45 citations · 78 across the 6 of their papers we have counts for
7 papers
On Noisy Evaluation in Federated Hyperparameter Tuning
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak +4
Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Is…
Embeddings and Attention in Predictive Modeling
Kevin Kuo, Ronald Richman
We explore in depth how categorical data can be processed with embeddings in the context of claim severity modeling. We develop several models that range in complexity from simple…
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Kevin Kuo, Anthony Ostuni, Elizabeth Horishny +5
The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectru…
Towards Explainability of Machine Learning Models in Insurance Pricing
Kevin Kuo, Daniel Lupton
Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of tra…
Individual Claims Forecasting with Bayesian Mixture Density Networks
Kevin Kuo
We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging.…
Generative Synthesis of Insurance Datasets
Kevin Kuo
One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work,…
DeepTriangle: A Deep Learning Approach to Loss Reserving
Kevin Kuo
We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of het…