–195- [J10] F. Orabona, K. Crammer, and N. Cesa-Bianchi. “A generalized online mirror descent with applications
to classification and regression”. In: Machine Learning 99 (3 June 2015), pp. 411–435* [J9] C. Gentile and F. Orabona. “On Multilabel Classification and Ranking with Bandit Feedback”. In:
Journal of Machine Learning Research 15 (July 2014), pp. 2451–2487- [J8] T. Tommasi, F. Orabona, and B. Caputo. “Learning Categories from Few Examples with Multi Model
Knowledge Transfer”. In: IEEE Trans. on Pattern Analysis and Machine Intelligence 36.5 (May 2014),
pp. 928–941- [J7] T. Tommasi, F. Orabona, C. Castellini, and B. Caputo. “Improving Control of Dexterous Hand
Prostheses Using Adaptive Learning”. In: IEEE Trans. on Robotics 29.1 (Feb. 2013), pp. 207–219- [J6] F. Orabona, L. Jie, and B. Caputo. “Multi Kernel Learning with Online-Batch Optimization”. In:
Journal of Machine Learning Research 13 (2012), pp. 227–253- [J5] F. Orabona, C. Castellini, B. Caputo, L. Jie, and G. Sandini. “On-line Independent Support Vector
Machines”. In: Pattern Recognition 43.4 (2010), pp. 1402–1412- [J4] E. Grossmann, J. A. Gaspar, and F. Orabona. “Discrete camera calibration from pixel streams”. In:
Computer Vision and Image Understanding 114.2 (2010). Special issue on Omnidirectional Vision, Camera
Networks and Non-conventional Cameras, pp. 198–209- [J3] F. Orabona, J. Keshet, and B. Caputo. “Bounded Kernel-Based Online Learning”. In: Journal ofMachine Learning Research 10 (2009), pp. 2571–2594- [J2] T. Tommasi, F. Orabona, and B. Caputo. “Discriminative Cue Integration for Medical Image Annotation”.
In: Pattern Recognition Letters 19.15 (2008), pp. 1996–2002 (Winner of the Medical automatic
annotation task ImageCLEF 2008)- [J1] C. Castellini, F. Orabona, G. Metta, and G. Sandini. “Internal models of reaching and grasping”. In:
Advanced Robotics 21.13 (2007), pp. 1545–1564

Book Chapters- [B5] T. Tatiana and F. Orabona. “Idiap on Medical Image Classification”. In: ImageCLEF. vol. 32. The
Information Retrieval Series. Berlin/Heidelberg: Springer, 2010, pp. 455–467- [B4] T. Tommasi, F. Orabona, and B. Caputo. “An SVM Confidence-Based Approach to Medical Image
Annotation”. In: Evaluating Systems for Multilingual and Multimodal Information Access. Vol. 5706. Lecture
Notes in Computer Science. Berlin/Heidelberg: Springer, 2009, pp. 696–703 (Winner of the Medical
automatic annotation task ImageCLEF 2009)- [B3] T. Tommasi, F. Orabona, and B. Caputo. “Cue Integration for Medical Image Annotation”. In:
Advances in Multilingual and Multimodal Information Retrieval. Vol. 5152. Lecture Notes in Computer
Science. Berlin/Heidelberg: Springer, 2008, pp. 577–584- [B2] F. Orabona, G. Metta, and G. Sandini. “A Proto-Object Based Visual Attention Model”. In: Attention
in Cognitive Systems. Vol. 4840. Lecture Notes in Artificial Intelligence. Berlin/Heidelberg: Springer, 2007,
pp. 198–215- [B1] L. Natale, F. Orabona, G. Metta, and G. Sandini. “Sensorimotor coordination in a “baby” robot:
learning about objects through grasping”. In: Progress in Brain Research, From Action to Cognition. Vol. 164.
Elsevier, 2007

