639 citations · 2.9k across the 94 of their papers we have counts for
11 papers · 1 filter
Robust Meta-Representation Learning via Global Label Inference and Classification
Ruohan Wang, Isak Falk, Massimiliano Pontil +1
Few-shot learning (FSL) is a central problem in meta-learning, where learners must efficiently learn from few labeled examples. Within FSL, feature pre-training has recently become…
Schedule-Robust Online Continual Learning
Ruohan Wang, Marco Ciccone, Giulia Luise +3
A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most…
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
Daniela A. Parletta, Andrea Paudice, Massimiliano Pontil +1
In this work we study high probability bounds for stochastic subgradient methods under heavy tailed noise. In this setting the noise is only assumed to have finite variance as oppo…
Group Meritocratic Fairness in Linear Contextual Bandits
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk +2
We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates' r…
Meta Representation Learning with Contextual Linear Bandits
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience. In this paper we investigate meta-learning in the settin…
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
Vladimir Kostic, Pietro Novelli, Andreas Maurer +3
We study a class of dynamical systems modelled as Markov chains that admit an invariant distribution via the corresponding transfer, or Koopman, operator. While data-driven algorit…