Determinantal Point Process

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Adaptive Determinantal Client Scheduling in Federated Learning
Sep 20, 2026Wen Xu, Ben Liang, Gary Boudreau +1Federated Learning Schedulers
University of Toronto, Canada · Ericsson, Canada
FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Sep 14, 2026Tyler Farnan, Benjamin Eng, Adam Abate +4Benchmark Datasets Determinantal Point Process
Capital One
Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals, and Why Decay Is Nearly Enough
Aug 12, 2026Sang Su Lee, Vineeth Loganathan, Shishir Dash +1Time-To-Event Data Determinantal Point Process
Thumbtack, Inc. San Francisco, CA, USA
Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty
Aug 9, 2026Jennifer N. Kampe, Changwoo J. Lee, Xin Shen +5Sound Source Localization Indoor Localization
Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland · Department of Statistical Science, Duke University, Durham, NC, USA · 3Digital Services, University of Jyväskylä, Jyväskylä, Finland +1
PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction
Jul 31, 2026Huanyu Liu, Baining Chen, Hui Liu +2Multimodal Recommendation Model Multimodal Embeddings
Huazhong University of Science and Technology Wuhan, Hubei, China · Central China Normal University Wuhan, Hubei, China
Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality
Jul 29, 2026Xiaoyin Pan, Christian R. Shelton, Rakshith Mahishi +1Determinantal Point Process Spatiotemporal Fields
University of California, Riverside · New York University · Zhongguancun Academy
Distributional Determinantal Point Process for Repulsive Clustering of Distributions
Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Determinantal Point Process Mixture Models
Department of Statistics and Data Sciences, University of Texas at Austin · Department of Biostatistics and Informatics, University of Colorado Anschutz · Department of Mathematics, University of Texas at Austin
Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
Jul 23, 2026Khalid El-Darymli, Christoph H. Gierull, Katerina Biron +1Synthetic Aperture Radar Determinantal Point Process
Defence Research and Development Canada, Ottawa, Ontario, Canada · Memorial University, St. John’s, NL, Canada
Smooth Neural Point Processes via B-Splines
Jul 23, 2026Michele Bellomo, Riccardo Ramaschi, Alberto Dolara +1Determinantal Point Process Splines
Politecnico di Milano Milan, Italy · University College London London, UK
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
Jul 13, 2026Neha Gupta, Aditya MaheshwariDeterminantal Point Process Deep Learning Framework
Operations Management and Quantitative Techniques Area, Indian Institute of Management Indore, Indore 453556, India.
From Jumps to Signatures: a Generative Method for Temporal Point Processes
Jul 7, 2026Niels Cariou-Kotlarek, Vasileios LamposDeterminantal Point Process Time-Series Generation
Centre for Artificial Intelligence Department of Computer Science University College London
Fast determinantal sampling on general spaces and diffusion geometry
Jul 7, 2026Hoang-Son Tran, Pranav Gupta, Subhroshekhar GhoshDeterminantal Point Process Optimal Sample Complexity
Department of Mathematics National University of Singapore
Determinantal point process sampling for bioacoustic active learning
Jul 7, 2026Hugo Magaldi, Gabriel DubusPassive Acoustic Monitoring Pool-Based Active Learning
Eco-Anthropologie, Mus´eum National d’Histoire Naturelle, UMR7206, CNRS, Paris, France
Efficient Temporal Point Processes via Monotone Alternating Splines
Jul 2, 2026Cheng Wan, Quyu Kong, Feng ZhouDeterminantal Point Process Splines
Hong Kong University of Science and Technology, Hong Kong · Alibaba Cloud, Hangzhou, China · Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Jul 1, 2026Yahya Aalaila, Mouad Elhamdi, Gerrit Großmann +3Determinantal Point Process Epidemiological Models
1German Research Center for Artificial Intelligence (DFKI) · 5Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU) · 2Universit´e Mohammed VI Polytechnique +2
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Jul 1, 2026Yahya Aalaila, Gerrit Großmann, Sebastian VollmerDeterminantal Point Process Spatiotemporal
German Research Center for Artificial Intelligence (DFKI), Data Science and its Applications Research Group, Kaiserslautern, Germany · Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, Germany
Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
Jun 23, 2026Shuai Zhang, Yancheng Chen, Chuan Zhou +5Autoregressive Diffusion Autoregressive Generation
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China · Department of Data Science, City University of Hong Kong, Hong Kong 999077, China +1
Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection
Jun 17, 2026Richard Yi Da XuDeterminantal Point Process Spectral Decomposition
Hong Kong Baptist University · TadReamk Limited
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
