Publications related to Decision tree learning

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Learning to Remove Cuts in Integer Linear Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

2024

Improved calorimetric particle identification in NA62 using machine learning techniques

Alessandro Mapelli, Radoslav Marchevski, Alina Kleimenova

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decis ...

New York2023

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu, Jie Wang

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Kamiar Aminian, Xavier Crevoisier, Robin Martin

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the ...

MDPI2023

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Olga Fink, Ismail Nejjar, Han Sun, Hao Dong

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due to the ...

2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Los Alamitos2023

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Decision tree learning

Boosting likelihood learning with event reweighting

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Learning to Remove Cuts in Integer Linear Programming

Improved calorimetric particle identification in NA62 using machine learning techniques

Contextual Stochastic Bilevel Optimization

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Robust Outlier Rejection for 3D Registration with Variational Bayes

Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks

Graph Chatbot

Chat with Graph Search

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Boosting likelihood learning with event reweighting

Learning to Remove Cuts in Integer Linear Programming

Contextual Stochastic Bilevel Optimization

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Improved calorimetric particle identification in NA62 using machine learning techniques

Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks

Robust Outlier Rejection for 3D Registration with Variational Bayes