A new kernel method improves Poisson process intensity estimation.
problem Estimating intensity functions of inhomogeneous Poisson processes.
method Kernel method-based intensity estimator using least squares loss.
result K2IE achieves comparable predictive performance with improved efficiency. Method uses deep learning to estimate traffic intensity.
problem Estimating stochastic intensity of traffic processes.
method Deep neural networks for nonlinear filtering.
result Deep learning method accurately estimates traffic intensity.
We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra…
Neural Diffusion Intensity Models simplify Cox processes inference.
problem Intractable nonparametric estimation and posterior inference of latent stochastic intensity in Cox processes.
method Variational framework using neural SDEs, with theoretical guarantee of ELBO maximization coinciding with maximum likelihood estimation.
result Accurate recovery of latent intensity dynamics and posterior paths with significant speedup.
Estimates point process intensity with many covariates for prediction.
problem Estimating intensity of point processes with many covariates.
method Additive model with linear combinations of unknown functions for covariates.
result Optimal rates of convergence for large number of active covariates.
Bayesian approach for inhomogeneous Poisson process intensity estimation.
problem Intractable integral in likelihood of Gaussian Cox process.
method Joint modeling of intensity and cumulative intensity as transformed Gaussian process; exact MCMC sampler.
result Exact posterior inference without approximations.
New method models intensity functions on spheres using normalizing flows.
problem Modeling non-homogeneous Poisson process intensity functions on the sphere.
method Flexible bijective map using normalizing flows to transform intensity functions.
result Normalizing flows provide a flexible way to model intensity functions on spheres.
The model analyzes order flows in financial markets using Cox-type intensities.
problem Analyzing order dynamics in limit order books for market insights.
method Cox-type model for relative intensities, parameter estimation by quasi likelihood maximization, model selection with information criteria.
result The model provides excellent agreement with empirical data and identifies important factors in order book dynamics.
Proposes a DOA estimation method using IVs and DNNs for noise and reverberation reduction.
problem Accuracy of IV-based DOA estimation degrades due to noise and reverberation.
method Combines IV-based DOA estimation with DNNs for denoising and dereverberation.
result Average DOA error of 0.528 degrees, outperforming conventional methods.
The paper uses facial keypoints to estimate post-surgical pain intensity.
problem Accurately assessing pain levels from self-reported ratings is challenging.
method The approach analyzes 2D and 3D facial keypoints to estimate pain intensity.
result The pain estimation model uses multiple instance learning.
This paper discusses properties of a Doubly Stochastic Poisson Process (DSPP) where the intensity process belongs to a class of affine diffusions. For any intensity process from this class we derive an analytical expression for probability distribution functions of the corresponding DSPP. A specification of our results…
Extends Hawkes process for flexible residual modeling in point processes.
problem Modeling high-frequency financial data with complex residual distributions.
method Introduces self and mutually exciting point process with discretely Markovian dynamics.
result Flexible residual distributions improve intensity modeling and high-frequency data estimation.
Exact simulation method for market impact estimation under various execution strategies.
problem Estimating market impact from observed price trajectories under different execution strategies.
method Conditional simulation of point processes under perturbed intensities.
result Exact, event-driven algorithm for reconstructing counterfactual paths.
Framework for continuous-time network data representation learning.
problem Learning reliable representations of dynamic network interactions.
method Three-stage process: intensity estimation, projection learning, evolving node representation construction.
result Trajectories satisfy structural and temporal coherence, providing robust inference.
New method models Poisson intensity using RKHS for high-dimensional data.
problem Tractable nonparametric modeling of inhomogeneous Poisson intensity functions.
method Reproducing Kernel Hilbert Space (RKHS) formulation for intensity functions.
result Optimization of penalized likelihood can be cast as a tractable finite-dimensional problem.
Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.
problem Modeling dependence structures in time series data with realistic autocorrelation functions.
method Develops a Hawkes process with CARMA(p,q) intensity to capture more complex dependencies.
result The CARMA(p,q)-Hawkes model can reproduce more realistic dependence structures and is stationary and positive.
