Improves efficiency of simulators that fail to return.
problem Computational inefficiency in simulators that don't return for certain inputs.
method Trains a conditional normalizing flow to propose perturbations.
result Increased computational efficiency of simulators.
A new method for optimizing engineering simulations efficiently.
problem Optimizing deterministic engineering simulations efficiently.
method KGCP (Knowledge Gradient policy for Continuous Parameters) derived from differences with EI (Expected Improvement).
result KGCP has similar performance to EI but better convergence for complex problems.
Modified neural network models Markov Chains for non-deterministic outcomes.
problem Simulating non-deterministic behavior in neural networks.
method Developed a modified neural network model capable of simulating Markov Chains.
result Demonstrated the network's ability to produce non-deterministic outcomes.
Framework simulates market microstructure with stable Hawkes processes.
problem Reproduce realistic market order flow dynamics.
method Deterministic C++ LOB simulator with Hawkes-driven stochastic order flow.
result Derives stability and ergodicity proofs for Hawkes models.
In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common…
Counterfactual learning improves SMT by smoothing out deterministic logs.
problem Deterministic logging limits exploration in SMT systems.
method Additive and multiplicative control variates to smooth out deterministic components.
result Improvements of up to 2 BLEU points achieved through counterfactual learning.
Modeling maximum drawdown records in capital markets using PDMP.
problem Capturing the statistical properties of maximum drawdown records in financial markets.
method Piecewise Deterministic Markov Process (PDMP) for modeling, statistical analysis of mean and variance, simulation study, parameter estimation techniques.
result Derivation of statistical results including mean and variance of maximum drawdown records.
Model predicts human food choices based on demographics.
problem Predicting human food choices from demographic data.
method Non-deterministic model based on NHANES dataset and behavioral studies.
result Generates synthetic data similar to original dataset.
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic le…
Generative models using PDMPs with explicit jump rates and kernels.
problem Creating efficient generative models for complex data distributions.
method Piecewise deterministic Markov processes (PDMPs) with explicit expressions for jump rates and kernels.
result Efficient training and simulation methods for PDMP-based generative models.
The log returns of financial time series are usually modeled by means of the stationary GARCH(1,1) stochastic process or its generalizations which can not properly describe the nonstationary deterministic components of the original series. We analyze the influence of deterministic trends on the GARCH(1,1) parameters us…
A new method assigns hidden parameters deterministically to improve learning efficiency.
problem Traditional learning methods struggle with high computational burden.
method Two-stage learning with deterministic assignment of hidden parameters.
result Deterministic assignment of hidden parameters almost matches traditional learning's generalization performance.
A model of fluctuations in the market price including many deterministic dealers, who predict their buying and selling prices from the latest price change, is developed. We show that price changes of the model is approximated by ARCH(1) process. We conclude that predictions of dealers affected by the past price changes…
We introduce a prototype model in an attempt to capture some aspects of market dynamics simulating a trading mechanism. The model description starts with a discrete-space, continuous-time Markov process describing arrival and movement of orders with different prices. We then perform a re-scaling procedure leading to a …
MF-GLaM models improve stochastic simulator emulation with multifidelity data.
problem Challenging to emulate stochastic simulators' full conditional probability distribution.
method Proposes MF-GLaMs to efficiently emulate HF stochastic simulators using LF data.
result MF-GLaMs achieve improved accuracy or comparable performance at reduced cost.
Paper presents conditions for SSC success with missing data.
problem Determining success of SSC under missing data in UoS model.
method Two variants of SSC with zero-filling, providing deterministic conditions.
result Deterministic conditions for perfect clustering under various missing data patterns.
We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby N indepe…
FM4PDE learns PDE solutions from sparse data.
problem Reconstructing PDE solutions from limited observations.
method Flow-matching generative framework that learns PDE coefficients and solutions.
result Error guarantees for guided procedures, including deterministic and stochastic samplers.
