Jan 27, 2023 · Abstract:Loss functions serve as the foundation of supervised learning and are often chosen prior to model development.

We present. LEGENDRETRON as a novel and practical method that jointly learns proper canonical losses and probabilities for multiclass problems. Tested on a.

Jul 23, 2023 · Tested on a benchmark of domains with up to 1,000 classes, our experimental results show that our method consistently outperforms the natural ...

LegendreTron: Uprising Proper Multiclass Loss Learning. Kevin Lam, Christian Walder, Spiridon Penev, Richard Nock. June 2023. PDF. Type. Conference paper.

Sep 4, 2024 · Loss functions serve as the foundation of supervised learning and are often chosen prior to model development. To avoid potentially ad hoc ...

Topics · Gradient · Loss Function · Statistical Decision Theory · Convex Functions · Multiclass Problems · Supervised Learning · Benchmarks ...

2023, 'LegendreTron: Uprising Proper Multiclass Loss Learning', in Proceedings of the 40th International Conference on Machine Learning, Proceedings of ...

Nov 28, 2023 · We present LegendreTron as a novel and prac- tical way of learning proper canonical losses and probabilities concurrently in the multiclass ...

LEGENDRETRON: uprising proper multiclass loss learning · Author Picture Kevin H. Lam. School of Mathematics & Statistics, UNSW Sydney, Australia. ,; Author ...

LegendreTron: Uprising Proper Multiclass Loss Learning ... Existing methods do this by fitting an inverse canonical link function which monotonically maps R to [ ...