5 papers
The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Stephen Mussmann
Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common heuristic and also equivalent…
Instance-Level Costs for Nuanced Classifier Evaluation
Kabir Kang, Stephen Mussmann
Standard classification treats all errors equally, but in applications such as content moderation and medical screening, mistakes on clear-cut cases are more costly than errors on…
Batch Bayesian Active Learning with Partial Batch Label Sampling
Kangping Hu, Stephen Mussmann
Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian-based active…
Sum Estimation via Vector Similarity Search
Stephen Mussmann, Mehul Smriti Raje, Kavya Tumkur +3
Semantic embeddings to represent objects such as image, text and audio are widely used in machine learning and have spurred the development of vector similarity search methods for…
Continuous nonlinear adaptive experimental design with gradient flow
Ruhui Jin, Qin Li, Stephen O. Mussmann +1
In computational inverse problems, the optimal experimental design (OED) problem seeks the best locations in time and space at which to take measurements. We investigate the nonlin…