Optimal weights improve particle-based approximations of discrete distributions.
problem Improving particle-based approximations of discrete distributions.
method Proving optimality of weights and showing how to compute them efficiently.
result Optimal weights can be computed from existing particle-based methods without extra costs.
Optimizes particle filtering for non-stationary environments.
problem Tracking and adapting to non-stationary environments in online prediction.
method Formulated an efficient particle filtering method using online mirror descent algorithm.
result Achieves optimal particle efficiency in non-stationary environments.
Interacting particle methods are increasingly used to sample from complex and high-dimensional distributions. These stochastic particle integration techniques can be interpreted as an universal acceptance-rejection sequential particle sampler equipped with adaptive and interacting recycling mechanisms. Practically, the…
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
problem Optimizing Bayesian filtering with deterministic particles to avoid degeneration.
method Progressive flow of particles through a sequence of sub-steps, each using an optimal transport map to replace non-equally weighted particles with equally weighted ones.
result The method avoids particle degeneration and simplifies the filtering process by not requiring inversions or monotonicity constraints.
New method accelerates energetic variational inference using particle dynamics.
problem Efficiently solving variational inference problems with reduced computational cost.
method Particle-based variational inference with implicit scheme, inspired by energy quadratization and operator splitting.
result Significantly reduces computational cost compared to existing methods.
A new variational inference method using optimal transport.
problem Approximating complex posterior distributions with flexible particle-based methods.
method Introducing a new particle-based variational inference method based on semi-discrete optimal transport.
result The method provides a particle approximation and optimal transportation densities.
New particle filter estimates model evidence without bias.
problem Unbiased estimation of marginal likelihood for model comparison.
method Particle filter with rejection control.
result Unbiased estimation of marginal likelihood.
We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm i…
New method learns latent energy models using particle algorithms.
problem Learning latent variable models with energy priors.
method Continuous-time SDEs for MMLE, particle-based discretization.
result Practical algorithm converges to solve MMLE problem.
New method scales Bayesian inference for nonlinear SSMs using buffered stochastic gradient.
problem Inference for nonlinear, non-Gaussian SSMs is computationally challenging and particle degeneracy increases with longer series.
method Extends stochastic gradient MCMC to nonlinear SSMs using particle methods and error bounds.
result Demonstrates the importance of particle buffered stochastic gradient for long sequential data.
New method optimizes multiple objectives using particle dynamics and gradient flow.
problem Optimizing multiple conflicting objectives in complex scenarios.
method Interacting particle method combining Langevin and birth-death dynamics with a dominance potential.
result Method effectively relocates dominated particles, improving Pareto optimality.
Develops particle-based optimisation for intractable gradient problems.
problem Optimisation of loss functions with intractable gradients.
method Mean-field dynamics and interacting-particle approximations.
result Exponential convergence and non-asymptotic error bound proved.
Framework expands particle filtering to estimate states beyond prior boundaries.
problem Limitations of traditional particle filtering in estimating states outside prior support.
method Diffusion-Enhanced Particle Filtering Framework with adaptive diffusion, entropy-driven regularisation, and kernel-based perturbations.
result Framework significantly improves state estimation accuracy and success rates for out-of-boundary targets.
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…
New algorithm trains latent diffusion models using interacting particles.
problem Training latent diffusion models efficiently and accurately.
method Reformulate training as minimizing a free energy functional, then approximate with interacting particles.
result The new algorithm outperforms previous methods in experiments.
Estimates log-likelihood of interacting particle systems using virtual particles.
problem Inconsistent estimation of finite-particle log-likelihood in large particle systems.
method Stochastic gradient estimate using continuous trajectory and virtual particle systems.
result Convergence to stationary points of limiting mean-field system's log-likelihood.
A method makes particle filters differentiable without altering their forward pass.
problem Compatibility issues between particle filters and automatic differentiation.
method Introduces a correction to particle weights using the stop-gradient operator.
result Automatic differentiation produces good estimators for gradients and second-order derivatives.