New models directly model inter-event times without intensity functions.
problem Learning temporal point processes with intensity-based approaches.
method Normalizing flows and mixture models for flexible and efficient modeling.
result Achieves state-of-the-art performance in prediction tasks.
Novel model for predicting event intensities from static and time series data.
problem Predicting event intensities from static and irregularly sampled time series data.
method Neural controlled differential equations and signature-based CoxSig model.
result The CoxSig model provides theoretical learning guarantees and performs well on various datasets.
New model estimates higher-order interactions in stochastic processes using lower-dimensional projections.
problem Estimating higher-order interaction effects in stochastic processes with limited data.
method Additive Poisson Process (APP) combines information geometry and generalized additive models to model intensity functions in lower dimensions.
result The model can estimate higher-order intensity functions with sparse data.
Develops a method to model multivariate count processes with Cox processes and shot noise intensities.
problem Modeling and estimating dependent count processes using granular data.
method Multivariate Cox process with shot noise intensities, connected via Lévy copulas.
result Allows for over-dispersion, auto-correlation, and realistic features in count processes.
Proposes a deep neural network for event intensity estimation.
problem Modeling irregular event sequences with historical dependencies.
method Non-parametric deep neural network with multi-channel RNN and fake event epochs.
result Outperforms state-of-the-art baselines on model fitting tasks.
Adaptive importance sampling for estimating point process statistics.
problem Estimating the expected value of a statistic of a locally stable point process.
method Adaptive importance sampling with Poisson point processes and cross-entropy minimization.
result The proposed estimator converges to the target value almost surely and is asymptotically normal.
In an asset return series there is a conditional asymmetric dependence between current return and past volatility depending on the current return's sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price hav…
HYVINT generates hypergraphs with intensity-driven incidence formation and variational learning.
problem Challenges in generating hypergraphs with mechanistic interpretation and limited latent space.
method HYVINT uses intensity-driven incidence formation and a lower-bound variational estimator for latent representations.
result HYVINT achieves strong fidelity and novelty on synthetic and real-world hypergraphs.
Develops a fast and precise method to evaluate likelihood of jump-diffusion models.
problem Evaluating likelihood functions of models with stochastic volatility and jumps.
method Deterministic nonlinear filtering algorithm based on Kitagawa's method.
result Deterministic filtering is faster and more precise than particle filter.
Paper develops a method to predict spatial point processes with guarantees.
problem Predicting the number of events in space with uncertainty.
method Regularized method to learn spatial models with out-of-sample guarantees.
result Method provides valid prediction intervals even when model is misspecified.
A new method uses SVMs and active learning for efficient fragility curve estimation.
problem Estimating fragility curves for structures under seismic and other excitations.
method Support Vector Machines (SVMs) coupled with active learning algorithm.
result Efficient estimation of fragility curves with reduced numerical calculations.
We propose a parametric model for the simulation of limit order books. We assume that limit orders, market orders and cancellations are submitted according to point processes with state-dependent intensities. We propose new functional forms for these intensities, as well as new models for the placement of limit orders …
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compute. A particular instance of interest is the L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a popular class of …
New method estimates tempered stable Lévy models with high accuracy.
problem Estimating volatility and jump intensity of tempered stable Lévy processes.
method Iterative method combining Truncated Realized Quadratic Variations and small-time approximations.
result Method outperforms existing alternatives in various scenarios.
Study exact community detection in k-community Gaussian mixtures with different intensities.
problem Community detection in k-community Gaussian mixtures with varying intensities.
method Explicitly find the threshold for exact recovery of maximum likelihood estimation.
result Threshold for exact recovery of maximum likelihood estimation is identified.
The paper extends intensity models for limit order books using marked point processes.
problem Modeling intensity ratios in limit order books with state dependency and clustering.
method Developed a new model combining three multiplicative components for marked point processes.
result The new model outperforms other intensity-based methods in predicting market order signs and aggressiveness.
Paper proposes Experts Model for better emotion detection in tweets.
problem Estimating intensity of emotion in tweets.
method Inspired by Mixture of Experts (MoE) model, each expert learns different features.
result Our Experts Model stands at top-5 results in emotion detection.