Peer Reviewed Conferences and Workshops* [C66] K. Jang, K.-S. Jun, I. Kuzborskij, and F. Orabona. “Improved Time-Uniform PAC-Bayes Bounds
using Coin-Betting”. In: ICML workshop on PAC-Bayes Meets Interactive Learning. 2023
- [C65] K. Chen, and F. Orabona. “Implicit Interpretation of Importance Weight Aware Updates”. In: ICML
Workshop on Duality Principles for Modern Machine Learning. 2023
* [C64] A. Cutkosky, H. Mehta, and F. Orabona. “Optimal Stochastic Non-smooth Non-convex Optimization
through Online-to-Non-convex Conversion”. In: Proc. of the International Conference on Machine Learning(ICML). 2023
- [C63] K. Chen, and F. Orabona. “Generalized Implicit Follow-The-Regularized-Leader”. In: Proc. of theInternational Conference on Machine Learning (ICML). 2023
* [C62] K. Jang, K.-S. Jun, I. Kuzborskij, and F. Orabona. “Tighter PAC-Bayes Bounds Through Coin-Betting”.
In: Proc. of the Annual Conference on Learning Theory (COLT). 2023
* [C61] M. Crawshaw, M. Liu, F. Orabona, W. Zhang, and Z. Zhuang. “Robustness to Unbounded Smoothnessof Generalized SignSGD”. in: Advances in Neural Information Processing Systems. 2022
- [C60] K. Chen, A. Cutkosky, and F. Orabona. “Implicit Parameter-free Online Learning with TruncatedLinear Models”. In: Proc. of the International Conference on Algorithmic Learning Theory (ALT). 2022
- [C59] X. Li, M. Liu, and F. Orabona. “On the Last Iterate Convergence of Momentum Methods”. In: Proc.
of the International Conference on Algorithmic Learning Theory (ALT). 2022
- [C58] M. Liu and F. Orabona. “On the Initialization for Convex-Concave Min-max Problems”. In: Proc. of
the International Conference on Algorithmic Learning Theory (ALT). 2022
- [C57] K. Chen, J. Langford, and F. Orabona. “Better Parameter-free Stochastic Optimization with ODE
Updates for Coin-Betting”. In: Proc. of the AAAI Conference on Artificial Intelligence (AAAI). 2022
- [C56] J. Negrea, B. Bilodeau, N. Campolongo, F. Orabona, and D. M. Roy. “Minimax Optimal Quantile andSemi-Adversarial Regret via Root-Logarithmic Regularizers”. In: Advances in Neural Information Processing
Systems. 2021
- [C55] G. Flaspohler, F. Orabona, J. Cohen, S. Mouatadid, M. Oprescu, P. Orenstein, and L. Mackey. “Online
Learning with Optimism and Delay”. In: Proc. of the International Conference on Machine Learning(ICML). 2021
- [C54] X. Li, Z. Zhuang, and F. Orabona. “A Second look at Exponential and Cosine Step Sizes: Simplicity,
Convergence, and Performance”. In: Proc. of the International Conference on Machine Learning (ICML).
2021- [C53] N. Campolongo and F. Orabona. “Temporal Variability in Implicit Online Learning”. In: Advances in
Neural Information Processing Systems. Ed. by H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, andH. Lin. Vol. 33. Curran Associates, Inc., 2020, pp. 12377–12387- [C52] X. Li and F. Orabona. “A High Probability Analysis of Adaptive SGD with Momentum”. In: ICML
Workshop on Beyond First Order Methods in ML Systems. 2020
- [C51] K.-S. Jun, A. Cutkosky, and F. Orabona. “Kernel Truncated Randomized Ridge Regression: Optimal
Rates and Low Noise Acceleration”. In: Advances in Neural Information Processing Systems 32. 2019
* [C50] A. Cutkosky and F. Orabona. “Momentum-Based Variance Reduction in Non-Convex SGD”. in:
Advances in Neural Information Processing Systems 32. 2019
* [C49] K.-S. Jun and F. Orabona. “Parameter-Free Locally Differentially Private Stochastic Subgradient
Descent”. In: NeurIPS Workshop on Privacy in Machine Learning. 2019
- [C48] Z. Zhuang, A. Cutkosky, and F. Orabona. “Surrogate Losses for Online Learning of Stepsizes in
Stochastic Non-Convex Optimization”. In: Proc. of the International Conference on Machine Learning(ICML). 2019
* [C47] K.-S. Jun and F. Orabona. “Parameter-free Online Convex Optimization with Sub-Exponential Noise”.
In: Proc. of the Annual Conference on Learning Theory (COLT). 2019
- [C46] X. Li and F. Orabona. “On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes”.
In: Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS). 2019
* [C45] A. Cutkosky and F. Orabona. “Black-Box Reductions for Parameter-free Online Learning in BanachSpaces”. In: Proc. of the Annual Conference on Learning Theory (COLT). 2018
* [C44] F. Orabona and T. Tommasi. “Training Deep Networks without Learning Rates Through Coin Betting”.
In: Advances in Neural Information Processing Systems 30. 2017√
* [C43] A. Beygelzimer, F. Orabona, and C. Zhang. “Efficient Online Bandit Multiclass Learning with Õ( T )Regret”. In: Proc. of the International Conference on Machine Learning (ICML). JMLR Proceedings.
JMLR.org, 2017
- [C42] K.-S. Jun, F. Orabona, R. Willett, and S. Wright. “Improved Strongly Adaptive Online Learning using
Coin Betting”. In: Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS).
2017* [C41] F. Orabona and D. Pál. “Coin Betting and Parameter-Free Online Learning”. In: Advances in Neural
Information Processing Systems 29. 2016
* [C40] F. Orabona and D. Pál. “Parameter-Free Convex Learning through Coin Betting”. In: Proceedings ofthe 2016 Workshop on Automatic Machine Learning. 2016, pp. 75–82- [C39] A. Gonen, F. Orabona, and S. Shalev-Shwartz. “Solving Ridge Regression using Sketched Preconditioned
SVRG”. in: Proc. of the International Conference on Machine Learning (ICML). JMLR Proceedings.
JMLR.org, 2016
- [C38] R. De Rosa, F. Orabona, and N. Cesa-Bianchi. “The ABACOC Algorithm: a Novel Approach for
Nonparametric Classification of Data Streams”. In: Proc. of the IEEE International Conference on Data
Mining (ICDM). IEEE, 2015
* [C37] F. Orabona and D. Pál. “Scale-Free Algorithms for Online Linear Optimization”. In: Proc. of
the International Conference on Algorithmic Learning Theory (ALT). Lecture Notes in Computer Science.
Springer, 2015
- [C36] I. Kuzborskij, F. Orabona, and B. Caputo. “Transfer Learning through Greedy Subset Selection”. In:
Proc. of the International Conference on Image Analysis and Processing (ICIAP). vol. 9279. Lecture Notes
in Computer Science. Springer, 2015 (Best Paper Award)- [C35] F. Orabona. “Simultaneous Model Selection and Optimization through Parameter-free Stochastic
Learning”. In: Advances in Neural Information Processing Systems 27. 2014
* [C34] B. McMahan and F. Orabona. “Unconstrained Online Linear Learning in Hilbert Spaces: Minimax
Algorithms and Normal Approximations”. In: Proc. of the Annual Conference on Learning Theory (COLT).
vol. 35. JMLR Proceedings. JMLR.org, 2014, pp. 1020–1039- [C33] F. Orabona, T. Hazan, A. D. Sarwate, and T. Jaakkola. “On Measure Concentration of Random
Maximum A-Posteriori Perturbations”. In: Proc. of the International Conference on Machine Learning(ICML). vol. 32. JMLR Proceedings. JMLR.org, 2014, pp. 432–440- [C32] F. Orabona. “Dimension-free Exponentiated Gradient”. In: Advances in Neural Information Processing
Systems 26. Curran Associates, Inc., 2013, pp. 1806–1814* [C31] S. Kpotufe and F. Orabona. “Regression-tree Tuning in a Streaming Setting”. In: Advances in Neural
Information Processing Systems 26. Curran Associates, Inc., 2013, pp. 1788–1796- [C30] M. Fornoni, B. Caputo, and F. Orabona. “Multiclass Latent Locally Linear Support Vector Machines”.
In: Proc. of the Asian Conference on Machine Learning (ACML). vol. 29. JMLR Proceedings. JMLR.org,

