Geometric tempering fails for Langevin dynamics, proving convergence limits.
problem Proving convergence and limitations of geometric tempering for Langevin dynamics.
method Theoretical investigation of geometric tempering using Langevin dynamics.
result Geometric tempering can lead to exponential time convergence and poor functional inequalities.
The paper connects tempering and entropic mirror descent for sampling.
problem Sampling from a target distribution with known unnormalized density.
method Establishes the connection between tempering SMC and entropic mirror descent, deriving convergence rates and geometric insights.
result Tempering SMC iterates correspond to entropic mirror descent on the reverse KL divergence, providing new optimization perspectives.
In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation. We present two tempering by subsampling algorithms, subsampled…
New adaptive temperature selection improves parallel tempering efficiency.
problem Enhancing mixing in multi-modal distributions using parallel tempering.
method Adaptive temperature selection using policy gradient approach.
result Lower integrated autocorrelation times achieved compared to traditional methods.
New method improves sampling from complex, multi-peaked distributions.
problem Sampling from high-dimensional, multimodal distributions using HMC.
method Combines tempered HMC with automatic tuning strategies.
result Demonstrates more effective scaling with dimension than adaptive methods.
Geometric tempering improves sampling from distributions, with exponential convergence rates.
problem Sampling from probability distributions using gradient flow dynamics.
method Geometric tempering of the target distribution in Wasserstein and Fisher-Rao gradient flows.
result Exponential convergence in continuous and discrete time for geometric tempering.
New theorem improves spectral gap for sampling from mixture distributions.
problem Sampling from multimodal distributions with simulated tempering.
method Introduced a decomposition theorem for the restricted spectral gap of simulated tempering.
result Lower bound on the restricted spectral gap for mixture distributions.
Enhances gradient-based discrete samplers with parallel tempering for multimodal distributions.
problem Local minima in high-dimensional, multimodal discrete distributions.
method Combines parallel tempering with discrete Langevin proposal, using Metropolis criterion for swaps.
result Significantly faster mixing and better sampling from complex distributions.
Polynomial mixing times for simulated tempering in mixture sampling problems.
problem Sampling from mixtures of log-concave distributions with location shifts.
method Conductance decomposition applied to an auxiliary Markov chain on an augmented space.
result First polynomial-time guarantee for simulated tempering with MALA.
New method improves sampling for Bayesian learning on complex distributions.
problem Efficient sampling from multimodal distributions and noisy data.
method Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo.
result Significantly improves performance over baselines on real datasets.
An infinite parallel tempering bouncy particle sampler improves sampling efficiency for multimodal distributions.
problem Sampling from complex posterior distributions with high accuracy and efficiency.
method Introduced an infinite parallel tempering bouncy particle sampler (BPS-PT) to accelerate convergence.
result Demonstrated improved sampling efficiency for multimodal distributions through numerical simulations.
The paper improves importance sampling and MCMC methods for complex distributions.
problem Improving sampling efficiency for distributions with atoms or heavy tails.
method Develops minimax optimal trial distributions and importance-tempered MCMC.
result Importance-tempered MCMC can be uniformly ergodic for certain distributions.
Improved lower bound for parallel tempering's mixing time.
problem Slow convergence and mixing in multimodal target distributions.
method Presented a new lower bound for the spectral gap of parallel tempering.
result Improved the best existing bound on spectral gap with polynomial dependence on parameters.
PTSD improves neural samplers by combining diffusion models and PT, enhancing efficiency.
problem Efficiency and correlation issues in neural samplers compared to PT.
method Sequential training of diffusion models across temperatures, combining high-temperature models for approximate lower-temperature samples.
result Significantly improved target evaluation efficiency, outperforming diffusion-based samplers.
New method accelerates Parallel Tempering using neural samplers.
problem Challenges in sampling from high-dimensional, multimodal distributions.
method Leverages neural samplers to reduce overlap between distributions.
result Improves sample quality and reduces computational cost.
Improved PDMP samplers for multi-modal distributions using tempering.
problem Struggles of PDMP samplers with multi-modal or heavy-tailed distributions.
method Tempering PDMPs by interpolating between a tractable and posterior distribution.
result PDMP samplers can be improved to sample from multi-modal distributions.
Improves GAN training stability and quality through a tempered learning process.
problem Training instability and low quality samples in GANs.
method Integrates a 'tempering' module that controls the real data distribution, balancing generator and discriminator.
result Improves quality, stability, and convergence speed across various GAN architectures.