We propose an efficient method for estimating covariate effects in doubly-stochastic spatial models.
problem Computational demands and restrictive assumptions in existing doubly-stochastic spatial models.
method Penalized regression method for estimating covariate effects in doubly-stochastic point processes.
result Consistency and asymptotic normality of the covariate effect estimates achieved despite model misspecification.
New methods for calculating credit valuation adjustment with reduced noise and faster computation.
problem High statistical noise in computing sensitivities of CVA due to non-differentiable default intensities.
method Ad hoc analytical estimators to overcome non-differentiability and finite differences.
result Low statistical noise and fast computation of sensitivities to market quotes.
Bayesian nonparametric Hawkes process model with EM-variational inference.
problem Limited model flexibility in classical Hawkes processes.
method Gaussian process modulated Hawkes process with EM-variational inference.
result Recover underlying baseline intensity and triggering kernel without parametric restriction.
A new method estimates nonhomogeneous Poisson process intensities with super-resolution.
problem Estimating cyclic arrival rates of nonhomogeneous Poisson processes.
method Super-resolution estimation using sinusoidal waves with unknown parameters.
result Finite sample guarantees for super-resolution estimation under suitable conditions.
New method detects TC imagery patterns for rapid intensity change.
problem Detecting upcoming rapid intensity changes in TC satellite imagery.
method Nonparametric test of association between images and event labels using neural networks and bootstrap.
result Identifies archetypes of infrared imagery associated with elevated rapid intensification risk.
Boost-R uses gradient boosted trees for analyzing recurrence data.
problem Analyzing recurrence data with static and dynamic features.
method Gradient boosted additive trees with time-dependent functions.
result Estimates the cumulative intensity function of recurrent event processes.
Parameter-free clustering method using cluster catch digraphs (CCDs).
problem Finding the correct number of clusters in data without specifying a parameter.
method Hybrid of density-based and graph-based clustering methods using Ripley's K function.
result Minimum dominating sets of RK-CCDs estimate and distinguish clusters from noise.
Paper uses Gibbs sampler with jump diffusion for European option pricing.
problem Estimating market parameters for jump diffusion models in option pricing.
method Gibbs sampler applied to jump diffusion model for estimating drift, volatility, jump intensity, and occurrence.
result Demonstrates impact of jump effects on European call option and annuity pricing.
Modeling high-frequency order book data with Hawkes-Markovian process.
problem Capturing the dynamics of high-frequency order book events.
method Hawkes process with Markovian baseline intensities, LASSO regularization, and Akaike Information Criteria.
result Effective modeling of order book dynamics with reduced parameter redundancy.
This paper quantifies privacy-robustness and generalization-robustness trade-offs in adversarial training.
problem Privacy and generalization issues in adversarial training.
method Defines robustified intensity and empirical robustified intensity to measure robustness, proving differential privacy and generalization bounds.
result Proves adversarial training is (ε,δ)-differentially private and provides generalization bounds. Generative model combines shape and intensity priors for left atrium segmentation.
problem Challenges in segmenting left atrium MRI images due to shape variation and multimodality.
method Generative image model with mixture of Gaussians for shape priors and autoencoders for intensity priors.
result Maximizes posterior probability using a mixture of Gaussians for shape priors and autoencoders for intensity priors.
Deep Random Splines model neural activity data with better dimensionality.
problem Modeling neural population data with shape constraints.
method Deep neural network transforming Gaussian noise into spline parameters.
result Better dimensionality reduction of neural spiking activity.
Paper introduces statistical learning for point processes.
problem Statistical learning for point processes in general spaces.
method Combines bivariate innovations and point process cross-validation.
result Statistical learning approach outperforms state of the art.
Efficiently infers Poisson process intensity using Gaussian process with sigmoid link.
problem Estimating intensity of inhomogeneous Poisson processes efficiently.
method Variational free-form mean field optimization and sparse Laplace's method.
result Method is one order of magnitude faster than exact inference and competitive with quadratic link function models.
Asymptotic factorizations for the small-ball probability (SmBP) of a Hilbert valued random element X are rigorously established and discussed. In particular, given the first d principal components (PCs) and as the radius ε of the ball tends to zero, the SmBP is asymptotically proportional to (a) the joi…