Deep learning outperforms traditional methods in estimating OU process parameters.
problem Parameter estimation of the Ornstein-Uhlenbeck process is challenging.
method Used a multi-layer perceptron to estimate OU process parameters compared to traditional methods like Kalman filter and maximum likelihood estimation.
result Deep learning method outperforms traditional methods in parameter estimation of the OU process.
Automated process discovery is a class of process mining methods that allow analysts to extract business process models from event logs. Traditional process discovery methods extract process models from a snapshot of an event log stored in its entirety. In some scenarios, however, events keep coming with a high arrival…
Method identifies regions of maximum dissimilarity in stochastic processes.
problem Comparing local characteristics of two random processes to find periods of maximum dissimilarity.
method Bayesian inference with integrated nested Laplace approximation for stochastic processes.
result Identifies regions of maximum dissimilarity with a certain volume.
New method detects structural shifts in multivariate Hawkes processes.
problem Detecting changes in multivariate Hawkes processes.
method Using Fréchet statistics on overlapping windows of causal network.
result Accurately detects and characterizes changes in causal structure.
Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have b…
New method learns stochastic process representations without exact reconstruction.
problem Learning exact representations of high-dimensional noisy stochastic processes.
method CReSP framework for contrastive learning of stochastic processes.
result Effective for learning representations of various stochastic processes.
A new method uses active learning to monitor industrial processes more accurately.
problem Classifying process states (IC, OC) with limited labeled data.
method Stream-based active learning for partially hidden Markov models.
result Improved dynamic recognition of process states, especially unseen classes.
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned. The Ostomachion p…
We propose a simple method that combines neural networks and Gaussian processes. The proposed method can estimate the uncertainty of outputs and flexibly adjust target functions where training data exist, which are advantages of Gaussian processes. The proposed method can also achieve high generalization performance fo…
New debiasing methods for fine-tuned neural networks.
problem Bias in neural networks used for high-stakes decisions.
method Intra-processing methods designed for fine-tuned models.
result Demonstrated effectiveness of intra-processing methods across various datasets.
Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel m…
A conjugate Bayesian method detects change points in Hawkes processes efficiently.
problem Non-conjugacy between Hawkes process likelihood and prior causes inefficiency in change point detection.
method Data augmentation to propose a conjugate Bayesian two-step change point detection method.
result The conjugate method is more accurate and efficient than non-conjugate methods.
Deep RL optimizes processing paths to desired material structures.
problem Optimizing processing paths to achieve desired material properties.
method Deep reinforcement learning guided by structure representations and reward signals.
result Algorithm learns to find optimal paths to target structures in material space.
New sparse Gaussian process method tackles unconstrained regression problems.
problem Dealing with physical systems that satisfy inequality constraints.
method Extends constrained Gaussian process by redefining hat basis functions.
result Reduces computational complexity from O(n3) to O(nm2). A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.
problem Efficient inference in complex hierarchical point processes.
method Developed an efficient posterior sampling via Markov chain Monte Carlo for likelihood-based inference.
result More hidden Poisson processes improve likelihood fitting and event prediction.
Improves SVGP methods for faster and more accurate Gaussian process inference.
problem Efficient non-conjugate Gaussian process inference.
method Dual parameterization of SVGP methods using site parameters.
result Faster and more accurate inference with tighter evidence lower bound.
Directly simulates squared Bessel processes efficiently.
problem Simulating squared Bessel processes accurately and efficiently.
method Two-dimensional Chebyshev expansion for non-central chi-square distribution inverse.
result Accurate and efficient simulation for various degrees of freedom.
New method uncovers hidden causal connections in multivariate point process networks.
problem Unobserved hidden variables confound causal discovery in high-dimensional point process networks.
method Proposes a deconfounding procedure to estimate causal interactions among observed nodes with unknown unobserved processes.
result The method accurately identifies causal interactions among observed processes, even with hidden variables.
New method infers and samples point processes from latent diffusion.
problem Modeling point processes with latent diffusion.
method Itô's excursion theory for inference and sampling.
result Proposes a new method to infer and sample point processes.
Proposes a method to align Hawkes processes across different event spaces.
problem Aligning Hawkes processes in multiple event spaces.
method Fused Gromov-Wasserstein alignment for Hawkes processes.
result The method effectively aligns Hawkes processes and their event types.
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. I…
Methodology for estimating marked Hawkes processes with neural networks.
problem Estimating conditional intensity of marked Hawkes processes.
method Proposes two models: Shallow Neural Hawkes with marks and Neural Network for Non-Linear Hawkes with Marks.
result Validation on synthetic datasets and real-world cryptocurrency order book data.
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative met…
New neural method for inferring Markov jump processes.
problem Inference in Markov jump processes is challenging.
method Variational inference using neural ODEs and backpropagation.
result Trains neural representations of data to approximate process rates.
Efficient GP framework for scalable non-stationary processes.
problem Heavy memory and computational requirements in Gaussian process regression for large data sets.
method Exploits structure in the kernel matrix, uses multiple sets of non-equidistant inducing points, and employs Toeplitz and Kronecker structure for efficient inference.
result Demonstrated scalability on numerical examples and large biomedical datasets.
New algorithm learns bridged diffusion processes without time-reversals.
problem Learning bridged diffusion processes efficiently and accurately.
method Score matching with Doob's h-transform, avoiding time-reversals.
result Outperforms existing methods in learning bridged diffusion processes.
