Exploring the world through grasping: a developmental approach. - Lorenzo Natale; Francesco Orabona; Giorgio Metta; Giulio Sandini (CIRA, 2005)

Object-based Visual Attention: a Model for a Behaving Robot. - Francesco Orabona; Giorgio Metta; Giulio Sandini (CVPR Workshops, 2005)

From sensorimotor development to object perception. - Lorenzo Natale; Francesco Orabona; Fabio Berton; Giorgio Metta; Giulio Sandini (Humanoids, 2005)

Learning Association Fields from Natural Images. - Francesco Orabona; Giorgio Metta; Giulio Sandini (CVPR Workshops, 2006)

Simulation and Assessment of Bioinspired Visual Processing System for Epi-retinal Prostheses. - Milena Vurro; Giuseppe Baselli; Francesco Orabona; Giulio Sandini (EMBC, 2006)

Indoor Place Recognition using Online Independent Support Vector Machines. - Francesco Orabona; Claudio Castellini (BMVC, 2007)

Cue Integration for Medical Image Annotation. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (CLEF, 2007)

CLEF2007: Image Annotation Task: an SVM-based Cue Integration Approach. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (CLEF (Working Notes), 2007)

Discrete camera calibration from the information distance between pixel streams. - Etienne Grossmann; Francesco Orabona; José António Gaspar (ICCV, 2007)

A Proto-object Based Visual Attention Model. - Francesco Orabona; Giorgio Metta; Giulio Sandini (WAPCV, 2007)

Internal models of reaching and grasping. - Claudio Castellini; Francesco Orabona; Giorgio Metta; Giulio Sandini (Adv. Robotics, 2007)

An SVM Confidence-Based Approach to Medical Image Annotation. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (CLEF, 2008)

CLEF2008 Image Annotation Task: an SVM Confidence-Based Approach. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (CLEF (Working Notes), 2008)

Calibration from Statistical Properties of the Visual World. - Etienne Grossmann; José António Gaspar; Francesco Orabona (ECCV (4), 2008)

The projectron: a bounded kernel-based Perceptron. - Francesco Orabona; Joseph Keshet; Barbara Caputo (ICML, 2008)

Discriminative cue integration for medical image annotation. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (Pattern Recognit. Lett., 2008)

An Online Framework for Learning Novel Concepts over Multiple Cues. - Jie Luo; Francesco Orabona; Barbara Caputo (ACCV (1), 2009)

Robust bounds for classification via selective sampling. - Nicolò Cesa-Bianchi; Claudio Gentile; Francesco Orabona (ICML, 2009)

Model adaptation with least-squares SVM for adaptive hand prosthetics. - Francesco Orabona; Claudio Castellini; Barbara Caputo; Angelo Emanuele Fiorilla; Giulio Sandini (ICRA, 2009)

You live, you learn, you forget: Continuous learning of visual places with a forgetting mechanism. - Muhammad Muneeb Ullah; Francesco Orabona; Barbara Caputo (IROS, 2009)

Bounded Kernel-Based Online Learning. - Francesco Orabona; Joseph Keshet; Barbara Caputo (J. Mach. Learn. Res., 2009)

Idiap on Medical Image Classification. - Tatiana Tommasi; Francesco Orabona (ImageCLEF, 2010)

OM-2: An online multi-class Multi-Kernel Learning algorithm Luo Jie. - Francesco Orabona; Marco Fornoni; Barbara Caputo; Nicolò Cesa-Bianchi (CVPR Workshops, 2010)

Online-batch strongly convex Multi Kernel Learning. - Francesco Orabona; Jie Luo; Barbara Caputo (CVPR, 2010)

Safety in numbers: Learning categories from few examples with multi model knowledge transfer. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (CVPR, 2010)

Learning from Candidate Labeling Sets. - Jie Luo; Francesco Orabona (NIPS, 2010)

New Adaptive Algorithms for Online Classification. - Francesco Orabona; Koby Crammer (NIPS, 2010)

Discrete camera calibration from pixel streams. - Etienne Grossmann; José António Gaspar; Francesco Orabona (Comput. Vis. Image Underst., 2010)

On-line independent support vector machines. - Francesco Orabona; Claudio Castellini; Barbara Caputo; Jie Luo; Giulio Sandini (Pattern Recognit., 2010)

Better Algorithms for Selective Sampling. - Francesco Orabona; Nicolò Cesa-Bianchi (ICML, 2011)

