Toward Building a Cost-Effective and Robust Machine Learning System
Explore cost-effective ML system development through active learning, continual learning, and transfer learning. Learn efficient model selection techniques for optimal source model transfer to target tasks.
Learning From Contrastive Examples in Machine Learning
Explores how contrastive training examples impact active learning algorithms, focusing on sample complexity for geometric concepts and Boolean functions, with connections to self-directed learning.
Humans and AI: Understanding LLM Impact on Human Decision Making
Explore the synergistic potential between humans and LLMs in decision-making tasks, examining active learning, ML fairness, and collaborative approaches to AI implementation.
Annotation-Efficient Object Detection: Unsupervised Discovery to Active Learning
Explore annotation-efficient object detection techniques, from unsupervised discovery to active learning strategies, enhancing intelligent systems with less manual input.
Adaptive Data Collection via Autoregressive Generation in News Recommendation Systems
Discover how autoregressive models can enhance adaptive data collection and decision-making through Thompson sampling, with applications in news recommendation systems and cold-start problems.