2 papers
cs.LG2024
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
Andreas Loukas, Karolis Martinkus, Ed Wagstaff +1
As training datasets grow larger, we aspire to develop models that generalize well to any diverse test distribution, even if the latter deviates significantly from the training dat…
cs.LG2024
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
NataÅ¡a Tagasovska, Vladimir GligorijeviÄ, Kyunghyun Cho +1
Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design u…