Minimum Description Length prevents overfitting in noisy data.
problem Learning from noisy data with overfitting risk.
method Minimum Description Length learning rule with tempered guarantees.
result Tempered agnostic finite sample learning guarantees and asymptotic behavior characterization.
New method uses neural networks to efficiently approximate Bayesian inference for complex models.
problem Efficiently approximating Bayesian inference for complex models with varying temperatures.
method Fully amortized neural posterior estimator trained on a single forward pass.
result Achieves competitive posterior approximations across various temperatures and benchmarks.
New sampler tackles noisy posterior sampling with multiple modes.
problem Efficient sampling from complex posterior distributions with multiple isolated modes.
method Integrates parallel tempering with Nosé-Hoover dynamics for stochastic gradient.
result Efficiently draws representative samples from noisy posterior distributions.
New method estimates partition functions for complex distributions.
problem Computing partition functions for complex and multimodal distributions.
method Rao-Blackwellized Tempered Sampling
result Empirically more accurate than Annealed Importance Sampling.
New method improves sampling from multi-modal distributions using Langevin diffusion and simulated tempering.
problem Sampling from multi-modal distributions is challenging and slow.
method Combining Langevin diffusion with simulated tempering.
result The new method mixes more rapidly and samples from distributions close to mixtures of gaussians.
Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.
problem Rare events and metastable trapping in sampling physical systems with rough energy landscapes.
method Introduces Jeffreys Flow, a robust generative framework using Parallel Tempering distillation and symmetric Jeffreys divergence to mitigate mode collapse and improve mode coverage.
result Minimizing Jeffreys divergence suppresses mode collapse and corrects inaccuracies in multi-modal distributions.
The paper optimizes portfolios using a new GARCH model with regime switching and tempered stable innovations.
problem Mitigating left tail risk in multi-asset portfolios.
method Proposes a Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) for portfolio optimization.
result Optimal portfolios with tail risk measures outperform standard deviation-based portfolios and equally weighted portfolios in various performance metrics.
Kernel Quadrature improves numerical integration with adaptive tempering.
problem Optimizing sampling distribution for Kernel Quadrature to reduce integration error.
method Adaptive tempering and sequential Monte Carlo approach to find optimal sampling distribution.
result Significant reduction in integration error (up to 4 orders of magnitude) achieved with the proposed method.
Improves Bayesian neural learning efficiency with surrogate-assisted parallel tempering.
problem Challenges in Bayesian neural learning due to large models and data.
method Combines parallel tempering MCMC with surrogate-assisted optimization for computationally expensive models.
result Significantly lowers computational cost while maintaining quality in decision making.
Researchers study the geometric properties of a specific type of stable processes.
problem Understanding the information geometry of tempered stable processes.
method Derivation of α-divergence, Fisher information matrices, and α-connections.
result Obtained Fisher information matrices and α-connections for statistical manifolds.
TGD improves conditional sampling by concentrating computation on promising trajectories.
problem Efficiently training-free conditional sampling with diffusion priors.
method Tempered Guided Diffusion (TGD) using annealed sequential Monte Carlo.
result TGD yields a consistent particle approximation to the posterior as the number of particles grows.
Investigates tempered stable distributions and processes, including density transformations and parameter estimation.
problem Understanding the properties and applications of tempered stable distributions and processes.
method Analysis of limit distributions, parameter estimation, density transformations, and computation of p-variation indices. result Computed p-variation indices for tempered stable processes and discussed exponential stock models driven by these processes. 2D-PT improves sampling in constrained optimization problems.
problem Sampling Boltzmann distributions with soft constraints.
method Two-dimensional extension of parallel tempering.
result 2D-PT achieves near-ideal mixing in constrained problems.
FlowVAT improves variational inference for multi-modal distributions.
problem Mode-seeking behavior and collapse in variational inference for complex posteriors.
method Conditional tempering approach for normalizing flow variational inference.
result FlowVAT outperforms traditional and adaptive annealing methods in multi-modal distributions, finding more modes and achieving better ELBO values.
Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work w…
We introduce a new distance metric for non-linear embeddings of Tempered Exponential Measures.
problem Non-linear embeddings of Tempered Exponential Measures (TEMs).
method Parameterization of finite discrete TEMs via Legendre functions, introducing tempered Hilbert co-simplex distance.
result Established a generalization of the Hilbert log cross-ratio simplex distance to a tempered Hilbert co-simplex distance.
