572 citations
- University of CambridgeGB8 papers
- University of TorontoCA8 papers
- Cornell UniversityUS6 papers
- University of Central FloridaUS6 papers
- University of WyomingUS6 papers
- Meta (Israel)IL4 papers
- OpenAI (United States)US4 papers
- Carnegie Mellon UniversityUS3 papers
- Georgia Institute of TechnologyUS3 papers
- Google (United States)US3 papers
- Linköping UniversitySE3 papers
- Stanford UniversityUS3 papers
6 papers · 2 filters
Multi-Attribute Bayesian Optimization With Interactive Preference Learning
Raul Astudillo, Peter I. Frazier
We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is asso…
Parametric Gaussian Process Regressors
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the result…
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Zhenyu Zhao, Radhika Anand, Mallory Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature se…
Uplift Modeling for Multiple Treatments with Cost Optimization
Zhenyu Zhao, Totte Harinen
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of in…
Variational Bayesian Optimal Experimental Design
Adam Foster, Martin Jankowiak, Eli Bingham +4
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by th…
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak +4
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do n…