# Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference arXiv: https://arxiv.org/abs/2306.02497 ยท HTML: https://mlsanity.com/paper/2306.02497/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 79 references that are in the map. - First posted on arXiv 4 Jun 2023. ## Builds on (most cited first, 27 of 27) - [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021) - [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018) - [Coverage-centric Coreset Selection for High Pruning Rates](https://mlsanity.com/paper/2210.15809/) (2022) - [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019) - [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020) - [Learned Video Compression](https://mlsanity.com/paper/1811.06981/) (2018) - [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018) - [Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.12369/) (2023) - [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020) - [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021) - [Determinantal Point Processes in Randomized Numerical Linear Algebra](https://mlsanity.com/paper/2005.03185/) (2020) - [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022) - [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022) - [Distributed Submodular Maximization](https://mlsanity.com/paper/1411.0541/) (2014) - [Exact sampling of determinantal point processes with sublinear time preprocessing](https://mlsanity.com/paper/1905.13476/) (2019) - [Sampling from a k-DPP without looking at all items](https://mlsanity.com/paper/2006.16947/) (2020) - [Fast determinantal point processes via distortion-free intermediate sampling](https://mlsanity.com/paper/1811.03717/) (2018) - [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022) - [Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation](https://mlsanity.com/paper/2210.05946/) (2022) - [Submodular Mutual Information for Targeted Data Subset Selection](https://mlsanity.com/paper/2105.00043/) (2021) - [Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study](https://mlsanity.com/paper/2201.13229/) (2022) - [Ambient Backscatter Communications: A Contemporary Survey](https://mlsanity.com/paper/1712.04804/) (2017) - [xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective](https://mlsanity.com/paper/2302.11993/) (2023) - [RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples](https://mlsanity.com/paper/2304.04137/) (2023) - [The Ginibre Point Process as a Model for Wireless Networks with Repulsion](https://mlsanity.com/paper/1401.3677/) (2014) - [User-Centric Federated Learning: Trading off Wireless Resources for Personalization](https://mlsanity.com/paper/2304.12930/) (2023) - [Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer](https://mlsanity.com/paper/2006.01696/) (2020) ## Built on by (most cited first, 2 of 2) - [DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2407.03575/) (2024) - [Channel Normalization for Time Series Channel Identification](https://mlsanity.com/paper/2506.00432/) (2025) ## Abstract (arXiv) This paper proposes a distributed version of Determinant Point Processing (DPP) inference to enhance multi-source data diversification under limited communication bandwidth. DPP is a popular probabilistic approach that improves data diversity by enforcing the repulsion of elements in the selected subsets. The well-studied Maximum A Posteriori (MAP) inference in DPP aims to identify the subset with the highest diversity quantified by DPP. However, this approach is limited by the presumption that all data samples are available at one point, which hinders its applicability to real-world applications such as traffic datasets where data samples are distributed across sources and communication between them is band-limited. Inspired by the techniques used in Multiple-Input Multiple-Output (MIMO) communication systems, we propose a strategy for performing MAP inference among distributed sources. Specifically, we show that a lower bound of the diversity-maximized distributed sample selection problem can be treated as a power allocation problem in MIMO systems. A determinant-preserved sparse representation of selected samples is used to perform sample precoding in local sources to be processed by DPP. Our method does not require raw data exchange among sources, but rather a band-limited feedback channel to send lightweight diversity measures, analogous to the CSI message in MIMO systems, from the center to data sources. The experiments show that our scalable approach can outperform baseline methods, including random selection, uninformed individual DPP with no feedback, and DPP with SVD-based feedback, in both i.i.d and non-i.i.d setups. Specifically, it achieves 1 to 6 log-difference diversity gain in the latent representation of CIFAR-10, CIFAR-100, StanfordCars, and GTSRB datasets.