Learning with Limited Labels via Momentum Damped & Differentially Weighted Optimization


As deep learning-based models are deployed more widely in search & recommender systems, system designers often face the issue of gathering large amounts of well-annotated data to train such neural models. While most user-centric systems rely on interaction signals as implicit feedback to train models, such signals are often weak proxies of user satisfaction, as compared to (say) explicit judgments from users, which are prohibitively expensive to collect. In this paper, we consider the task of learning from limited labeled data, wherein we aim at jointly leveraging strong supervision data (e.g. explicit judgments) along with weak supervision data (e.g. implicit feedback or labels from the related task) to train neural models. We present data mixing strategies based on submodular subset selection, and additionally, propose adaptive optimization techniques to enable the model to differentiate between a strong label data point and a weak supervision data point. Finally, we present two different case-studies (i) user satisfaction prediction with music recommendation and (ii) question-based video comprehension and demonstrate that the proposed adaptive learning strategies are better at learning from limited labels. Our techniques and findings provide practitioners with ways of leveraging external labeled data.


May 2021 | CHI

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, Henriette Cramer

April 2021 | The Web Conference

Where To Next? A Dynamic Model of User Preferences

Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas

April 2021 | AISTATS

Collaborative Classification from Noisy Labels

Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas