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Massimiliano Pontil

  1. Empirical Bernstein Bounds and Sample Variance Penalization
    2009/07/21 by Andreas Maurer, Massimiliano Pontil, Maurer, Andreas +1 · 42 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  2. The Benefit of Multitask Representation Learning
    2015/05/23 by Andreas Maurer, Massimiliano Pontil, Maurer, Andreas +3 · 18 citations
    Computer Science · Engineering · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques
  3. Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
    2024/11/19 by Riccardo Grazzi, Julien Siems, Grazzi, Riccardo +9 · 3 voices · 16 citations
    Engineering · Computer Science · #cs.LG #cs.CL #cs.FL
  4. On the Iteration Complexity of Hypergradient Computation
    2020/06/29 by Riccardo Grazzi, Luca Franceschi, Grazzi, Riccardo +5 · 15 citations
    Mathematics · Medicine · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Imaging Techniques and Applications #Model Reduction and Neural Networks #Tensor decomposition and applications
  5. On Learning Vector-Valued Functions
    2004/11/25 by Charles A. Micchelli, Massimiliano Pontil · 10 citations
    Computer Science · Engineering · #Control Systems and Identification #Gaussian Processes and Bayesian Inference #Neural Networks and Applications
  6. Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
    2022/05/27 by Vladimir R. Kostic, Kostic, Vladimir, Pietro Novelli +9 · 14 citations
    Computer Science · Engineering · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Model Reduction and Neural Networks
  7. Excess risk bounds for multitask learning with trace norm regularization
    2012/12/06 by Andreas Maurer, Massimiliano Pontil, Maurer, Andreas +1 · 4 citations
    Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Inequalities and Applications #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques
  8. Sharp Spectral Rates for Koopman Operator Learning
    2023/02/03 by Vladimir R. Kostic, Karim Lounici, Kostic, Vladimir +5 · 8 citations
    Physics and Astronomy · Medicine · Computer Science · #Model Reduction and Neural Networks #Thermal Regulation in Medicine #Gaussian Processes and Bayesian Inference
  9. Distance-Based Regularisation of Deep Networks for Fine-Tuning
    2020/02/19 by Henry Gouk, Gouk, Henry, Timothy M. Hospedales +3 · 3 citations
    Computer Science · Engineering · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #Sparse and Compressive Sensing Techniques
  10. Meta-learning with Stochastic Linear Bandits
    2020/05/18 by Leonardo Cella, Alessandro Lazaric, Cella, Leonardo +3 · 3 citations
    Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #Machine Learning and Data Classification
  11. A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
    2022/05/27 by Arya Akhavan, Akhavan, Arya, Evgenii Chzhen +5 · 4 citations
    Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
  12. A New Convex Relaxation for Tensor Completion
    2013/07/17 by Bernardino Romera‐Paredes, Massimiliano Pontil, Romera-Paredes, Bernardino +1 · 2 citations
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
  13. Neural Conditional Probability for Uncertainty Quantification
    2024/07/01 by Vladimir Kostić, Karim Lounici, Kostic, Vladimir R. +9 · 6 citations
    Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications #Statistics Theory (math.ST)
  14. Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
    2023/06/02 by Isak Falk, Falk, John, Bonati, Luigi +5 · 3 citations
    Computer Science · Materials Science · #Advanced Graph Neural Networks #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Topic Modeling
  15. Estimating Koopman operators with sketching to provably learn large scale dynamical systems
    2023/06/07 by Giacomo Meanti, Meanti, Giacomo, Antoine Chatalic +9 · 3 citations
    Computer Science · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Model Reduction and Neural Networks #Neural Networks and Applications
  16. Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm
    2023/06/03 by Arya Akhavan, Akhavan, Arya, Evgenii Chzhen +5 · 2 citations
    Computer Science · Decision Sciences · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Risk and Portfolio Optimization #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
  17. From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
    2024/06/13 by Timothée Devergne, Devergne, Timothée, Vladimir Kostić +5 · 5 citations
    Mathematics · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Mathematical and Theoretical Analysis
  18. The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
    2020/08/25 by Giulia Denevi, Denevi, Giulia, Massimiliano Pontil +3 · 1 citation
    Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques
  19. Conditional Meta-Learning of Linear Representations
    2021/03/30 by Giulia Denevi, Denevi, Giulia, Massimiliano Pontil +3 · 2 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimodal Machine Learning Applications
  20. Convergence Properties of Stochastic Hypergradients
    2020/11/13 by Riccardo Grazzi, Grazzi, Riccardo, Massimiliano Pontil +3 · 1 citation
    Mathematics · Computer Science · Engineering · #Advanced Topology and Set Theory #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques
  21. Slow dynamical modes from static averages
    2025/03/25 by Timothée Devergne, Vladimir Kostić, Massimiliano Pontil +1 · 2 voices · 2 citations
    Neuroscience · Physics and Astronomy · #Neural dynamics and brain function #Spectroscopy and Quantum Chemical Studies #stochastic dynamics and bifurcation
  22. Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
    2024/10/18 by Vladimir Kostić, Karim Lounici, Kostic, Vladimir R. +9 · 2 citations
    Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Statistics Theory (math.ST)
  23. Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
    2024/03/18 by Riccardo Grazzi, Grazzi, Riccardo, Massimiliano Pontil +3 · 1 citation
    Computer Science · #Optimization and Variational Analysis
  24. Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling
    2023/12/12 by Daniel Ordoñez-Apraez, Ordoñez-Apraez, Daniel, Vladimir Kostić +11 · 1 citation
    Physics and Astronomy · #Model Reduction and Neural Networks
  25. Fast and Fourier Features for Transfer Learning of Interatomic Potentials
    2025/05/08 by Pietro Novelli, Giacomo Meanti, Novelli, Pietro +10 · 3 citations
    Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Quantum many-body systems
  26. Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth
    2024/10/10 by Leonardo Sabattini, Sabattini, Leonardo, Annalisa Coriolano +17 · 1 citation
    Materials Science · #Catalytic Processes in Materials Science #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Mesoscale and Nanoscale Physics (cond-mat.mes-hall)