← Authors

Alessandro Rudi

45 papers · 2013–2023 · 5 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (13) 🗺️ Taxonomy Completionist (59) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🐣 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)

Top co-authors

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

Keywords

kernel methods (12) structured prediction (10) kernel ridge regression (5) stochastic gradient descent (4) learning rate (4) density estimation (4) nyström method (4) optimal transport (3) generalization bound (3) regret 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

Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models NIPS 2023
GloptiNets: Scalable Non-Convex Optimization with Certificates NIPS 2023
Nyström Kernel Mean Embeddings ICML 2022
Measuring dissimilarity with diffeomorphism invariance ICML 2022
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares COLT 2022
Active Labeling: Streaming Stochastic Gradients NIPS 2022
On the Consistency of Max-Margin Losses AISTATS 2022
Sampling from Arbitrary Functions via PSD Models AISTATS 2022
On the Benefits of Large Learning Rates for Kernel Methods COLT 2022
Vector-Valued Least-Squares Regression under Output Regularity Assumptions JMLR 2022
Fast Rates for Structured Prediction COLT 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
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation COLT 2021
Disambiguation of Weak Supervision leading to Exponential Convergence rates ICML 2021
Consistent Structured Prediction with Max-Min Margin Markov Networks ICML 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
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings JMLR 2020
Structured Prediction with Partial Labelling through the Infimum Loss ICML 2020
Non-parametric Models for Non-negative Functions NIPS 2020
Kernel Methods Through the Roof: Handling Billions of Points Efficiently NIPS 2020
Sharp Analysis of Learning with Discrete Losses AISTATS 2019
Efficient online learning with kernels for adversarial large scale problems NIPS 2019
Localized Structured Prediction NIPS 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions 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
Learning with SGD and Random Features NIPS 2018
On Fast Leverage Score Sampling and Optimal Learning NIPS 2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NIPS 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NIPS 2018
Exponential Convergence of Testing Error for Stochastic Gradient Methods COLT 2018
Manifold Structured Prediction 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