Jun 15, 2026Yahya Aalaila, Sumantrak Mukherjee, Gerrit Großmann +1Determinantal Point Process Spatiotemporal
German Research Center for Artificial Intelligence (DFKI), Data Science and its Applications Research Group, Kaiserslautern, Germany
Non-Negative Matrix Factorization for Event Data
Jun 4, 2026Raphaël RomeroNonnegative Matrix Factorization Determinantal Point Process
Ghent University · Ghent, Belgium
GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
May 31, 2026Guanyu Zhou, Yao Liu, Yanglei Gan +5Determinantal Point Process Diffusion Models
University of Electronic Science and Technology of China, Chengdu, China
From DPPs to $k$ -DPPs: identifiability analysis via spectral decomposition
May 25, 2026Hideitsu Hino, Keisuke YanoIdentifiability Determinantal Point Process
The Institute of Statistical Mathematics, 10-3 Midori cho, Tachikawa City, 190-8562, Tokyo, Japan.
Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
May 17, 2026Zhitong Xu, Qiwei Yuan, Yinghao Chen +2Determinantal Point Process Temporal Dependence
Kahlert School of Computing, University of Utah · Celonis AI
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
May 13, 2026Mohammad R. Rezaei, Tejas Balaji, Rahul G. KrishnanTime-Series Generation Determinantal Point Process
Department of Computer Science University of Toronto Vector Institute Toronto, ON, Canada
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
May 13, 2026Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1Determinantal Point Process Subsampling
Department of Mathematics National University of Singapore · Univ. Lille, CNRS, Centrale Lille UMR 9189 – CRIStAL
LDDR: Linear-DPP-Based Dynamic-Resolution Frame Sampling for Video MLLMs
May 12, 2026Jingfeng Chen, Jiawen Qian, Wendi Deng +5Video Understanding Frame Rate
1Carnegie Mellon University · 2Individual Researcher · 3National University Singapore +2
PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
May 11, 2026Zetao YangElectronic Design Automation Physics-Aware Models
School of Mathematics and Statistics Beijing Institute of Technology Beijing, China
On Observation Time for Recovering Latent Hawkes Networks
May 8, 2026Jonas Linkerhägner, Michele Bortolasi, Lorenzo Baldassari +2Network Science Determinantal Point Process
University of Basel · Rice University
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
May 2, 2026Keyan Chen, Qiwei Yuan, Zhitong Xu +2Spatiotemporal Determinantal Point Process
Kahlert School of Computing University of Utah · Celonis AI
Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
Apr 22, 2026Huaiyu Jia, Jiehshun You, Yizhi Luo +2Decentralized Finance Determinantal Point Process
HKUST(GZ) · GUANGZHOU, China · Hong Kong Polytechnic University +1
On two ways to use determinantal point processes for Monte Carlo integration
Apr 21, 2026Guillaume Gautier, Rémi Bardenet, Michal ValkoDeterminantal Point Process Monte Carlo
†Univ. Lille, CNRS, Centrale Lille, UMR 9189 – CRIStAL, 59651 Villeneuve d’Ascq, France · *Inria Lille-Nord Europe, 40 avenue Halley 59650 Villeneuve d’Ascq, France · ‡DeepMind Paris, 14 Rue de Londres, 75009 Paris, France +1
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Oct 27, 2024Kaiyan Zhao, Yiming Wang, Yuyang Chen +3Rejection Sampling Replay
School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao, China · School of Artificial Intelligence Shenzhen Polytechnic University, China

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Adaptive Determinantal Client Scheduling in Federated Learning

Wen Xu, Ben Liang, Gary Boudreau, Hamza Sokun

University of Toronto, Canada · Ericsson, Canada

Federated Learning Schedulers Schedule Convergence Determinantal Point Process Diversity

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Scheduling clients for model training is critical in federated learning due to both data and system heterogeneity. Most previous works focus on the quality of the scheduled clients to achieve faster convergence, shorter wall-clock convergence time, or better average model performance. They rarely consider the diversity of clients, which is important to counter heterogeneity and improve performance for the worst-off clients. In this work, we advocate the use of determinantal point processes (DPPs) to model and enhance the diversity in client scheduling. We first design the kernel matrices of DPPs using gradient information and quality scores, which inherently enables a flexible quality-diversity trade-off. Applying fast MAP inference over DPPs, we propose Adaptive Determinantal Client Scheduling (ADCS) in FL. We further quantify the gradient approximation error of ADCS and develop convergence analysis for general biased client selection in FL with non-convex loss functions. We conduct comparative numerical experiments showing that ADCS outperforms state-of-the-art client scheduling algorithms, including both quality-based and diversity-based ones.

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