We first propose the concept of generalized bags, an extension of bags and then devise an algorithm to combine bag distributions, if possible, into good ...
In this paper we address the hard problem of solving. LLP with provable error bounds while being bag distribution agnostic and model agnostic. We first propose ...
Search · On Combining Bags to Better Learn from Label Proportions.
Jan 27, 2024 · I thought you train a bags with different proportions. But you predict a single class. Can I use sklearn? But you would always end up with ...
Missing: Combining | Show results with:Combining
Aug 17, 2023 · In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary ...
Missing: Combining | Show results with:Combining
We present a novel way of training models in theweakly supervised setup of learning frombagsof examples with just aggregate label informa-tion.
Nov 22, 2023 · Summary: This paper provided an algorithm to perform efficient learning from bag-level label proportions. The author utilized Belief Propagation ...
Mar 20, 2024 · On combining bags to better learn from label proportions. In International Conference on Artificial Intelligence and Statistics, pp. 5913 ...
MixBag increases the number of labeled bags artificially by sampling instances from a pair of origi- nal bags and mixing them: this operation mimics the above.
Learning from label proportions (LLP) is a weak supervision setting for classification in which training data come in the form of bags. Each bag contains ...