MPM-ParVI uses particle sampling for variational inference.
problem Variational inference for complex probabilistic models.
method Material Point Method (MPM) for particle-based simulation.
result Deterministic sampling and inference for intractable densities.
PSAEM combines EM and particle methods for efficient dynamical system learning.
problem Learning dynamical systems with stochastic approximation and particle methods.
method Particle stochastic approximation EM (PSAEM) algorithm combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS).
result PSAEM achieves superior computational performance and convergence compared to existing methods.
New sampling method improves on particle-optimization techniques.
problem Particles tend to collapse in SVGD, leading to poor sampling.
method Introduce stochastic noise to update particles, developing non-asymptotic convergence theory.
result More particles do not always improve approximation due to computational constraints.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
CPS solves inverse problems using forward passes and constrained particle seeking.
problem Solving inverse problems with limited forward observation information.
method Gradient-free approach that reformulates inverse problem as constrained optimization.
result CPS achieves results comparable to gradient-based methods while outperforming alternatives.
New method for online learning in interacting particle systems.
problem Parameter estimation in stochastic interacting particle systems.
method Stochastic approximation of gradient of asymptotic log likelihood using continuous observations.
result Convergence to stationary points of asymptotic log-likelihood under suitable assumptions.
Unified approach unites GANs and diffusion models using particle methods.
problem Combining GANs and diffusion models for generative tasks.
method Proposes a unified framework where generator training is seen as a generalization of particle models.
result Demonstrates that GANs and diffusion models can be integrated within a unified framework.
Topaz uses neural networks to pick particles from cryoEM images efficiently.
problem Manual particle picking is time-consuming and requires post-processing.
method Positive-Unlabeled (PU) learning with minimal labeled data.
result Topaz improves cryoEM reconstruction resolution by up to 0.15 Å.
New particle-based method improves semi-supervised learning robustness to label noise.
problem Label noise degrades semi-supervised learning accuracy.
method Particle competition and cooperation algorithm for robust semi-supervised learning.
result Improved robustness to label noise compared to existing methods.
Improves SVGD for high-dimensional Bayesian inference by reducing variance collapse.
problem Variance collapse in SVGD reduces accuracy and diversity of estimation.
method Augmented Message Passing SVGD (AUMP-SVGD) method, a two-stage optimization procedure.
result AUMP-SVGD achieves satisfactory accuracy and overcomes variance collapse in various benchmark problems.
Paper studies particle method for LSV model calibration, proving convergence and error bounds.
problem Calibration of local-stochastic volatility models with open well-posedness question.
method Regularized Euler--Maruyama scheme for particle approximation of McKean--Vlasov dynamics.
result Strong convergence of the Euler--Maruyama scheme with rate 1/2 in step-size.
A new EVI framework improves ParVI methods by maintaining variational structure and reducing KL-divergence.
problem Improving variational inference methods for better approximation of target distributions.
method EVI framework that minimizes the VI objective function based on an energy-dissipation law, including a new 'Approximation-then-Variation' scheme.
result The new scheme significantly decreases KL-divergence and outperforms existing ParVI methods in fidelity.
Improved particle Gibbs sampling by marginalizing parameters.
problem Bayesian inference in high-dimensional state-space models is challenging.
method Marginalized particle Gibbs sampling, combining MCMC and sequential Monte Carlo.
result Marginalization improves performance beyond the Gibbs sampler, scaling linearly.
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 studies when particle filtering is efficient for planning in partially observed systems.
problem The efficiency of particle filtering for planning in partially observed linear dynamical systems.
method Coupling of ideal and approximate sequences to bound particle complexity.
result Polynomially many particles suffice for stable systems to approximate optimal planning.
