We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PG-AS). Similarly to the existing PG with backward simulation (PG-BS) procedure, we use backward sampling to (considerably) improve the mixing of the PG kernel. Instead of using separate forward a…
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48 results for “Backward simulation”
New method finds failures in high-fidelity simulators with fewer steps.
problem Finding failures in high-fidelity simulators is expensive and impractical.
method Adaptive stress testing with backward algorithm adaptation from low-fidelity to high-fidelity.
result Significantly fewer high-fidelity simulation steps needed to find failures.
Extends Bayesian OWL for multi-stage treatment decisions.
problem Lack of uncertainty quantification in treatment decisions.
method Backward Bayesian Outcome Weighted Learning (BBOWL).
result Propagates uncertainty backward through DTR learning process.
A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuationmath.OC
A method for risk valuation using backward stochastic differential equations.
problem Risk evaluation in financial markets.
method Dual representation and stochastic control problem conversion, followed by dynamic programming.
result Piecewise-constant dual control provides a good approximation for risk valuation.
Proposes EM-C algorithm for solving stochastic control problems.
problem Solving multi-period finite time horizon stochastic control problems.
method Sequentially updates control policies using Monte Carlo simulation in a forward-backward manner.
result Demonstrates effectiveness in monopoly pricing and real business cycle studies.
New method approximates quadratic-growth BSDEs with short-term expansions.
problem Approximating solutions to quadratic-growth Backward Stochastic Differential Equations (BSDEs).
method Connecting semi-analytic asymptotic expansions over short-time intervals.
result Avoids Monte Carlo simulation and numerical integrations for estimating conditional expectations.
A new backward Monte Carlo method reduces variance in exotic option pricing.
problem Pricing exotic options with high variance in local volatility models.
method Backward Monte Carlo algorithm using a discrete multinomial tree.
result Substantial variance reduction in exotic option pricing.
Algorithm for hedging American options with transaction costs.
problem Hedging American options considering transaction costs.
method Backward Hedging algorithm minimizing loss function.
result Optimal hedging strategy determined by minimizing loss function.
New method for efficient conditional sampling from diffusion models.
problem Efficient conditional simulation from diffusion models.
method Explicit forward-backward bridging to express conditional simulation as an inference problem.
result Principled particle Gibbs and pseudo-marginal samplers for conditional distribution.
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.
Paper solves complex control problems using novel SDEs.
problem Solving stochastic differential games for nonlinear systems.
method Uses Deep Forward-Backward SDEs with neural networks.
result Numerical solution validated on two example systems.
Deep learning solves high-dimensional Bermudan swaption pricing and hedging efficiently.
problem Efficiently pricing and hedging Bermudan swaptions in Libor market model.
method Backward DNN solver for FBSDEs, demonstrating superior performance over Monte Carlo.
result Deep learning method effectively and efficiently solves high-dimensional Bermudan swaption pricing and hedging.
Bayesian method learns volatility from noisy data.
problem Learning volatility from noisy market data.
method Nonparametric Bayesian approach with piecewise constant prior and Forward Filtering Backward Simulation algorithm.
result Good performance on synthetic and real data.
Recurrent neural networks' hidden state can be reconstructed from its past, providing a theoretical framework for stability and tracking.
problem Hidden-state stability in RNNs
method Backward coherence analysis
result Almost-sure convergence, rates under mixing, interpretable limiting representation, finite pathwise stopping times, and theoretical framework for time-uniform confidence sequences.
A simple approach improves performance on both past and future tasks in lifelong learning.
problem Forgetting in lifelong learning, where performance on past tasks degrades when learning new tasks.
method Representation ensembling to improve performance on both future and past tasks.
result Representation ensembling demonstrates both forward and backward transfer across various datasets.
Quantum machine learning solves high-dimensional PDEs with lower variance and improved accuracy.
problem Approximating solutions to high-dimensional parabolic PDEs.
method Pure Variational Quantum Circuit (VQC) for BSDE approximation, using temporal discretization and Monte Carlo simulation.
result VQC achieves lower variance and improved accuracy in most cases, particularly in highly nonlinear regimes.