2013, pp. 229–244- [C29] I. Kuzborskij and F. Orabona. “Stability and Hypothesis Transfer Learning”. In: Proc. of theInternational Conference on Machine Learning (ICML). vol. 28. JMLR Proceedings. JMLR.org, 2013,
pp. 942–950- [C28] I. Kuzborskij, F. Orabona, and B. Caputo. “From N to N+1: Multiclass Transfer Incremental Learning”.
In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013,
pp. 3358–3365* [C27] C. Gentile and F. Orabona. “On Multilabel Classification and Ranking with Partial Feedback”. In:
Advances in Neural Information Processing Systems 25. Curran Associates, Inc., 2012, pp. 1151–1159- [C26] T. Tommasi, F. Orabona, M. Kaboli, and B. Caputo. “Leveraging over prior knowledge for online
learning of visual categories”. In: Proc. of the British Machine Vision Conference (BMVC). BMVA Press,
2012, pp. 1–11
- [C25] F. Orabona, N. Cesa-Bianchi, and C. Gentile. “Beyond Logarithmic Bounds in Online Learning”. In:
Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS). vol. 22. JMLR
Proceedings. JMLR.org, 2012, pp. 823–831- [C24] F. Orabona and N. Cesa-Bianchi. “Better Algorithms for Selective Sampling”. In: Proc. of theInternational Conference on Machine Learning (ICML). Omnipress, 2011, pp. 433–440- [C23] F. Orabona and L. Jie. “Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning”. In:
Proc. of the International Conference on Machine Learning (ICML). Omnipress, 2011, pp. 249–256- [C22] F. Orabona and K. Crammer. “New Adaptive Algorithms for Online Classification”. In: Advances in
Neural Information Processing Systems 23. 2010, pp. 1840–1848* [C21] L. Jie and F. Orabona. “Learning from Candidate Labeling Sets”. In: Advances in Neural Information
Processing Systems 23. 2010, pp. 1504–1512- [C20] F. Orabona, L. Jie, and B. Caputo. “Online-Batch Strongly Convex Multi Kernel Learning”. In: The
23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010, pp. 787–794- [C19] T. Tommasi, F. Orabona, and B. Caputo. “Safety in Numbers: Learning Categories from Few Examples
with Multi Model Knowledge Transfer”. In: The 23rd IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). IEEE, 2010, pp. 3081–3088- [C18] L. Jie, F. Orabona, M. Fornoni, B. Caputo, and N. Cesa-Bianchi. “OM-2: An Online Multi-classMulti-kernel Learning Algorithm”. In: Proc. of the 4th IEEE Online Learning for Computer Vision Workshop(in CVPR’10). IEEE Computer Society, June 2010, pp. 43–50- [C17] M. M. Ullah, F. Orabona, and B. Caputo. “You Live, You Learn, You Forget: Continuous Learning ofVisual Places with a Forgetting Mechanism”. In: IEEE/RSJ International Conference on Intelligent RObots
and Systems, IROS. IEEE Press, 2009, pp. 3154–3161- [C16] L. Jie, F. Orabona, and B. Caputo. “An Online Framework for Learning Novel Concepts over Multiple
Cues”. In: Computer Vision - ACCV 2009, 9th Asian Conference on Computer Vision, Xi’an, China,
September 23-27, 2009, Revised Selected Papers, Part I. vol. 5994. Lecture Notes in Computer Science.
Berlin/Heidelberg: Springer, 2010
* [C15] N. Cesa-Bianchi, C. Gentile, and F. Orabona. “Robust Bounds for Classification via Selective Sampling”.
In: Proc. of the Annual International Conference on Machine Learning (ICML). vol. 382. ACM International
Conference Proceeding Series. ACM, 2009, pp. 121–128- [C14] F. Orabona, B. Caputo, A. Fillbrandt, and F. W. Ohl. “A Theoretical Framework for Transfer ofKnowledge Across Modalities in Artificial and Biological Systems”. In: Proc. of the International Conferenceon Development and Learning (ICDL). Washington, DC, USA: IEEE Computer Society, June 2009, pp. 1–7- [C13] F. Orabona, C. Castellini, B. Caputo, A. E. Fiorilla, and G. Sandini. “Model Adaptation with
Least-Squares SVM for Adaptive Hand Prosthetics”. In: Proc. of the International Conference on Robotics
and Automation (ICRA). IEEE Press, May 2009, pp. 439–445- [C12] T. Tommasi, F. Orabona, and B. Caputo. “CLEF2008 Image Annotation Task: an SVM ConfidenceBased Approach”. In: Working Notes of the 2008 CLEF Workshop. 2008
- [C11] E. Grossmann, J. A. Gaspar, and F. Orabona. “Calibration from statistical properties of the visual
world”. In: Proc. of European Conference on Computer Vision (ECCV). Berlin/Heidelberg: Springer-Verlag,
2008, pp. 228–241- [C10] F. Orabona, J. Keshet, and B. Caputo. “The Projectron: a Bounded Kernel-Based Perceptron”. In:
Proc. of the International Conference on Machine Learning (ICML). vol. 307. ACM International Conference
Proceeding Series. ACM, 2008, pp. 720–727* [C9] J. Anemueller, J-H. Bach, B. Caputo, L. Jie, F. Ohl, F. Orabona, R. Vogel, D. Weinshall, and A. Zweig.