Paper introduces deterministic EM approximations for non-convex likelihood functions.
problem Deterministic approximations for the E-step of EM algorithm are lacking.
method Developed a theoretical framework for deterministic approximations, analyzed Riemann sums and tempered EM.
result Proved convergence guarantees for deterministic approximations and new non-trivial temperature profiles.
This research enhances exploration in DDPG using latent trajectory optimization.
problem Limited exploration in DDPG with deterministic policies.
method Model-based trajectory optimization for exploration in DDPG, using a learned deep dynamics model.
result Improved performance in continuous control tasks, especially with sparse rewards and images.
Flexible model captures commodity skews with maturity effects.
problem Capturing market skew in commodity futures with maturity effects.
method Non-parametric extension with leverage functions, calibrated using Monte Carlo simulation.
result Model accurately captures market smile and implied variance accumulation.
A novel meta-learning method using ES for efficient reinforcement learning.
problem Sample inefficiency in reinforcement learning.
method Evolution strategies (ES) for exploration in parameter space, deterministic policy gradients for adaptation.
result Demonstrates improved performance in high-dimensional control tasks compared to gradient-based methods.
This paper tackles reliability analysis for stochastic systems using surrogate models.
problem Traditional reliability analysis relies on deterministic models, which are not suitable for stochastic systems with non-repeatable outcomes.
method The paper introduces reliability analysis for stochastic models by using generalized lambda models and stochastic polynomial chaos expansions as surrogate models to lower computational cost.
result The surrogate models enable efficient uncertainty quantification at a lower cost than traditional Monte Carlo simulation.
A deterministic trading strategy by a representative investor on a single market asset, which generates complex and realistic returns with its first four moments similar to the empirical values of European stock indices, is used to simulate the effects of financial regulation that either pricks bubbles, props up crashe…
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
New model predicts dynamic tax evasion with audits and imitation.
problem Static treatment of tax compliance and evasion in Bertotti and Modanese model.
method Piecewise Deterministic Markov Processes (PDMPs) for audits and imitation mechanisms.
result Model shows persistent fluctuations and stationary distribution, not extreme equilibrium.
Recently, a number of mostly ℓ1-norm regularized least squares type deterministic algorithms have been proposed to address the problem of \emph{sparse} adaptive signal estimation and system identification. From a Bayesian perspective, this task is equivalent to maximum a posteriori probability estimation under a …
Paper analyzes subspace clustering with missing data under UoS model.
problem Subspace clustering under missing data with UoS model.
method Deterministic analysis of conditions for clustering in two cases.
result Dual conditions for Case II provide perfect clustering under missing data.
DQAEM uses quantum annealing to improve MLE.
problem EM algorithm's dependence on initial configurations and inability to find global optimum.
method Quantum annealing extension of EM (DQAEM).
result DQAEM outperforms EM in MLE.
Electrostatics method samples complex distributions deterministically.
problem Sampling and inference of complex, high-dimensional distributions.
method Electrostatics-based particle system with Newton mechanics principles.
result Method achieves comparable performance to other methods in benchmark tasks.
This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local …
New method speeds up Bayesian inference for complex simulators.
problem Challenges in Bayesian inference for complex stochastic simulators with intractable likelihood functions.
method Optimization Monte Carlo framework reformulated as deterministic optimization problems with gradient-based methods.
result Accurate posterior inference with reduced runtimes compared to existing methods.
Study applies Gai-Kapadia framework to global equity markets to assess systemic risk and default cascades.
problem Assessing systemic risk and default cascades in global equity markets.
method Used Gai-Kapadia framework, 20-asset network, Monte Carlo simulations, and deterministic propagation analysis.
result High clustering among Brazilian assets leads to localized contagion, while developed markets show resilience.
A new method for high-dimensional RBDO using stochastic emulators.
problem Efficient RBDO in high-dimensional settings.
method Unified stochastic representation, stochastic emulators, deterministic mapping.
result Significant computational gains in high-dimensional settings.