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning. - Francesco Orabona; Jie Luo (ICML, 2011)

Leveraging over prior knowledge for online learning of visual categories. - Tatiana Tommasi; Francesco Orabona; Mohsen Kaboli; Barbara Caputo (BMVC, 2012)

On Multilabel Classification and Ranking with Partial Feedback. - Claudio Gentile; Francesco Orabona (NIPS, 2012)

PRISMA: PRoximal Iterative SMoothing Algorithm - Francesco Orabona; Andreas Argyriou; Nathan Srebro (CoRR, 2012)

Beyond Logarithmic Bounds in Online Learning. - Francesco Orabona; Nicolò Cesa-Bianchi; Claudio Gentile (AISTATS, 2012)

Multi Kernel Learning with Online-Batch Optimization. - Francesco Orabona; Jie Luo; Barbara Caputo (J. Mach. Learn. Res., 2012)

Multiclass Latent Locally Linear Support Vector Machines. - Marco Fornoni; Barbara Caputo; Francesco Orabona (ACML, 2013)

From N to N+1: Multiclass Transfer Incremental Learning. - Ilja Kuzborskij; Francesco Orabona; Barbara Caputo (CVPR, 2013)

Stability and Hypothesis Transfer Learning. - Ilja Kuzborskij; Francesco Orabona (ICML (3), 2013)

Regression-tree Tuning in a Streaming Setting. - Samory Kpotufe; Francesco Orabona (NIPS, 2013)

Dimension-Free Exponentiated Gradient. - Francesco Orabona (NIPS, 2013)

Improving Control of Dexterous Hand Prostheses Using Adaptive Learning. - Tatiana Tommasi; Francesco Orabona; Claudio Castellini; Barbara Caputo (IEEE Trans. Robotics, 2013)

Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. - H. Brendan McMahan; Francesco Orabona (COLT, 2014)

On Measure Concentration of Random Maximum A-Posteriori Perturbations. - Francesco Orabona; Tamir Hazan; Anand D. Sarwate; Tommi S. Jaakkola (ICML, 2014)

Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning. - Francesco Orabona (NIPS, 2014)

Learning by Transferring from Auxiliary Hypotheses. - Ilja Kuzborskij; Francesco Orabona (CoRR, 2014)

On multilabel classification and ranking with bandit feedback. - Claudio Gentile; Francesco Orabona (J. Mach. Learn. Res., 2014)

Learning Categories From Few Examples With Multi Model Knowledge Transfer. - Tatiana Tommasi; Francesco Orabona; Barbara Caputo (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Scale-Free Algorithms for Online Linear Optimization. - Francesco Orabona; Dávid Pál (ALT, 2015)

The ABACOC Algorithm: A Novel Approach for Nonparametric Classification of Data Streams. - Rocco De Rosa; Francesco Orabona; Nicolò Cesa-Bianchi (ICDM, 2015)

Transfer Learning Through Greedy Subset Selection. - Ilja Kuzborskij; Francesco Orabona; Barbara Caputo (ICIAP (1), 2015)

A Simple Expression for Mill's Ratio of the Student's t-Distribution. - Francesco Orabona (CoRR, 2015)

Optimal Non-Asymptotic Lower Bound on the Minimax Regret of Learning with Expert Advice. - Francesco Orabona; Dávid Pál (CoRR, 2015)

A generalized online mirror descent with applications to classification and regression. - Francesco Orabona; Koby Crammer; Nicolò Cesa-Bianchi (Mach. Learn., 2015)

Open Problem: Parameter-Free and Scale-Free Online Algorithms. - Francesco Orabona; Dávid Pál (COLT, 2016)

Solving Ridge Regression using Sketched Preconditioned SVRG. - Alon Gonen; Francesco Orabona; Shai Shalev-Shwartz (ICML, 2016)

Parameter-Free Convex Learning through Coin Betting. - Francesco Orabona; Dávid Pál (AutoML@ICML, 2016)

Coin Betting and Parameter-Free Online Learning. - Francesco Orabona; Dávid Pál (NIPS, 2016)

From Coin Betting to Parameter-Free Online Learning. - Francesco Orabona; Dávid Pál (CoRR, 2016)

Improved Strongly Adaptive Online Learning using Coin Betting. - Kwang-Sung Jun; Francesco Orabona; Stephen J. Wright 001; Rebecca Willett (AISTATS, 2017)