We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal dist…
New methods improve estimation of nonhomogeneous Poisson processes from limited data.
problem Estimating nonhomogeneous Poisson processes from limited data.
method Formulated as a learning generalization problem, proposed adaptive and data-driven binning methods.
result Improved estimation of nonhomogeneous Poisson processes with limited data.
New scalable variational Bayes methods for Hawkes processes.
problem Computational intractability of Bayesian estimation for generalised nonlinear Hawkes processes.
method Unified variational Bayes framework, adaptive mean-field approximation, sparsity-inducing procedure.
result Adaptive mean-field variational algorithm for sigmoid Hawkes processes is scalable and robust.
New method estimates volatility for Lévy processes with unbounded jumps efficiently.
problem Efficient estimation of volatility for Lévy processes with unbounded jumps.
method Developed a new estimator based on high-order expansions of truncated moments.
result Method outperforms existing alternatives in estimating volatility.
A fast Monte Carlo method for additive processes and option pricing.
problem Efficiently pricing path-dependent options with additive processes.
method Developed a fast Monte Carlo scheme for additive processes, analyzing and reducing numerical error sources.
result Shows significant reduction in error (1 bp or below) for pricing path-dependent options.
NNNH uses neural networks to model complex event patterns.
problem Analyzing multi-dimensional nonlinear Hawkes processes with mutual excitation and inhibition.
method NNNH employs feedforward neural networks to model individual kernels and base intensity, optimizing parameters via Stochastic Gradient Descent.
result NNNH accurately captures complexities of nonlinear Hawkes processes, as demonstrated by numerical experiments.
We define a numerical method that provides a non-parametric estimation of the kernel shape in symmetric multivariate Hawkes processes. This method relies on second order statistical properties of Hawkes processes that relate the covariance matrix of the process to the kernel matrix. The square root of the correlation f…
In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event fro…
Deep learning model predicts material microstructures from processing methods.
problem Linking processing conditions to material properties for material design.
method Conditional image synthesis using Wasserstein GAN with gradient penalty.
result Deep learning model synthesizes high-quality microstructures for given cooling methods.
The paper examines Wiener process for LID estimation methods.
problem Estimating local intrinsic dimension in high-dimensional datasets.
method Investigates recent LID estimation methods from a Wiener process perspective.
result Explains how methods behave under non-ideal conditions.
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 detects outliers using ensembles of Dirichlet process mixtures.
problem Challenges in unsupervised outlier detection using Dirichlet process mixtures.
method Ensembles of Dirichlet process Gaussian mixtures with random subspace and subsampling.
result Empirically outperforms existing approaches in unsupervised outlier detection.
Neural networks estimate spatial process likelihoods efficiently.
problem Challenges in estimating spatial processes with slow or intractable likelihoods.
method Convolutional neural networks trained on a classification task to learn likelihood function.
result Neural likelihood surfaces provide fast and accurate parameter estimation.
Paper proposes method for generating paths of stochastic volatility CGMY process for option pricing.
problem Generating accurate sample paths for stochastic volatility models for option pricing.
method Monte-Carlo method for European and American options, least square regression for calibration.
result Calibrated model parameters to S\&P 100 index options market using path-dependent options.
Two methods improve simulation of European call options under Heston model.
problem Efficient simulation of European call options under Heston model.
method Two strongly convergent and positivity-preserving methods for Cox-Ingersoll-Ross process under Lamperti transformation: truncated Euler and backward Euler methods.
result Explicit truncated Euler method is computationally effective and robust under high volatility, while implicit backward Euler method provides high accuracy and stability.
New method speeds up inference for non-conjugate Gaussian processes.
problem Inference for non-conjugate Gaussian processes is slow and unreliable.
method Automated augmented conjugate inference method that constructs auxiliary variables to make the model conditionally conjugate.
result Our method is up to two orders of magnitude faster and more robust than existing methods.
Study evaluates post-processing methods for improving solar power forecasts.
problem Improving accuracy of probabilistic solar energy forecasts through model chain approaches.
method Systematically evaluates different post-processing strategies for ensemble weather forecasts and direct solar power forecasting.
result Post-processing significantly improves solar power generation forecasts, especially when applied to power predictions.
This study bridges discrete and continuous state spaces using the Ehrenfest process and diffusion models.
problem Understanding the relationship between discrete and continuous state spaces in stochastic processes.
method Investigates time-continuous Markov jump processes on discrete state spaces and their correspondence to state-continuous diffusion processes.
result The time-reversal of the Ehrenfest process converges to the time-reversed Ornstein-Uhlenbeck process, bridging discrete and continuous state spaces.
This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multivariate Gaussian processes from the circulant embedding literature. The method can be performed in O(nlog2n) operations, where…
A new method uses Gaussian processes and deep kernel learning to price high-dimensional American options efficiently.
problem Challenges in pricing high-dimensional American options, especially with excessive computational costs.
method Modified Gaussian process regression with deep kernel learning and sparse variational Gaussian processes.
result The method outperforms least squares Monte Carlo in high-dimensional scenarios, especially with Merton's jump diffusion model.
New method for QPT without needing to know or prepare specific input states.
problem Quantum process characterization with unknown input states.
method Blind Quantum Process Tomography (BQPT) with single-preparation methods.
result Ability to characterize quantum processes using arbitrary unknown input states.
New method combines spectral and sparse methods for Gaussian processes.
problem Efficiently fitting Gaussian processes to large datasets.
method Orthogonally decoupled variational Fourier features.
result Competitive performance on synthetic and real-world data.