The paper develops a model for sovereign debt dynamics with explicit maturity structure.
problem Analyzing the sustainability and risk of long-term sovereign debt issuance.
method Discrete-time model with explicit maturity structure, deterministic and stochastic extensions.
result The model identifies conditions for ergodic convergence and derives analytical formulas for key metrics.
Diffusion models' sampling paths lie in a low-dimensional subspace, resembling boomerangs.
problem Understanding the geometric structure of diffusion-based generative models.
method Characterization of deterministic sampling trajectories using low-dimensional subspace and kernel-estimated data modeling.
result Sampling trajectories in diffusion models are confined to a low-dimensional subspace and exhibit a boomerang shape.
New Monte Carlo methods use continuous-time Markov processes for big data.
problem Efficient sampling from posterior distributions in big data.
method Piecewise deterministic Markov processes for continuous-time Monte Carlo.
result Continuous-time Monte Carlo methods can target true posterior distributions efficiently.
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
problem Efficiently estimating quantities of interest from multi-fidelity simulations.
method Bayesian sequential strategy that maximizes the ratio of expected uncertainty reduction to simulation cost.
result MR-SUR strategy unifies and provides principled approaches to develop new methods.
Proposes a deterministic LIME for CAD systems.
problem Instability in LIME explanations.
method Uses agglomerative HC and KNN to select relevant clusters and trains a linear model.
result DLIME is more stable than LIME.
Improved pricing method for illiquid assets using Lambert function.
problem Inaccurate pricing of illiquid assets using traditional methods.
method Deterministic decomposition of reservation price using Lambert function; improved Monte Carlo method (LMC).
result Improved accuracy in pricing illiquid assets through LMC method.
The paper shows how shared random seeds can reduce variance in machine learning evaluations.
problem The statistical structure of comparative evaluation under shared random seeds is not well understood.
method An extended learning-based multi-agent economic simulator was used to demonstrate the effects of shared random seeds on variance reduction.
result Pairing seeds can reduce variance in machine learning evaluations, especially when outcomes are positively correlated at the seed level.
RL agent learns to smoothly change lanes in a dynamic driving environment.
problem Challenging lane change control with safety and comfort.
method Formulated continuous action for lane change in DDPG algorithm, defined reward function for learning.
result Successfully changed lanes with 100% success rate in diverse driving situations.
Paper tackles running physics model training, improving reinforcement learning efficiency.
problem Training a physiologically-based human model to navigate complex obstacle courses efficiently.
method Physics-based reinforcement learning, using Deep Deterministic Policy Gradient, with improvements like layer normalization and parameter noise.
result Deep Deterministic Policy Gradient proved most efficient, and improvements helped stabilize training.
Deep RL controls anesthesia more accurately than traditional methods.
problem Controlling the level of unconsciousness during anesthesia.
method Deep Reinforcement Learning (DRL) to map patient state to propofol dosage.
result Deep RL model outperformed traditional controllers (1.7% vs 3.4% median absolute performance error).
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
problem Analyzing trade execution strategies in a stochastic market with price impact.
method Formulated a Markov game model and used backward induction method of dynamic programming.
result Explicit closed-form execution strategy at Markov perfect equilibrium.
New framework for evaluating ad auctions using stochastic modeling.
problem Challenges in evaluating deterministic ad auctions.
method Repurposed bid landscape model to approximate propensity scores, enabling robust OPE estimators.
result Remarkable alignment with online A/B test results, achieving 92% MDA in CTR prediction.
The paper proposes a machine learning approach for production forecasting without model calibration.
problem Generating accurate production forecasts for reservoir development.
method Sequential model aggregation using machine learning algorithms without model calibration.
result The proposed method provides robust multi-step-ahead production forecasts.
Approximates derivative pricing under fractional stochastic volatility.
problem Derivative pricing under fractional stochastic volatility model.
method Approximate expression derived from deterministic functions and fractional Ornstein-Uhlenbeck process.
result Numerical simulations show the feasibility and effect of long-range dependencies on derivative prices.