← Authors

Francesco Orabona

50 papers · 2009–2026 · 12 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (11) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (19) 🧭 Keyword Pioneer πŸƒ Academic Marathon (16) 🐣 Hot Topic Early Bird πŸ† Keyword Champion (4) πŸ† Grand Slam 🌱 Topic Pioneer πŸ”¬ Deep Specialist (16) πŸ”₯ Unstoppable (10) πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ’Ž Century Club (49) πŸ—ƒοΈ Keyword Collector (56)

Conferences

NIPS (14) ICML (12) COLT (7) ALT (5) AISTATS (3) JMLR (3) AAAI (1) ACML (1) AUTOML (1) CVPR (1) EACL (1) ICLR (1)

Top co-authors

Ashok Cutkosky (6) Kwang-Sung Jun (5) Ilja Kuzborskij (4) Barbara Caputo (4) Yulian Wu (3) Claudio Gentile (3) DΓ‘vid PΓ‘l (3) Zhenxun Zhuang (3) Mingrui Liu (3) Xiaoyu Li (3)

Keywords

online learning (17) regret bound (16) stochastic gradient descent (10) stochastic optimization (6) convex optimization (6) non-convex optimization (6) coin betting (5) parameter-free algorithm (4) kernel methods (4) online convex optimization (4) online linear optimization (3) parameter-free learning (3) expert advice (2) adaptive learning rate (2) multilabel classification (2) adversarial setting (2) adversarial learning (2) transfer learning (2) ridge regression (2) gradient descent (2)

Papers

Cards Against Contamination: TCG-Bench for Difficulty-Scalable Multilingual LLM Reasoning EACL 2026
New Lower Bounds for Non-Convex Stochastic Optimization through Divergence Decomposition COLT 2025
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ICML 2025
Square$Ο‡$PO: Differentially Private and Robust $Ο‡^2$-Preference Optimization in Offline Direct Alignment ICML 2025
Self-Directed Node Classification on Graphs ALT 2025
A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO ICML 2025
An Equivalence Between Static and Dynamic Regret Minimization NIPS 2024
Better-than-KL PAC-Bayes Bounds COLT 2024
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion ICLR 2024
Algorithmic Learning Theory 2023: Preface ALT 2023
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion ICML 2023
Generalized Implicit Follow-The-Regularized-Leader ICML 2023
Tighter PAC-Bayes Bounds Through Coin-Betting COLT 2023
On the Initialization for Convex-Concave Min-max Problems ALT 2022
Robustness to Unbounded Smoothness of Generalized SignSGD NIPS 2022
Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting AAAI 2022
Implicit Parameter-free Online Learning with Truncated Linear Models ALT 2022
On the Last Iterate Convergence of Momentum Methods ALT 2022
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance ICML 2021
Online Learning with Optimism and Delay ICML 2021
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers NIPS 2021
Temporal Variability in Implicit Online Learning NIPS 2020
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019
Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NIPS 2019
Parameter-Free Online Convex Optimization with Sub-Exponential Noise COLT 2019
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization ICML 2019
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces COLT 2018
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret ICML 2017
Training Deep Networks without Learning Rates Through Coin Betting NIPS 2017
Improved Strongly Adaptive Online Learning using Coin Betting AISTATS 2017
Open Problem: Parameter-Free and Scale-Free Online Algorithms COLT 2016
Solving Ridge Regression using Sketched Preconditioned SVRG ICML 2016
Parameter-Free Convex Learning through Coin Betting AUTOML 2016
Coin Betting and Parameter-Free Online Learning NIPS 2016
On Measure Concentration of Random Maximum A-Posteriori Perturbations ICML 2014
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations COLT 2014
On Multilabel Classification and Ranking with Bandit Feedback JMLR 2014
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning NIPS 2014
From N to N+1: Multiclass Transfer Incremental Learning CVPR 2013
Multiclass Latent Locally Linear Support Vector Machines ACML 2013
Stability and Hypothesis Transfer Learning ICML 2013
Dimension-Free Exponentiated Gradient NIPS 2013
Regression-tree Tuning in a Streaming Setting NIPS 2013
On Multilabel Classification and Ranking with Partial Feedback NIPS 2012
Multi Kernel Learning with Online-Batch Optimization JMLR 2012
Beyond Logarithmic Bounds in Online Learning AISTATS 2012
Learning from Candidate Labeling Sets NIPS 2010
New Adaptive Algorithms for Online Classification NIPS 2010
Bounded Kernel-Based Online Learning JMLR 2009