Label Propagation for Learning with Label Proportions

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag.Comment: Accepted to MLSP 201

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

International audienceDuring the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce the \betarisk as a generalized formulation of the standard empirical risk based on surrogate margin-based loss functions. This risk allows us to express the reliability on the labels and to derive different kinds of learning algorithms. We specifically focus on SVMs and propose a soft margin \betasvm algorithm which behaves better that the state of the art

Weakly supervised learning via statistical sufficiency

The Thesis introduces a novel algorithmic framework for weakly supervised learn- ing, namely, for any any problem in between supervised and unsupervised learning, from the labels standpoint. Weak supervision is the reality in many applications of machine learning where training is performed with partially missing, aggregated- level and/or noisy labels. The approach is grounded on the concept of statistical suf- ficiency and its transposition to loss functions. Our solution is problem-agnostic yet constructive as it boils down to a simple two-steps procedure. First, estimate a suffi- cient statistic for the labels from weak supervision. Second, plug the estimate into a (newly defined) linear-odd loss function and learn the model by any gradient-based solver, with a simple adaptation. We apply the same approach to several challeng- ing learning problems: (i) learning from label proportions, (ii) learning with noisy labels for both linear classifiers and deep neural networks, and (iii) learning from feature-wise distributed datasets where the entity matching function is unknown