Point Processes

Benchmarks archive 2025-07-28

8 leaderboard tables shown for this task, 8 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 164 papers with code (541 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
  19 May 2019 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
• Intensity-Free Learning of Temporal Point Processes
  26 Sep 2019 5 repositories listed
  The standard way of learning in such models is by estimating the conditional intensity function.
• Determinantal point processes for machine learning
  25 Jul 2012 5 repositories listed
  Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
• Kronecker Determinantal Point Processes
  26 May 2016 4 repositories listed
  Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of N items.
• Modelling Behavioural Diversity for Learning in Open-Ended Games
  14 Mar 2021 3 repositories listed Syntology ran 8 of 8 samples · 0 unverified · 8 pointer-only (licence)
  Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.
• Transformer Hawkes Process
  21 Feb 2020 3 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.
• Scalable Log Determinants for Gaussian Process Kernel Learning
  9 Nov 2017 3 repositories listed
  For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an n ×n…
• Retrieval of Temporal Event Sequences from Textual Descriptions
  17 Oct 2024 2 repositories listed
  To advance this task, we introduce TESRBench, a comprehensive benchmark for temporal event sequence retrieval (TESR) from textual descriptions.
• DeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction
  23 Aug 2024 2 repositories listed
  The implementation of DeTPP is publicly available on GitHub.
• HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
  20 Jun 2024 2 repositories listed
  Accurately forecasting multiple future events within a given time horizon is crucial for finance, retail, social networks, and healthcare applications.
• Spatio-temporal Diffusion Point Processes
  21 May 2023 2 repositories listed Syntology ran 7 of 7 samples · 0 unverified · 7 pointer-only (licence)
  To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
• Scalable Sampling for Nonsymmetric Determinantal Point Processes
  20 Jan 2022 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  However, existing work leaves open the question of scalable NDPP sampling.
• Counterfactual Temporal Point Processes
  15 Nov 2021 2 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning.
• Noise-Contrastive Estimation for Multivariate Point Processes
  2 Nov 2020 2 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  The log-likelihood of a generative model often involves both positive and negative terms.
• Particle gradient descent model for point process generation
  27 Oct 2020 2 repositories listed
  This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window.
• Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
  17 Jun 2020 2 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.
• Coverage probability in wireless networks with determinantal scheduling
  9 Jun 2020 2 repositories listed
  We propose a new class of algorithms for randomly scheduling network transmissions.
• Detecting discrete processes with the Epps effect
  21 May 2020 2 repositories listed
  The Epps effect is key phenomenology relating to high frequency correlation dynamics in financial markets.
• Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
  15 Apr 2020 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
• Effective Diversity in Population Based Reinforcement Learning
  3 Feb 2020 2 repositories listed Syntology ran 10 of 12 samples · 2 unverified · 9 pointer-only (licence)
  Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment.
• Exact sampling of determinantal point processes with sublinear time preprocessing
  31 May 2019 2 repositories listed
  For this purpose, we propose a new algorithm which, given access to 𝐋, samples exactly from a determinantal point process while satisfying the following two properties: (1) its preprocessing cost is n ·poly(k), i.
• Neural Jump Stochastic Differential Equations
  24 May 2019 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.
• Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
  6 Mar 2019 2 repositories listed
  Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as…
• DPPy: Sampling DPPs with Python
  19 Sep 2018 2 repositories listed
  Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine…
• Determinantal Point Processes for Coresets
  23 Mar 2018 2 repositories listed
  We apply our results to both the k-means and the linear regression problems, and give extensive empirical evidence that the small additional computational cost of DPP sampling comes with superior performance over its…
• Optimized Algorithms to Sample Determinantal Point Processes
  23 Feb 2018 2 repositories listed
  The standard sampling algorithm is separated in three phases: 1/~eigendecomposition of 𝐋, 2/~an eigenvector sampling phase where 𝐋's eigenvectors are sampled independently via a Bernoulli variable parametrized by their…
• Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
  10 Jul 2017 2 repositories listed
  Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
• Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
  24 May 2017 2 repositories listed
  In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous…
• Diversity Networks: Neural Network Compression Using Determinantal Point Processes
  16 Nov 2015 2 repositories listed
  We introduce Divnet, a flexible technique for learning networks with diverse neurons.
• MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
  9 Jul 2025 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  To address this task, we propose Multi-Source DPPs, a simple yet strong baseline that extends the Determinantal Point Process (DPP) to multi-sources.

Syntology lines on 12 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.