Proposes a neural network for high-dimensional American option pricing.
problem High-dimensional American option pricing and hedging.
method Deep neural network framework based on backward stochastic differential equations.
result The framework yields prices and deltas on the entire spacetime.
ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.
problem Detecting and quantifying causal influence ranges in complex systems.
method Bayesian data assimilation and assimilative causal inference (ACI) to trace causes back from observed effects.
result Mathematically rigorous formulations of forward and backward causal influence ranges (CIRs) for nonlinear dynamical systems.
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides t…
New deep learning methods improve solving FBSDEs without losing stability.
problem Solving high-dimensional nonlinear FBSDEs using classical methods is computationally infeasible.
method Inspired by deep learning, propose using deep learning architectures for FBSDEs and multilevel discretization.
result Multilevel discretization improves solution times by an order of magnitude.
Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forgetcs.LG
Neural nets learn and forget tasks sequentially, showing promising scalability.
problem Learning and forgetting of multiple visual tasks in a sequential setting.
method Simulated sequential learning of ten related visual tasks.
result Neural nets show forward facilitation and backward interference, which are key phenomena.
Method combines deep learning and elicitability for solving complex stochastic equations.
problem Solving McKean-Vlasov FBSDEs with common noise.
method Combines Picard iterations, elicitability, and deep learning.
result Validated on systemic-risk model and extended to quantile-mediated interactions.
We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward-in-Time Selection (BTS) that takes into account feedback and multi-collinearity, often present in multivariate time series. We compare …
Optimizes control of infectious disease spread using stochastic methods.
problem Optimizing control of highly infectious diseases like COVID-19.
method Reformulated Hamilton-Jacobi-Bellman equation as stochastic minimum principle, leading to forward-backward stochastic differential equations.
result Numerous numerical solutions presented under various scenarios.
Paper presents a new backward deep BSDE method for solving nonlinear FBSDE problems.
problem Nonlinear Forward Backward Stochastic Differential Equations (FBSDE) with terminal conditions.
method Backward deep BSDE method applied to FBSDE with nonlinear generators and random initial conditions.
result Derives exact and Taylor-based approximations for time-stepping nonlinear BSDEs.
New GFlowNet training framework using policy gradients for combinatorial object generation.
problem Training efficiency and robustness in GFlowNet models.
method Policy-dependent rewards and coupled training strategy for forward and backward policies.
result Advanced RL perspectives for robust gradient estimation improve GFlowNet performance.
Valuation of Credit Valuation Adjustment (CVA) has become an important field as its calculation is required in Basel III, issued in 2010, in the wake of the credit crisis. Exposure, which is defined as the potential future loss of a default event without any recovery, is one of the key elementsfor pricing CVA. This pap…
Improves sequence generation by training a backward network.
problem Generating long-term dependencies in sequence models.
method Train a backward recurrent network to predict states of a forward model.
result Achieves 9% relative improvement in speech recognition and significant improvement in caption generation.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
Generative models speed up complex system simulations.
problem Accurately forecasting the dynamics of complex systems at reduced cost.
method Generative Learning of Effective Dynamics (G-LED) using auto-regressive attention and Bayesian diffusion models.
result Generative models can accurately forecast complex system dynamics at lower computational cost.
SurvNet selects important variables in DNNs with false discovery rate control.
problem Variable selection in deep neural networks (DNNs) for interpretability.
method Backward elimination procedure based on a new variable importance measure.
result SurvNet estimates and controls false discovery rate of selected variables.
Optimal dual martingales, their analysis and application to new algorithms for Bermudan productsq-fin.CP
In this paper we introduce and study the concept of optimal and surely optimal dual martingales in the context of dual valuation of Bermudan options, and outline the development of new algorithms in this context. We provide a characterization theorem, a theorem which gives conditions for a martingale to be surely optim…
Shows uniqueness of mean curvature flow in higher dimensions.
problem Backwards uniqueness of mean curvature flow.
method Analysis of mean curvature flow with bounded second fundamental form.
result Proves backwards uniqueness in arbitrary codimension.