“Biologically Motivated Audio-Visual Cue Integration for Object Categorization”. In: Proc. of International
Conference on Cognitive Systems. 2008
- [C8] E. Grossmann, F. Orabona, and J.A. Gaspar. “Discrete camera calibration from the information distance
between pixel streams”. In: Proc. of 7th Workshop on Omnidirectional Vision (in ICCV’07). 2007, pp. 1–8- [C7] F. Orabona, C. Castellini, B. Caputo, J. Luo, and G. Sandini. “Indoor Place Recognition using Online
Independent Support Vector Machines”. In: Proc. of the British Machine Vision Conference (BMVC).
BMVA, 2007, pp. 1090–1099- [C6] T. Tommasi, F. Orabona, and B. Caputo. “CLEF2007 Image Annotation Task: an SVM-based Cue
Integration Approach”. In: In Working Notes of the 2007 CLEF Workshop. 2007
- [C5] M. Vurro, G. Baselli, F. Orabona, and G. Sandini. “Simulation and Assessment of Bioinspired Visual
Processing System for Epi-retinal Prostheses”. In: Proc. of the Annual International Conference of the IEEE
Engineering in Medicine and Biology Society. 2006
- [C4] F. Orabona, G. Metta, and G. Sandini. “Learning Association Fields from Natural Images”. In: Proc.
of the Conference on Computer Vision and Pattern Recognition – Workshop. Washington, DC, USA: IEEE
Computer Society, 2006
- [C3] L. Natale, F. Orabona, F. Berton, G. Metta, and G. Sandini. “From sensorimotor development to object
perception”. In: Proc. of the International Conference on Humanoid Robots (Humanoids). 2005, pp. 226–231- [C2] L. Natale, F. Orabona, G. Metta, and G. Sandini. “Exploring the world through grasping: a developmental approach”. In: Proc. of the IEEE International Symposium on Computational Intelligence in
Robotics and Automation (CIRA). 2005, pp. 559–565- [C1] F. Orabona, G. Metta, and G. Sandini. “Object-based Visual Attention: a Model for a Behaving Robot”.
In: Proc. of the Conference on Computer Vision and Pattern Recognition – Workshops. Washington, DC,
USA: IEEE Computer Society, 2005 (Best Paper Award)