Efficient Online Bandit Multiclass Learning with Õ(√T) Regret. - Alina Beygelzimer; Francesco Orabona; Chicheng Zhang (ICML, 2017)

Training Deep Networks without Learning Rates Through Coin Betting. - Francesco Orabona; Tatiana Tommasi (NIPS, 2017)

Efficient Online Bandit Multiclass Learning with $ tilde{O}( sqrt{T})$ Regret. - Alina Beygelzimer; Francesco Orabona; Chicheng Zhang (CoRR, 2017)

Backprop without Learning Rates Through Coin Betting. - Francesco Orabona; Tatiana Tommasi (CoRR, 2017)

Online Learning for Changing Environments using Coin Betting. - Kwang-Sung Jun; Francesco Orabona; Stephen J. Wright 001; Rebecca Willett (CoRR, 2017)

Scalable greedy algorithms for transfer learning. - Ilja Kuzborskij; Francesco Orabona; Barbara Caputo (Comput. Vis. Image Underst., 2017)

Fast rates by transferring from auxiliary hypotheses. - Ilja Kuzborskij; Francesco Orabona (Mach. Learn., 2017)

Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. - Ashok Cutkosky; Francesco Orabona (COLT, 2018)

Scale-free online learning. - Francesco Orabona; Dávid Pál (Theor. Comput. Sci., 2018)

On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes. - Xiaoyu Li; Francesco Orabona (AISTATS, 2019)

Parameter-Free Online Convex Optimization with Sub-Exponential Noise. - Kwang-Sung Jun; Francesco Orabona (COLT, 2019)

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. - Zhenxun Zhuang; Ashok Cutkosky; Francesco Orabona (ICML, 2019)

Momentum-Based Variance Reduction in Non-Convex SGD. - Ashok Cutkosky; Francesco Orabona (NeurIPS, 2019)

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. - Kwang-Sung Jun; Ashok Cutkosky; Francesco Orabona (NeurIPS, 2019)

Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. - Kwang-Sung Jun; Francesco Orabona (CoRR, 2019)

A Modern Introduction to Online Learning. - Francesco Orabona (CoRR, 2019)

High Dimensional Inference With Random Maximum A-Posteriori Perturbations. - Tamir Hazan; Francesco Orabona; Anand D. Sarwate; Subhransu Maji; Tommi S. Jaakkola (IEEE Trans. Inf. Theory, 2019)

Temporal Variability in Implicit Online Learning. - Nicolò Campolongo; Francesco Orabona (NeurIPS, 2020)

Exponential Step Sizes for Non-Convex Optimization. - Xiaoyu Li; Zhenxun Zhuang; Francesco Orabona (CoRR, 2020)

A High Probability Analysis of Adaptive SGD with Momentum. - Xiaoyu Li; Francesco Orabona (CoRR, 2020)

Adam+: A Stochastic Method with Adaptive Variance Reduction. - Mingrui Liu; Wei Zhang 022; Francesco Orabona; Tianbao Yang (CoRR, 2020)

Online Learning with Optimism and Delay. - Genevieve Flaspohler; Francesco Orabona; Judah Cohen; Soukayna Mouatadid; Miruna Oprescu; Paulo Orenstein; Lester Mackey (ICML, 2021)

A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance. - Xiaoyu Li; Zhenxun Zhuang; Francesco Orabona (ICML, 2021)

Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. - Jeffrey Negrea; Blair L. Bilodeau; Nicolò Campolongo; Francesco Orabona; Daniel M. Roy 001 (NeurIPS, 2021)

Parameter-free Stochastic Optimization of Variationally Coherent Functions. - Francesco Orabona; Dávid Pál (CoRR, 2021)

A closer look at temporal variability in dynamic online learning. - Nicolò Campolongo; Francesco Orabona (CoRR, 2021)

A Parameter-free Algorithm for Convex-concave Min-max Problems. - Mingrui Liu; Francesco Orabona (CoRR, 2021)

Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. - Keyi Chen 001; John Langford 001; Francesco Orabona (AAAI, 2022)

Implicit Parameter-free Online Learning with Truncated Linear Models. - Keyi Chen 001; Ashok Cutkosky; Francesco Orabona (ALT, 2022)

On the Last Iterate Convergence of Momentum Methods. - Xiaoyu Li; Mingrui Liu; Francesco Orabona (ALT, 2022)

