Eugene Berta
@eberta.bsky.social
PhD student at INRIA Paris. Working on calibration of machine learning classifiers.
Still using temperature scaling?
With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.
With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.
November 13, 2025 at 12:27 PM
Still using temperature scaling?
With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.
With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.
Early stopping on validation loss? This leads to suboptimal calibration and refinement errors—but you can do better!
With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.
With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.
February 3, 2025 at 1:03 PM
Early stopping on validation loss? This leads to suboptimal calibration and refinement errors—but you can do better!
With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.
With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.