[ML Seminar] IDA ML Seminar - 04/12 13:30 - Paolo Giordani - HTBoost: data efficient learning via hybrid tree boosting

[Louis Ohl]

Dear colleagues,

A kind reminder: the seminar is today at 13.30 in Alan Turing.

Best regards,
--
Louis
________________________________
Från: Louis Ohl <louis.ohl at liu.se>
Skickat: den 20 november 2025 13:00
Till: ml-seminars at lists.liu.se <ml-seminars at lists.liu.se>; idaint at ida.liu.se <idaint at ida.liu.se>; ai-academy at groups.liu.se <ai-academy at groups.liu.se>
Ämne: IDA ML Seminar - 04/12 13:30 - Paolo Giordani - HTBoost: data efficient learning via hybrid tree boosting

Welcome to an IDA Machine Learning Seminar on Thursday, December 4 at 13:30 In Alan Turing


Title: HTBoost: data efficient learning via hybrid tree boosting

Speaker: Paolo Giordani<https://www.bi.no/en/about-bi/employees/department-of-finance/paolo-giordani/>, Professor at BI Norwegian Business School


Abstract: We develop HTBoost (Hybrid Tree Boosting), a model designed to retain the strengths of gradient boosting machines while addressing some of their limitations. Hybrid trees may contain any mix of standard hard splits and more flexible soft splits, adopting a two-step procedure to escape the local minima in which soft splits are sometimes trapped. Additionally, the fitted values of each tree are enhanced by a nonlinear transformation. These modifications increase accuracy when approximating functions that are smooth with respect to at least some features. The efficiency gains come at the cost of much longer computing times, particularly for large samples. We consider some strategies to reduce the computing cost. The performance of HTBoost, XGBoost, and LightGBM is compared in simulations and in a few well-known datasets, where the realized accuracy gains are in line with what predicted by summary measures of smoothness. Finally, we extend the automatic treatment of missing values to smooth and hybrid trees.


A preliminary paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143093


Location: Alan Turing, https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan

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--
Louis Ohl
Postdoc
louis.ohl at liu.se

Linköping University
IDA / STIMA
B-Huset, 2A:445
Webpage<https://oshillou.github.io/>
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