arXiv research

Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.

The paper uses moment matching method for pricing spread options under Lévy models.

Maximum likelihood learning with exponential families leads to moment-matching of the sufficient statistics, a classic result. This can be generalized to conditional exponential families and/or when there are hidden data. This document gives a first-principles explanation of these generalized moment-matching conditions…

IMM generates high-quality samples in few steps with stable training.

Framework generates causal probabilities from observational data.

Improves Bayesian optimization for multi-fidelity functions.

We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techni…

New SQ lower bounds for NGCA without requiring chi-squared condition.

New approach for testable learning using moment matching and Rademacher complexity.

New technique reduces imitation learning performance gap in finite samples.

Develops efficient methods for approximating densities of financial models with jumps.

Q-MMR evaluates policies using reweighted rewards and moment matching.

Paper introduces new approximations for lognormal sums, matching comonotonicity and moments.

New method improves sampling from noisy energy models.

Optimal SQ bounds for learning binary product distributions and Ising models.

SkMM selects data for finetuning by balancing bias and variance.

We introduce three novel semi-parametric extensions of probabilistic canonical correlation analysis with identifiability guarantees. We consider moment matching techniques for estimation in these models. For that, by drawing explicit links between the new models and a discrete version of independent component analysis …

A new method approximates option pricing in stochastic interest rate markets.

Completely random measures (CRM) represent the key building block of a wide variety of popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. A popular representation of CRMs as a random series with decreasing jumps is due to Ferguson and Klass (1972). This can immediately be turned into a…

Unified framework for imitation learning via moment matching.

New method distills discrete diffusion models, maintaining quality and diversity.

Expectation propagation (EP) is a powerful approximate inference algorithm. However, a critical barrier in applying EP is that the moment matching in message updates can be intractable. Handcrafting approximations is usually tricky, and lacks generalizability. Importance sampling is very expensive. While Laplace propag…

This paper compares VaR estimation methods under tail misspecification, finding importance sampling underestimates VaR.

A new method for generating samples without training, using smoothed score matching.

Unified framework for FDR control in knockoffs, validating Gaussian knockoffs.

Paper identifies tensor ranks via prior predictive matching, solving system of equations.

Bayesian learning is often hampered by large computational expense. As a powerful generalization of popular belief propagation, expectation propagation (EP) efficiently approximates the exact Bayesian computation. Nevertheless, EP can be sensitive to outliers and suffer from divergence for difficult cases. To address t…

GMMNs model cross-sectional dependence for better option pricing and simulation.

Models with many signals, high-dimensional models, often impose structures on the signal strengths. The common assumption is that only a few signals are strong and most of the signals are zero or close (collectively) to zero. However, such a requirement might not be valid in many real-life applications. In this article…

Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performanc…

Paper improves basket option pricing for log-normal models.

Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.

Conditional DGP learns effective kernels from low-fidelity data.

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…

This work develops efficient methods for computing moments of Gaussian mixtures.

The Gaussian mixture model is a classic technique for clustering and data modeling that is used in numerous applications. With the rise of big data, there is a need for parameter estimation techniques that can handle streaming data and distribute the computation over several processors. While online variants of the Exp…

A2-SBNN models spatial data with copulas for non-Gaussian dependencies.

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much l…

Unified framework for distribution shift estimation, explanation, and improvement.

The paper examines Nash equilibrium in GANs for stationary Gaussian processes.

Efficient algorithm for learning halfspaces in a new model with polynomial time complexity.

Proposes efficient Gaussian approximations for non-Gaussian likelihoods.

Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of the unknown data generating density. This paper contributes to the mathematical understanding of this phenomenon and helps define better justified sampling algorithms for deep learning based on auto-encoder v…

The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses we introduce variational characterizations that naturally suggest a two-step scheme for their optimizatio…

Develops a deterministic method to approximate NSDEs for better uncertainty quantification.

We provide a framework to approximate the 2-Wasserstein distance and the optimal transport map, amenable to efficient training as well as statistical and geometric analysis. With the quadratic cost and considering the Kantorovich dual form of the optimal transportation problem, the Brenier theorem states that the optim…

ELSA efficiently adapts to label shift without post-prediction calibrations.

Unified view of label shift estimation methods.