41 citations · 100 across the 13 of their papers we have counts for
19 papers
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…
Exploration via Planning for Information about the Optimal Trajectory
Viraj Mehta, Ian Char, Joseph Abbate +5
Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying…
Generalizing Bayesian Optimization with Decision-theoretic Entropies
Willie Neiswanger, Lantao Yu, Shengjia Zhao +2
Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries. Existing information-theoretic BO proc…
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
Avanika Narayan, Piero Molino, Karan Goel +2
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmar…
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Youngseog Chung, Ian Char, Han Guo +2
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common…
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
Yang Liu, Sujay Khandagale, Colin White +1
As machine learning models grow more complex and their applications become more high-stakes, tools for explaining model predictions have become increasingly important. This has spu…