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cs.LG2025
When Embedding Models Meet: Procrustes Bounds and Applications
Lucas Maystre, Alvaro Ortega Gonzalez, Charles Park +4
Embedding models trained separately on similar data often produce representations that encode stable information but are not directly interchangeable. This lack of interoperability…
cs.LG2025
A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna +4
Performing gradient descent in a wide neural network is equivalent to computing the posterior mean of a Gaussian Process with the Neural Tangent Kernel (NTK-GP), for a specific pri…
cs.LG2025
Impatient Bandits: Optimizing for the Long-Term Without Delay
Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek +2
Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a bandit prob…