# Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes

arXiv: https://arxiv.org/abs/2111.14674 · HTML: https://mlsanity.com/paper/2111.14674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Lazier Than Lazy Greedy](https://mlsanity.com/paper/1409.7938/) (2014)
- [Determinantal Point Processes in Randomized Numerical Linear Algebra](https://mlsanity.com/paper/2005.03185/) (2020)
- [Modelling Behavioural Diversity for Learning in Open-Ended Games](https://mlsanity.com/paper/2103.07927/) (2021)
- [Guarantees for Greedy Maximization of Non-submodular Functions with Applications](https://mlsanity.com/paper/1703.02100/) (2017)
- [Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search](https://mlsanity.com/paper/2006.07593/) (2020)
- [Learning Nonsymmetric Determinantal Point Processes](https://mlsanity.com/paper/1905.12962/) (2019)
- [Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes](https://mlsanity.com/paper/2006.09862/) (2020)
- [ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data](https://mlsanity.com/paper/2009.12316/) (2020)
- [From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization](https://mlsanity.com/paper/2102.05347/) (2021)
- [Learning from Sets of Items in Recommender Systems](https://mlsanity.com/paper/1904.12643/) (2019)
- [PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs](https://mlsanity.com/paper/2002.11304/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time](https://mlsanity.com/paper/2309.07418/) (2023)
- [Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability](https://mlsanity.com/paper/2210.08371/) (2022)

## 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.