Alessandro Rudi

46 papers · 2013–2026 · 6 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🏃 Academic Marathon (10) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🐣 Hot Topic Early Bird

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) 🏃 Academic Marathon (10) 🏠 Conference Loyalist (24) 🤝 Dynamic Duo (16) 🌱 Topic Pioneer 🔬 Deep Specialist (14) 🏆 Keyword Champion (5) 🗃️ Keyword Collector (176) 📈 Trend Setter 🔥 Unstoppable (9) ⚡ Prolific Year (6) 💎 Century Club (45)

Conferences

NIPS (24) COLT (8) AISTATS (6) ICML (5) JMLR (2) ALT (1)

Top co-authors

Francis Bach (16) Lorenzo Rosasco (14) Carlo Ciliberto (8) Ulysse Marteau-Ferey (4) Luigi Carratino (4) Loucas Pillaud-Vivien (4) Pierre Gaillard (4) Rémi Jézéquel (3) Francis R. Bach (3) Gaspard Beugnot (3)

Keywords

kernel methods (12) structured prediction (10) kernel ridge regression (5) stochastic gradient descent (4) regret bound (4) density estimation (4) nyström method (4) learning rate (4) optimal transport (3) generalization bound (3) nonparametric learning (3) wasserstein distance (3) binary classification (2) probabilistic modeling (2) non-convex optimization (2) online learning (2) large-scale learning (2) gradient descent (2) ridge regression (2) statistical consistency (2)

Papers

Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm ALT 2026 Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models NIPS 2023 GloptiNets: Scalable Non-Convex Optimization with Certificates NIPS 2023 Measuring dissimilarity with diffeomorphism invariance ICML 2022 Active Labeling: Streaming Stochastic Gradients NIPS 2022 On the Benefits of Large Learning Rates for Kernel Methods COLT 2022 On the Consistency of Max-Margin Losses AISTATS 2022 Sampling from Arbitrary Functions via PSD Models AISTATS 2022 Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares COLT 2022 Vector-Valued Least-Squares Regression under Output Regularity Assumptions JMLR 2022 Nyström Kernel Mean Embeddings ICML 2022 Disambiguation of Weak Supervision leading to Exponential Convergence rates ICML 2021 PSD Representations for Effective Probability Models NIPS 2021 Mixability made efficient: Fast online multiclass logistic regression NIPS 2021 Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization NIPS 2021 Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning NIPS 2021 Fast Rates for Structured Prediction COLT 2021 A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation COLT 2021 Consistent Structured Prediction with Max-Min Margin Markov Networks ICML 2020 Kernel Methods Through the Roof: Handling Billions of Points Efficiently NIPS 2020 Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions AISTATS 2020 Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020 Efficient improper learning for online logistic regression COLT 2020 Non-parametric Models for Non-negative Functions NIPS 2020 A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings JMLR 2020 Structured Prediction with Partial Labelling through the Infimum Loss ICML 2020 Sharp Analysis of Learning with Discrete Losses AISTATS 2019 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses NIPS 2019 Massively scalable Sinkhorn distances via the Nyström method NIPS 2019 Localized Structured Prediction NIPS 2019 Affine Invariant Covariance Estimation for Heavy-Tailed Distributions COLT 2019 Efficient online learning with kernels for adversarial large scale problems NIPS 2019 Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NIPS 2018 On Fast Leverage Score Sampling and Optimal Learning NIPS 2018 Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NIPS 2018 Exponential Convergence of Testing Error for Stochastic Gradient Methods COLT 2018 Manifold Structured Prediction NIPS 2018 Learning with SGD and Random Features NIPS 2018 Generalization Properties of Learning with Random Features NIPS 2017 FALKON: An Optimal Large Scale Kernel Method NIPS 2017 Consistent Multitask Learning with Nonlinear Output Relations NIPS 2017 A Consistent Regularization Approach for Structured Prediction NIPS 2016 NYTRO: When Subsampling Meets Early Stopping AISTATS 2016 Less is More: Nyström Computational Regularization NIPS 2015 On the Sample Complexity of Subspace Learning NIPS 2013