Moving in an uncertain world: robust and adaptive control of locomotion from organisms to machine intelligence

JM Mongeau, Y Yang, I Escalante… - Integrative and …, 2024 - academic.oup.com
Whether walking, running, slithering, or flying, organisms display a remarkable ability to
move through complex and uncertain environments. In particular, animals have evolved to …

Prospective Learning: Learning for a Dynamic Future

A De Silva, R Ramesh, R Yang, S Yu… - arxiv preprint arxiv …, 2024 - arxiv.org
In real-world applications, the distribution of the data, and our goals, evolve over time. The
prevailing theoretical framework for studying machine learning, namely probably …

Accuracy and Fairness for Web-Based Content Analysis under Temporal Shifts and Delayed Labeling

AA Almuzaini, DM Pennock, VK Singh - … of the 16th ACM Web Science …, 2024 - dl.acm.org
Web-based content analysis tasks, such as labeling toxicity, misinformation, or spam often
rely on machine learning models to achieve cost and scale efficiencies. As these models …

Transformers for Supervised Online Continual Learning

J Bornschein, Y Li, A Rannen-Triki - arxiv preprint arxiv:2403.01554, 2024 - arxiv.org
Transformers have become the dominant architecture for sequence modeling tasks such as
natural language processing or audio processing, and they are now even considered for …

Fair and Accurate Machine Learning in Dynamic and Multi-Domain Settings

AAS Almuzaini - 2024 - search.proquest.com
A multitude of decision-making tasks, such as content moderation, medical diagnosis,
misinformation detection, and recidivism prediction, are increasingly being automated due to …