Determinantal Point Process

Also known as DPP

A determinantal point process (DPP) assigns probabilities to subsets so that similar items tend to repel one another, making it useful when selections should be both relevant and diverse. Researchers use DPPs for data and trajectory selection, video-frame and skill retrieval, clustering, and Monte Carlo integration, often with quality-diversity kernels or fixed-size k-DPPs. Spectral methods, continuous relaxations, diffusion kernels, and random-feature optimization aim to make sampling and learning more scalable. A central tension is how to gain expressive interactions, including attraction as well as repulsion, without losing efficient inference, reliable parameter identification, or strong sampling guarantees.

Latest papers

18 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Identication and Estimation of Nonsymmetric Determinantal Point Processes
Oct 10, 2026Christian Gouriéroux, Yang LuDeterminantal Point Process Representation Identifiability
University of Toronto, Toulouse School of Economics and CREST · Department of Mathematics and Statistics, Concordia University, Montreal, Canada
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels
Oct 9, 2026Hideaki Ishibashi, Takahiro Kawashima, Hideitsu HinoDeterminantal Point Process
Kyushu Institute of Technology · ZOZO Research · Institute of Statistical Mathematics +1
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions
Oct 9, 2026Takahiro Kawashima, Hideitsu HinoDeterminantal Point Process Kernel Methods
ZOZO Research · Institute of Statistical Mathematics · Waseda University
Beyond Top- $k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Sep 5, 2026Wang Wei, Tiankai Yang, Samyadeep Basu +7LLM Agent Skill Retrieval LLM Agent Routing
Virginia Tech · University of Southern California · Adobe Research +3
Fair Multi-View Determinantal Coresets via Adaptive NEPv
Aug 18, 2026Richard Yi Da XuCoreset Selection Determinantal Point Process
Hong Kong Baptist University and TadReamk Limited
Distributional Determinantal Point Process for Repulsive Clustering of Distributions
Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Bayesian Mixture Models Clustering
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
Fast determinantal sampling on general spaces and diffusion geometry
Jul 7, 2026Hoang-Son Tran, Pranav Gupta, Subhroshekhar GhoshDeterminantal Point Process
Department of Mathematics National University of Singapore
Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection
Jun 17, 2026Richard Yi Da XuDeterminantal Point Process Training Data Selection
Hong Kong Baptist University · TadReamk Limited
A Geometric Profile of Semantic Information in Text: Frame-Conditional Uniqueness and a Trade-Off Triangle for Scalar Summaries
May 27, 2026Dmitriy KompaneetsDeterminantal Point Process
Independent Researcher
From DPPs to $k$ -DPPs: identifiability analysis via spectral decomposition
May 25, 2026Hideitsu Hino, Keisuke YanoParameter Identifiability Determinantal Point Process
The Institute of Statistical Mathematics, 10-3 Midori cho, Tachikawa City, 190-8562, Tokyo, Japan.
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
May 24, 2026Han Liu, Zhi Xu, Xiaotong Zhang +5Determinantal Point Process Transfer-Based Adversarial Attacks
Dalian University of Technology Dalian, China · Peking University Beijing, China · Macao Polytechnic University Macao, China +1
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 Wavelet Methods
Department of Mathematics National University of Singapore · Univ. Lille, CNRS, Centrale Lille UMR 9189 – CRIStAL
Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention
May 12, 2026Abid Ali, Diego Molla-Aliod, Usman NaseemCross-Modal Alignment Determinantal Point Process
School of Computing, Macquarie University, Sydney, Australia
LDDR: Linear-DPP Dynamic-Resolution Frame Sampling for Video MLLMs
May 12, 2026Jingfeng Chen, Jiawen Qian, Wendi Deng +5Video Frame Selection Multimodal Large Language Models
Carnegie Mellon University · Individual Researcher · National University Singapore +2
E $^2$ DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
Apr 30, 2026Kaiyan Zhao, Borong Zhang, Yiming Wang +4Long-Horizon Robotic Manipulation Transformer
School of Computer Science, Wuhan University, Wuhan 430072, China · State Key Lab of Internet of Things for Smart City and Department of Computer Information Science, University of Macau, Macao 999078, China
On two ways to use determinantal point processes for Monte Carlo integration
Apr 21, 2026Guillaume Gautier, Rémi Bardenet, Michal ValkoDeterminantal Point Process Variance Reduction
†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
Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
Mar 23, 2026Joanna Zou, Youssef MarzoukTraining Data Curation Training Data Selection
Computational Science & Engineering Massachusetts Institute of Technology Cambridge, MA, 02139, USA
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Oct 27, 2024Kaiyan Zhao, Yiming Wang, Yuyang Chen +3Experience Replay Determinantal Point Process
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

Oct 10, 2026J/K move · Enter open · S save

Identication and Estimation of Nonsymmetric Determinantal Point Processes

Christian Gouriéroux, Yang Lu

University of Toronto, Toulouse School of Economics and CREST · Department of Mathematics and Statistics, Concordia University, Montreal, Canada

Determinantal Point Process Representation Identifiability

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Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonpositive pairwise dependence. Statistical research on nonsymmetric DPPs, which can accommodate positive dependence, remains limited. We study identication and estimation for this class. First, we establish the equivalence of two sucient conditions for a nonsymmetric kernel to dene a valid DPP. Second, we develop the identication theory for nonsymmetric DPPs. Third, we develop cardinality-truncated maximum-likelihood estimation and estimation by averaged stochastic gradient descent. Under suitable identication and regularity conditions, we establish consistency and asymptotic normality for both estimators. Simulations examine identication, the eects of truncation, and the nite-sample performance of the estimators. We illustrate the methods with two cyber applications at markedly dierent sampling frequencies: weekly sector-by-breach data and second-by-second enterprise authentication data with more than ve million set observations.