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cs.LG2024
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco +3
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belon…
cs.LG2024
Goal-Conditioned Reinforcement Learning from Sub-Optimal Data on Metric Spaces
Alfredo Reichlin, Miguel Vasco, Hang Yin +1
We study the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning under sparse rewards, invertible actions and determi…
cs.LG2023
Learning Geometric Representations of Objects via Interaction
Alfredo Reichlin, Giovanni Luca Marchetti, Hang Yin +2
We address the problem of learning representations from observations of a scene involving an agent and an external object the agent interacts with. To this end, we propose a repres…