Others- [T6] F. Orabona and K.-S. Jun. Tight Concentrations and Confidence Sequences from the Regret of Universal
Portfolio. 2021. arXiv: 2110.14099 [stat.ML]- [T5] F. Orabona. A Modern Introduction to Online Learning. 2019. arXiv: 1912.13213 [cs.LG]* [T4] F. Orabona and D. Pál. Optimal Non-Asymptotic Lower Bound on the Minimax Regret of Learningwith Expert Advice. 2015. arXiv: 1511.02176 [stat.ML]- [T3] F. Orabona. A Simple Expression for Mill’s Ratio of the Student’s t-Distribution. 2015. arXiv:
1502.01632 [cs.LG]- [T2] F. Orabona, A. Argyriou, and N. Srebro. PRISMA: PRoximal Iterative SMoothing Algorithm. 2012.
arXiv: 1206.2372 [math.OC]- [T1] F. Orabona. DOGMA: a MATLAB toolbox for Online Learning. Software available at http://dogma.
sourceforge.net. 2009

Teaching activities
Spring Instructor, Boston University, Boston, MA, USA.
2023–2019 EC 503 Introduction to Learning from Data
Fall 2019, Instructor, Boston University, Boston, MA, USA.
2021–2022 EC 500 Introduction to Online Learning
Fall 2020 Instructor, Boston University, Boston, MA, USA.
EC 414 Introduction to Machine Learning

Spring 2018 Instructor, Stony Brook University, Stony Brook, NY, USA.
CSE 592 Convex Optimization

Spring 2017 Instructor, Stony Brook University, Stony Brook, NY, USA.
CSE 512 Machine Learning