This paper proposes a novel approach to train deep neural networks using Langevin dynamics with continuous tempering.
problem Training deep neural networks is challenging due to non-convex and high-dimensional objective functions.
method The approach divides the training process into two phases: Bayesian sampling and stochastic optimization. Continuous tempering and Langevin dynamics are applied to the Bayesian learning phase.
result The method achieves remarkable improvements in various types of neural networks, overcoming the challenge of early trapping into bad local minima.
A new method uses surrogate models to speed up landscape evolution model inference.
problem Complex and computationally expensive landscape evolution models.
method Surrogate-assisted parallel tempering for Bayesian inversion.
result Significant reduction in computational cost with preserved solution quality.
A definition for elliptical tempered stable distribution, based on the characteristic function, have been explained which involve a unique spectral measure. This definition provides a framework for creating a connection between infinite divisible distribution, and particularly elliptical tempered stable distribution, w…
Improved sampling from multi-modal distributions using simulated tempering and Langevin Monte Carlo.
problem Sampling from multi-modal distributions with multiple modes is difficult.
method Combining Langevin diffusion with simulated tempering to improve mixing.
result The combined method mixes more rapidly than Langevin diffusion alone.
Improved model-based estimation through tempered Bayes filter.
problem Improving predictive accuracy in partially-observable stochastic systems.
method Developed tempered Bayes filter combining likelihood and full posterior tempering.
result Tempered Bayes filter achieves improved predictive performance over the Bayes filter baseline.
This work explains how tempering improves Bayesian neural networks by reducing the impact of data augmentation.
problem Improper sharpening of Bayesian neural networks leads to suboptimal performance.
method Theoretical analysis and empirical evaluations of simplified settings and group convolutions.
result Tempering reduces the misspecification due to modeling augmentations as independent and identically distributed (i.i.d.) data.
Paper improves Bayesian neural learning efficiency and uncertainty quantification.
problem Challenges in convergence and scalability of MCMC techniques for Bayesian neural learning.
method Parallel tempering and Langevin-gradient information in Metropolis-Hastings proposals.
result Improves computational time and prediction/decision-making capabilities.
Defines Schwartz and tempered functions on o-minimal manifolds.
problem Defining Schwartz and tempered functions on non-polynomially bounded o-minimal manifolds.
method Defining Schwartz and tempered functions on manifolds definable in polynomially bounded o-minimal structures, and showing classical properties hold.
result The theory of Schwartz and tempered functions can be constructed on manifolds definable in polynomially bounded o-minimal structures but not on non-polynomially bounded ones.
Bayesian inference improved with classifier-based misspecification detection and tempering.
problem Model misspecification in Bayesian inference leads to overly concentrated posteriors.
method Probabilistic classifiers trained on simulated vs. observed data to estimate model misspecification and tempering level.
result Estimation of negative KL divergence provides useful diagnostic and update method.
Enhances sampling for complex hidden Markov models using ensemble MCMC.
problem Challenges in Bayesian inference for factorial hidden Markov models due to large latent variable space.
method Introduces ensemble MCMC with parallel tempering and genetic algorithm for efficient exploration.
result Improves sampling efficiency and mixing of existing samplers in various applications.
A new method called TemperFlow tackles multimodality in sampling from unnormalized distributions.
problem Sampling from unnormalized distributions with isolated modes.
method TemperFlow learns a sequence of tempered distributions to progressively approach the target distribution.
result TemperFlow overcomes the limitations of existing methods and achieves superior performance.
Accumulated stock returns exhibit tempered skew t-distribution.
problem Analyzing the distribution of stock returns over multiple days.
method Employing a tempered skew t-distribution model.
result Tempered skew t-distribution fits the distribution of accumulated stock returns well.
We offer new formulas for European option pricing under tempered stable processes.
problem Pricing European options under tempered stable processes.
method Series expansions for tempered stable densities and European option prices.
result Our formulas are hyperparameter-free and competitive with traditional methods.
New findings on how neural networks generalize with varying dimensions.
problem Understanding how neural networks generalize with different dimensions and noise levels.
method Study of 2-layer ReLU NNs in a classification setting, proving transitions in overfitting types.
result The type of overfitting transitions from tempered to benign as input dimension increases.
New financial models use tempered stable subordination for better correlation dynamics.
problem Building financial models with better correlation dynamics.
method Introducing tempered stable Sato subordinators and additive inhomogeneous processes.
result The new process has time-dependent correlation, improving fit for financial data.