On the Initialization for Convex-Concave Min-max Problems. - Mingrui Liu; Francesco Orabona (ALT, 2022)

Robustness to Unbounded Smoothness of Generalized SignSGD. - Michael Crawshaw; Mingrui Liu; Francesco Orabona; Wei Zhang 022; Zhenxun Zhuang (NeurIPS, 2022)

Understanding AdamW through Proximal Methods and Scale-Freeness. - Zhenxun Zhuang; Mingrui Liu; Ashok Cutkosky; Francesco Orabona (Trans. Mach. Learn. Res., 2022)

Tighter PAC-Bayes Bounds Through Coin-Betting. - Kyoungseok Jang; Kwang-Sung Jun; Ilja Kuzborskij; Francesco Orabona (COLT, 2023)

Generalized Implicit Follow-The-Regularized-Leader. - Keyi Chen 001; Francesco Orabona (ICML, 2023)

Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. - Ashok Cutkosky; Harsh Mehta; Francesco Orabona (ICML, 2023)

Implicit Interpretation of Importance Weight Aware Updates. - Keyi Chen 001; Francesco Orabona (CoRR, 2023)

Normalized Gradients for All. - Francesco Orabona (CoRR, 2023)

Better-than-KL PAC-Bayes Bounds. - Ilja Kuzborskij; Kwang-Sung Jun; Yulian Wu; Kyoungseok Jang; Francesco Orabona (COLT, 2024)

Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. - Alexandru Meterez; Amir Joudaki; Francesco Orabona; Alexander Immer; Gunnar Rätsch; Hadi Daneshmand (ICLR, 2024)

An Equivalence Between Static and Dynamic Regret Minimization. - Andrew Jacobsen; Francesco Orabona (NeurIPS, 2024)

Self-Directed Learning of Convex Labelings on Graphs. - Georgy Sokolov; Maximilian Thiessen; Margarita Akhmejanova; Fabio Vitale; Francesco Orabona (CoRR, 2024)

Tight Concentrations and Confidence Sequences From the Regret of Universal Portfolio. - Francesco Orabona; Kwang-Sung Jun (IEEE Trans. Inf. Theory, 2024)

Self-Directed Node Classification on Graphs. - Georgy Sokolov; Maximilian Thiessen; Margarita Akhmejanova; Fabio Vitale; Francesco Orabona (ALT, 2025)

New Lower Bounds for Non-Convex Stochastic Optimization through Divergence Decomposition. - El Mehdi Saad; Wei-Cheng Lee; Francesco Orabona (COLT, 2025)

ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. - Artavazd Maranjyan; El Mehdi Saad; Peter Richtárik; Francesco Orabona (CoRR, 2025)

New Lower Bounds for Stochastic Non-Convex Optimization through Divergence Composition. - El Mehdi Saad; Weicheng Lee; Francesco Orabona (CoRR, 2025)

Optimal Regret of Bernoulli Bandits under Global Differential Privacy. - Achraf Azize; Yulian Wu; Junya Honda; Francesco Orabona; Shinji Ito; Debabrota Basu (CoRR, 2025)

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO. - Xingyu Zhou; Yulian Wu; Francesco Orabona (CoRR, 2025)

New Perspectives on the Polyak Stepsize: Surrogate Functions and Negative Results. - Francesco Orabona; Ryan D'Orazio (CoRR, 2025)

SquareχPO: Differentially Private and Robust χ2-Preference Optimization in Offline Direct Alignment. - Xingyu Zhou; Yulian Wu; Wenqian Weng; Francesco Orabona (CoRR, 2025)

STaR-Bets: Sequential Target-Recalculating Bets for Tighter Confidence Intervals. - Václav Vorácek; Francesco Orabona (CoRR, 2025)

A Finite-Time Analysis of TD Learning with Linear Function Approximation without Projections nor Strong Convexity. - Wei-Cheng Lee; Francesco Orabona (CoRR, 2025)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track. - Rylan Schaeffer; Joshua Kazdan; Yegor Denisov-Blanch; Brando Miranda; Matthias Gerstgrasser; Susan Zhang; Andreas Haupt; Isha Gupta; Elyas Obbad; Jesse Dodge; Jessica Zosa Forde; Koustuv Sinha; et al. (CoRR, 2025)

Dynamic Regret Reduces to Kernelized Static Regret. - Andrew Jacobsen; Alessandro Rudi; Francesco Orabona; Nicolò Cesa-Bianchi (CoRR, 2025)