Fall 2013 Instructor, Toyota Technological Institute and University of Chicago, Chicago, USA.
TTIC 31070 (CMSC 34500 Optimization/ BUSF 36903) Convex Optimization

2004, 2005 Teaching assistant, University of Genoa, Genoa, Italy.
Course “Biomedical Informatics”, Prof. V. Tagliasco, Bachelor in Biomedical EngineeringService to the Community and Scientific Activities{ Invited talks at international conferences, workshops and schools:- 2023, Parameter-Free Online Convex Optimization: Past, Present, and Future, Mini-symposium at SIAM
Conference on Optimization- 2022, Robustness to Unbounded Smoothness of Generalized SignSGD, Mini-symposium at SIAM Conference
on Mathematics of Data Science- 2020, Parameter-free Stochastic Optimization through Coin Betting, Minisymposium on Online Methods in
Inverse Problems and Imaging at 2020 SIAM, Online, July 2020
- 2019, Parameter-free Machine Learning through Coin Betting, Third International Conference on Econometrics and Statistics, National Chung Hsing University, Taiwan, June 2019
- 2019, Parameter-free Convex Stochastic Optimization through Coin Betting, Third International Conference
on Mathematics of Data Science, City University of Hong Kong, June 2019
- 2019, Coin Betting for Backprop without Learning Rates and More, New England Machine Learning Day,
Northeastern University, May 2019
- 2019, Parameter-free Machine Learning through Coin Betting, Geometric Analysis Approach to AI
Workshop, Harvard University, Jan 2019
- 2016, One Coin to Rule Them All: From Betting to Online Learning to Automatic Model Selection with
One Algorithm, Workshop on Theoretical Foundations for Learning from Easy Data, Lorentz Center,
Netherlands, November 7-11.
- 2015, Simultaneous Model Selection and Learning through Parameter-free Stochastic Gradient Descent,
MOPTA 2015, Lehigh University, PA, July 20-22.
- 2015, Non IID Data in Advertising, Machine Learning with Interdependent and Non-identically Distributed
Data, Dagstuhl, Germany, April 7-10.
- 2014, Simultaneous Model Selection and Learning through Parameter-free Stochastic Gradient Descent,
FOCM Workshop on Learning Theory, Montevideo, Uruguay, December 18-20.
- 2012, Efficient and Principled Online Algorithms for Lifelong Learning., IROS Workshop on Lifelong
Learning for Mobile Robotics Applications, Vilamoura, Portugal, Oct.
{ Invited research seminars in universities/research centers:- 2023, Two Birds with One Coin: Confidence Sequences and Convex Optimization with Coin-Betting ,Princeton- 2023, Understanding Adam and AdamW through proximal updates, scale-freeness, and relaxed smoothness,
Microsoft Research Asia
- 2022, Better Optimization Algorithms for Machine Learning, MBZUAI.
- 2022, Parameter-free Online Optimization: Past, Present, and Future, KAUST.
- 2022, Two Birds with One Coin: Convex Optimization and Confidence Sequences with Coin-Betting,Rutgers University.
- 2021, Parameter-free Stochastic Optimization of Variationally Coherent Functions, University of Genova;
University of Copenhagen; Purdue University; University of Pittsburgh.
- 2021, Parameter-free Machine Learning through Coin Betting, Italian Institute of Technology; BMW AI
Centre of Excellence.
- 2019, Parameter-free Machine Learning through Coin Betting, MSR Cambridge; University of Milan.
- 2018, Parameter-free Machine Learning through Coin Betting, Boston University; UMass Amherst; MSR
NY; Yahoo.
- 2017, Coin Betting for Backprop without Learning Rates and More, Google Research NY; Google Research
Zurich; DeepMind; MSR Cambridge; UMass Amherst; IBM Thomas J. Watson Research Center.
- 2017, One Coin to Rule Them All: From Betting to Online Learning to Automatic Model Selection with
One Algorithm, SUNY Albany; Princeton.
- 2016, From 1 to 1,000,000 Samples: Theoretically Principled Machine Learning Algorithms for Real-World
Applications. Stony Brook University.
- 2015, One Coin to Rule Them All: from Adversarial Betting to Adaptive Stochastic Optimization. Lehigh
University.
- 2015, Simultaneous Model Selection and Learning through Parameter-free Stochastic Gradient Descent.
Wisconsin Institute for Discovery.
- 2014, From 1 to 1,000,000 Samples: Theoretically Principled Machine Learning Algorithms for Real-World
Applications. University of Copenhagen; Tulane University.

