Resource Efficient Machine Learning (Budgeted ML)
Machine learning applications often requires achieving high prediction accuracy under certain budget constraint. In automated medical diagnosis each medical test carries a monetary cost and the aim is to correctly diagnose any given patient based on a sequence of tests while keeping an expected budget. In search engines the speed of producing search results is often just as important as the quality of the results. Given a budget in terms of CPU time, the goal is to have a ranking algorithm that produces accurate ranking results within the budget.
Learning under resource constraints departs from the traditional machine learning setting and introduces new exciting challenges. For instance, features are accompanied by costs (e.g. extraction time in search engines or true monetary values in medical diagnosis) and their amortized sum is constrained at test-time.
This introduces a new trade-off between prediction accuracy and prediction cost. Studying this tradeoff is an inherent challenge that needs to be investigated in a principled fashion in order to invent practically relevant machine learning algorithms.
Selective Classification Based on One-Sided Prediction, AISTATS 2021
Budget Learning by Bracketing, AISTATS 2020
Structured Prediction on a Budget, AAAI 2017
Cheap Bandits, ICML 2015
Feature-Budgeted Random Forest, ICML 2015
Model Selection by Linear Programming, ECCV 2014
An LP for Sequential Learning Under Budgets, AISTATS 2014
Fast Margin-based Cost-Sensitive Classification, ICASSP 2014
Multistage Learning under Budget Constraints, AISTATS 2013 (Oral)
Multi Stage Classifier Design, ACML 2012