New methods combine MALA and mGRAD for scalable Bayesian inference in high-dimensional state-space models.
problem Bayesian inference in high-dimensional state-space models with limited scalability.
method Combines gradient-based MALA and prior-informed mGRAD for scalable inference.
result Extends classical MCMC methods to handle multiple time steps and particles.
New framework accelerates particle-based variational inference methods.
problem Improving the accuracy and speed of particle-based variational inference.
method Unified understanding of ParVIs through Wasserstein gradient flows, and acceleration framework based on the geometry of the Wasserstein space.
result Improved convergence and enhanced sample accuracy through the proposed acceleration framework and bandwidth-selection method.
A new method optimizes particles in SG-MCMC for better approximation.
problem High correlation in SG-MCMC samples.
method Directly optimizes particles to approximate target distribution.
result Particle optimization in SG-MCMC can be seen as SVGD with momentum.
Study of free particle's geometry and its perturbations using complex projective structures.
problem Understanding the geometry of a free particle and its perturbations.
method Use of complex projective structures and quasiconformal geometry to study perturbations.
result Main results loosely modeled on algebraic transformation theory, foundational for geometric understanding of the exact WKB method.
Paper compares semi-supervised training for differentiable particle filters.
problem Lack of labelled data in real-world applications.
method Compares two semi-supervised training objectives.
result Improved performance in environments with scarce labelled data.
Impute missing events in continuous-time sequences using particle smoothing.
problem Missing events in continuous-time sequences.
method Particle smoothing with trainable bidirectional LSTM proposals.
result Imputed sequences have low Bayes risk compared to ground truth.
GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.
problem High-dimensional multimodal sampling problems in lattice field theory.
method GPU-accelerated particle Monte Carlo methods (Sequential Monte Carlo and nested sampling).
result These methods match or outperform neural samplers in sample quality and wall-clock time.
New method optimizes function space for Bayesian neural networks, improving performance.
problem Bayesian neural networks inference challenge in high-dimensional, over-parameterized models.
method Directly optimizes a set of regression functions in function space for variational inference.
result Successfully overcomes sub-optimal performance in over-parameterized models, outperforming baselines.
GER learns particle dynamics from unpaired snapshots using physics-informed GANs.
problem Learning particle dynamics from unpaired snapshots with physics constraints.
method Physics-informed generative model to fit particle ensemble distributions.
result Inferred dynamics of particle ensembles governed by SODEs up to 100 dimensions.
Bayesian inference for neural networks improves uncertainty quantification.
problem Improving predictive uncertainty in neural networks.
method Ensemble Kalman filter extensions and interacting particle systems.
result Effective methods for quantifying predictive uncertainty in neural networks.
Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a prog…
MP-SVGD improves SVGD's performance in high-dimensional Bayesian inference.
problem Particles tend to collapse to modes in SVGD, especially in high dimensions.
method MP-SVGD converts high-dimensional inference into local problems over Markov blankets.
result MP-SVGD prevents vanishing repulsive force in high-dimensional space.
New method improves phylogenetic model inference by 30x.
problem Improving phylogenetic model inference for birth-death processes.
method Combines alive particle filter with delayed sampling.
result Significant improvement in effective sample size and acceptance rate.
Proposes PGPS for efficient Bayesian inference.
problem Efficient sampling from complex posterior distributions.
method Path-guided particle-based sampling with Log-weighted Shrinkage.
result PGPS generates samples closer to target distribution.
Unbiased smoothing method improves accuracy in noisy measurements.
problem Approximating latent Markov process expectations with noisy measurements.
method Coupling Particle Independent Metropolis-Hastings (PIMH) chains.
result Unbiased smoothing estimators for parallel computing and confidence intervals.
SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.
problem Sampling partially known densities or using gradients in probabilistic models.
method Smoothed Particle Hydrodynamics (SPH) for modeling fluid dynamics to approximate target densities.
result SPH-ParVI provides fast, flexible, scalable, and deterministic sampling for Bayesian inference and generative models.