- 2014, Efficient Learning through Online Algorithms. Dartmouth College; Yahoo Labs New York.
- 2014, Learning with Few Samples. Toyota Technological Institute (Nagoya).
- 2013, Adaptation in Online Learning through Dimension-free Exponentiated Gradient. University of Rome;
University of Illinois at Chicago; Columbia University; Microsoft Research New York.

- 2012, Selective sampling and ranking in the partial adversarial setting. German Aerospace Center (DLR);Max Planck Institute; Yahoo Labs Silicon Valley.

- 2012, Efficient Stochastic and Batch Optimization Algorithms. Katholieke Universiteit.
- 2011, On the Beauty of Online Selective Sampling. Idiap Research Institute.
- 2011, Efficient and Principled Learning Algorithms for Real World Problems. Toyota Technological Institute;
MIT.

- 2011-2010, Simple and Efficient Algorithms for Online and Stochastic Learning. Technion; Centre de Visió
per Computador.

- 2008, Bounded (and not) Online Learning with Kernels. Fondazione Bruno Kessler.
{ Tutorials:- Tutorial on Parameter-free Online Optimization. Co-organizer, ICML’20.
- Tutorial on Domain Adaptation and Transfer Learning. Co-organizer, ECCV’14.
{ Organizer of workshops:- Recent Advances in Machine Learning and Optimization at the SIAM Conference on Mathematics of Data
Science 2022.
- 5rd TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ECCV’18.
- 4rd TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ICCV’17.
- 3rd TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ECCV’16.
- 2nd TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ICCV’15.
- Transfer and Multi-task learning: Theory Meets Practice at NIPS’14.
- 1st TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ECCV’14.
- New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks at NIPS’13.
- Learning from Multiple Sources with Applications to Robotics at NIPS’09.
{ Editor:- Action Editor: IEEE Trans. Information Theory, 2022-.
- Action Editor: Journal of Machine Learning Research, 2020-.
- Co-Guest editor of the Journal of Machine Learning Research Special Topic on Multi Task Learning,
Domain Adaptation and Transfer Learning.
{ Program Chair of Conferences- Algorithmic Learning Theory (ALT) 2023
{ Member of the Program Committee- AAAI Conference on Artificial Intelligence (AAAI) 2020, 2024 Senior Area Chair- Conference On Learning Theory (COLT) 2017, 2019, 2021-2023 Area Chair- Neural Information Processing Systems 2016–2019, 2022 Area Chair; 2020, 2023 Senior Area Chair- International Conference on Machine Learning (ICML) 2019-2021, Area Chair- International Conference on Learning Representations (ICLR), 2021–2023, Area Chair- Artificial Intelligence and Statistics (AISTATS) 2016, 2017, 2019–2023, Area Chair- International Joint Conferences on Artificial Intelligence (IJCAI) 2019, Area Chair- Algorithmic Learning Theory (ALT) 2015, 2022, Senior PC member{ Reviewer- Several major journals, including: Journal of Machine Learning Research, Machine Learning Journal, Neural
Networks, Neural Computation, Neurocomputing, Pattern Recognition, Image and Vision Computing,IEEE Trans. on Neural Networks, IEEE Trans. on Information Theory.
- International Conferences: AISTATS 2011-2012; ECML 2012; ICML 2011-2015; NeurIPS 2008-2013,2015;
COLT 2010-2016,2018; ALT 2009,2012; ICRA 2010-2012; ICDL 2007-2008; ICLR 2017; AAAI 2022
{ Referee for NSF, and Israel Science Foundation

Advising{ Post Docs:- Mingrui Liu, 2020-2021, currently Assistant Prof. at George Mason University- Kwang-Sung Jun, 2018-2019, currently Assistant Prof. at University of Arizona

{ PhD Students:- Keyi Chen, CS, 2018- Zhenxun Zhuang, CS, 2018-2022, now Research Scientist at Meta
- Xiaoyu Li, SE, 2018-2022, now Data Scientist at Microsoft{ MS Students:- Krishna Chaitanya Chakka, 2016