Paper generalizes LSMC algorithm for stochastic control problems.
problem Optimal decision problems with uncertainty.
method Backward simulation method with three pillars.
result Generalization of LSMC algorithm for a wide class of models.
Paper studies forward-backward envelope for convex problems and applies it to least squares.
problem Minimizing the sum of a convex and a smooth function.
method Derives conditions for level-bounded and Kurdyka-Łojasiewicz functions, applies forward-backward envelope to difference-of-convex problems.
result Forward-backward envelope can be efficiently minimized for certain convex problems.
Backward exploration reduces sample complexity in policy evaluation.
problem Empirical policy evaluation in reinforcement learning.
method Backward exploration algorithms from high-cost states.
result Reduced average-case sample complexity to .
Study eigenvalues of Laplace-Beltrami under Ricci flow on 3-manifolds.
problem Eigenvalue behavior under Ricci flow on 3-manifolds.
method Monotonic quantities and bounds constructed for the first eigenvalue.
result Eigenvalue tends to zero in converging cases after rescaling.
The study introduces backward baselines to distinguish past prediction from future prediction in machine learning models.
problem Differentiating between past and future prediction in machine learning models.
method Theoretical, empirical, and normative arguments support a family of simple and efficient statistical tests called backward baselines.
result The study provides a meaningful backward baseline for auditing black-box prediction systems.
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithmseess.SP
New algorithms improve direction finding using prior signal knowledge.
problem Efficiently estimate signal direction from sensor data.
method Multi-step knowledge-aided iterative conjugate gradient algorithms.
result MS-KAI-CG algorithms outperform existing techniques in simulations.
The paper extends NUP representations to factor graphs for better estimation.
problem Nontrivial model-based estimation problems.
method Augmenting factor graphs with convex-dual variables and NUP representations; proposing a new iterative algorithm.
result A new dual algorithm for state space problems.
Study proves existence of equilibrium in incomplete economies with discontinuous volatility.
problem Existence of incomplete Radner equilibrium with nondegenerate endogenous volatility.
method Established existence of solution for Markovian quadratic BSDEs with discontinuous generators using unique continuation and backward uniqueness.
result Existence of incomplete Radner equilibrium with nondegenerate endogenous volatility.
This paper explains how deep learning performs hierarchical learning efficiently.
problem How deep learning can perform hierarchical learning efficiently.
method Backward feature correction principle and SGD training.
result Deep learning can efficiently train complex hierarchical tasks using SGD.
Model strategic interactions between market makers and traders to optimize execution.
problem Optimizing execution in markets with strategic interactions.
method Stochastic game modeling with FBSDEs and decoupling approach.
result Established Nash equilibria and global well-posedness for specific models.
Wavelets improve accuracy in solving backward SDEs.
problem Solving backward stochastic differential equations (SDEs) with high accuracy and simplicity.
method Time discretization combined with trigonometric wavelets, enhanced by antireflective boundary technique.
result Improved numerical algorithm for SDEs with enhanced accuracy and ease of implementation.
New method uses zeroth-order queries to approximate proximal sampling efficiently.
problem Approximating proximal sampling with zeroth-order information.
method Direct simulation of heat flow dynamics, treating intermediate distribution as Gaussian mixture.
result Inherits exponential convergence under isoperimetric conditions, avoids rejection sampling.
A new method ranks and selects features without model fitting.
problem Feature importance measures algorithm-specific and need improvement.
method Integrates global sensitivity analysis with forward selection and backward elimination.
result Demonstrates clear advantage over state-of-the-art methods.
Paper presents IMRCs for evolving tasks with forward and backward learning.
problem Incremental learning of evolving tasks with few samples per task.
method Incremental minimax risk classifiers (IMRCs) that exploit forward and backward learning.
result IMRCs provide significant performance improvement, especially with reduced sample sizes.
SGD converges with perturbed forward-backward passes, explained by geometric amplification.
problem Analyzing convergence of SGD with perturbed forward-backward passes in composite optimization.
method Characterized propagation and amplification of perturbations, derived convergence guarantees for non-convex and PL objectives.
result Perturbations cascade through the computational graph, affecting convergence order under specific conditions.