methodology-2

Point Processes

3260 papers • 126 benchmarks • 313 datasets

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guilgautier/DPPy

6 papers 217

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MemeTracker

BillSum

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Most implemented papers

Explaining machine learning classifiers through diverse counterfactual explanations

Chenhao Tan, Ramaravind Kommiya Mothilal, Amit Sharma•Sat May 18 2019

This work proposes a framework for generating and evaluating a diverse set of counterfactual explanations based on determinantal point processes, and provides metrics that enable comparison ofcounterfactual-based methods to other local explanation methods.

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HongtengXu/Hawkes-Process-Toolkit

3 papers 106

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Determinantal Point Processes for Machine Learning

Alex Kulesza, B. Taskar•Tue Jul 24 2012

Determinantal Point Processes for Machine Learning provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows how they can be applied to real-world applications.

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Kronecker Determinantal Point Processes

S. Sra, Zelda E. Mariet•Wed May 25 2016

This work enables efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices, which enables fast exact sampling.

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Determinantal thinning of point processes with network learning applications

B. Błaszczyszyn, H. P. Keeler•Mon Oct 08 2018

A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques.

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Scalable Log Determinants for Gaussian Process Kernel Learning

David Eriksson, A. Wilson, Kun Dong, H. Nickisch, D. Bindel•Wed Nov 08 2017

It is found that Lanczos is generally superior to Chebyshev for kernel learning, and that a surrogate approach can be highly efficient and accurate with popular kernels.

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Intensity-Free Learning of Temporal Point Processes

Stephan Günnemann, Oleksandr Shchur, Marin Bilos•Wed Sep 25 2019

A simple mixture model is proposed that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form and is suitable for novel applications, such as learning sequence embeddings and imputing missing data.

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Transformer Hawkes Process

H. Zha, Haoming Jiang, T. Zhao, Simiao Zuo, Zichong Li•Thu Feb 20 2020

A Transformer Hawkes Process (THP) model is proposed, which leverages the self-attention mechanism to capture long-term dependencies and meanwhile enjoys computational efficiency.

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Modelling Behavioural Diversity for Learning in Open-Ended Games

Yaodong Yang, Jun Wang, Nicolas Perez Nieves, Oliver Slumbers, D. Mguni•Sat Mar 13 2021

This work offers a geometric interpretation of behavioural diversity in games and introduces a novel diversity metric based on determinantal point processes (DPP) that develops diverse fictitious play and diverse policy-space response oracle for solving normal-form games and open-ended games.

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Modeling the Intensity Function of Point Process Via Recurrent Neural Networks

Junchi Yan, Xiaokang Yang, H. Zha, Shuai Xiao, Stephen M. Chu•Fri Feb 03 2017

This paper model the background by a Recurrent Neural Network with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.

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