Publications associées à Decision stump

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

Prospects for B-c(+) -> tau(+)nu(tau) at FCC-ee

Clement Helsens, Donal Patrick Hill, Marie Hartmann

This paper presents the prospects for a precise measurement of the branching fraction of the leptonic B-c(+) -> tau(+)nu(tau) decay at the Future Circular Collider (FCC-ee) running at the Z-pole. A detailed description of the simulation and analysis framew ...

SPRINGER2021

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Ola Nils Anders Svensson, Adam Teodor Polak, Buddhima Ruwanmini Gamlath Gamlath Ralalage, Xinrui Jia

We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to be explainable if it is given by a decision tree where each internal node splits data point ...

2021

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

SHiP is a proposed high-intensity beam dump experiment set to operate at the CERN SPS. It is expected to have an unprecedented sensitivity to a variety of models containing feebly interacting particles, such as Heavy Neutral Leptons (HNLs). Two HNLs or mor ...

2020

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

Maksym Andriushchenko

The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, ...

2019

Search for standard model production of four top quarks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Jian Wang, Matthias Finger, Lesya Shchutska, Qian Wang, Matthias Wolf, Varun Sharma, Konstantin Androsov, Jan Steggemann, Leonardo Cristella, Roberto Castello, Alessandro Degano, Mingkui Wang, Zhirui Xu, Chao Wang, João Miguel das Neves Duarte, Tian Cheng, Yixing Chen, Werner Lustermann, Andromachi Tsirou, Alexis Kalogeropoulos, Andrea Rizzi, Ioannis Papadopoulos, Paolo Ronchese, Thomas Muller, Ho Ling Li, Giuseppe Codispoti, Hua Zhang, Siyuan Wang, Peter Hansen, Daniel Gonzalez, Tao Huang, David Vannerom, Ji Hyun Kim, Donghyun Kim, Dipanwita Dutta, Zheng Wang, Sanjeev Kumar, Wei Li, Yong Yang, Yi Wang, Ajay Kumar, Ashish Sharma, Georgios Anagnostou, Joao Varela, Csaba Hajdu, Muhammad Ahmad, Ekaterina Kuznetsova, Ioannis Evangelou, Matthias Weber, Muhammad Shoaib, Milos Dordevic, Vineet Kumar, Vladimir Petrov, Francesco Fiori, Quentin Python, Meng Xiao, Hao Liu, Sourav Sen, Viktor Khristenko, Marco Trovato, Gurpreet Singh, Fan Xia, Kai Yi, Bibhuprasad Mahakud, Lei Feng, Muhammad Waqas, Shuai Liu, Hui Wang, Seungkyu Ha, Davide Cieri, Maren Tabea Meinhard, Giorgia Rauco, Aram Avetisyan, Ali Harb, Benjamin William Allen, Alexandre Aubin, Xin Sun

A search for events containing four top quarks ( tt¯tt¯ ) is reported from proton–proton collisions recorded by the CMS experiment at s=13 TeV and corresponding to an integrated luminosity of 2.6 fb −1 . The analysis considers the single-lepton (e or μ )+j ...

2017

Search for s channel single top quark production in pp collisions at $ \sqrt{s}=7 $ and 8 TeV

Jian Wang, Matthias Finger, Lesya Shchutska, Qian Wang, Matthias Wolf, Varun Sharma, Konstantin Androsov, Jan Steggemann, Leonardo Cristella, Roberto Castello, Alessandro Degano, Mingkui Wang, Zhirui Xu, Chao Wang, João Miguel das Neves Duarte, Tian Cheng, Yixing Chen, Werner Lustermann, Andromachi Tsirou, Alexis Kalogeropoulos, Andrea Rizzi, Ioannis Papadopoulos, Paolo Ronchese, Thomas Muller, Ho Ling Li, Giuseppe Codispoti, Hua Zhang, Siyuan Wang, Peter Hansen, Daniel Gonzalez, Tao Huang, Paul Turner, Wei Sun, Raffaele Tito D'Agnolo, Ji Hyun Kim, Donghyun Kim, Dipanwita Dutta, Zheng Wang, Sanjeev Kumar, Wei Li, Yong Yang, Yi Wang, Ajay Kumar, Ashish Sharma, Georgios Anagnostou, Joao Varela, Csaba Hajdu, Muhammad Ahmad, Ekaterina Kuznetsova, Ioannis Evangelou, Matthias Weber, Muhammad Shoaib, Milos Dordevic, Vineet Kumar, Vladimir Petrov, Francesco Fiori, Quentin Python, Meng Xiao, Hao Liu, Sourav Sen, Viktor Khristenko, Marco Trovato, Gurpreet Singh, Fan Xia, Kai Yi, Bibhuprasad Mahakud, Rajat Gupta, Lei Feng, Muhammad Waqas, Shuai Liu, Davide Cieri, Maren Tabea Meinhard, Giorgia Rauco, Aram Avetisyan, Charles Dietz, Alexandre Aubin, Michal Simon, Xin Sun, Matteo Marone, Amr Mohamed, Juan Ramon Castiñeiras De Saa

A search is presented for single top quark production in the s channel in proton-proton collisions with the CMS detector at the CERN LHC in decay modes of the top quark containing a muon or an electron in the final state. The signal is extracted through a ...

2016

Automated Classification of Brain Tumor Type in Whole-Slide Digital Pathology Images Using Local Representative Tiles

Adrien Raphaël Depeursinge

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digita ...

Elsevier2016

Prediction of Asynchronous Dimensional Emotion Ratings from Audiovisual and Physiological Data

Jean-Philippe Thiran, Touradj Ebrahimi, Eleni Kroupi, Anil Yuce

Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models. Whereas the dimensional approach is getting more and more popular for rating: affective behaviours in con ...

Elsevier2015

Adaptive Sampling for Large Scale Boosting

François Fleuret, Charles Dubout

Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such con ...

2014

Decision stump

Improved calorimetric particle identification in NA62 using machine learning techniques

Prospects for B-c(+) -> tau(+)nu(tau) at FCC-ee

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

Search for standard model production of four top quarks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Search for s channel single top quark production in pp collisions at $ \sqrt{s}=7 $ and 8 TeV

Automated Classification of Brain Tumor Type in Whole-Slide Digital Pathology Images Using Local Representative Tiles

Prediction of Asynchronous Dimensional Emotion Ratings from Audiovisual and Physiological Data

Adaptive Sampling for Large Scale Boosting

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Prospects for B-c(+) -> tau(+)nu(tau) at FCC-ee

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

Prediction of Asynchronous Dimensional Emotion Ratings from Audiovisual and Physiological Data

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

Automated Classification of Brain Tumor Type in Whole-Slide Digital Pathology Images Using Local Representative Tiles

Adaptive Sampling for Large Scale Boosting

Search for standard model production of four top quarks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Search for s channel single top quark production in pp collisions at $ \sqrt{s}=7 $ and 8 TeV