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Carlo Ciliberto

27 papers · 2014–2024 · 6 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) πŸ—ΊοΈ Taxonomy Completionist (44) πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (6) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (6) 🀝 Dynamic Duo (14) πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (121) ⚑ Prolific Year (6) πŸ”₯ Unstoppable (9) πŸ’Ž Century Club (27)

Conferences

NIPS (16) ICML (6) CVPR (2) AISTATS (1) ICCV (1) JMLR (1)

Top co-authors

Massimiliano Pontil (14) Alessandro Rudi (8) Lorenzo Rosasco (8) Giulia Denevi (5) Giulia Luise (4) Ruohan Wang (3) Yiannis Demiris (2) Pietro Novelli (2) Sean Ryan Fanello (2) Dimitris Stamos (2)

Keywords

structured prediction (7) transfer learning (4) reproducing kernel hilbert space (3) optimal transport (3) multi-task learning (3) few-shot learning (2) kernel methods (2) biased regularization (2) task distribution (2) learning to learn (2) manifold learning (2) representation learning (2) sinkhorn divergence (2) convex optimization (2) online learning (2) maximum mean discrepancy (2) multitask learning (2) manifold regression (2) task adaptation (2) conditional meta-learning (2)

Papers

Operator World Models for Reinforcement Learning NIPS 2024
Distribution Regression with Sliced Wasserstein Kernels ICML 2022
Measuring dissimilarity with diffeomorphism invariance ICML 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces NIPS 2022
Conditional Meta-Learning of Linear Representations NIPS 2022
PSD Representations for Effective Probability Models NIPS 2021
The Role of Global Labels in Few-Shot Classification and How to Infer Them NIPS 2021
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings JMLR 2020
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning NIPS 2020
Structured Prediction for Conditional Meta-Learning NIPS 2020
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning NIPS 2020
Hyperbolic Manifold Regression AISTATS 2020
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm NIPS 2019
Localized Structured Prediction NIPS 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction ICML 2019
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation ICML 2019
Online-Within-Online Meta-Learning NIPS 2019
Learning To Learn Around A Common Mean NIPS 2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NIPS 2018
Manifold Structured Prediction NIPS 2018
Low Compute and Fully Parallel Computer Vision With HashMatch ICCV 2017
Consistent Multitask Learning with Nonlinear Output Relations NIPS 2017
A Consistent Regularization Approach for Structured Prediction NIPS 2016
Learning Multiple Visual Tasks While Discovering Their Structure CVPR 2015
Convex Learning of Multiple Tasks and their Structure ICML 2015
Ask the Image: Supervised Pooling to Preserve Feature Locality CVPR 2014