Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
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Abstract (arXiv)
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 29 Nov 2021.
Builds on
11 of 27 references.
References · 11
- 2014Lazier Than Lazy Greedycited by 97
- 2020Determinantal Point Processes in Randomized Numerical Linear Algebracited by 41
- 2021Modelling Behavioural Diversity for Learning in Open-Ended Gamescited by 39
- 2017Guarantees for Greedy Maximization of Non-submodular Functions with Applicationscited by 34
- 2020Optimal Transport Kernels for Sequential and Parallel Neural Architecture Searchcited by 29
- 2019Learning Nonsymmetric Determinantal Point Processescited by 15
- 2020Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processescited by 10
- 2020ML-based Visualization Recommendation: Learning to Recommend Visualizations from Datacited by 5
- 2019Learning from Sets of Items in Recommender Systemscited by 4
- 2020PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designscited by 3
- 2021From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximizationcited by 3
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.