A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new EVI framework improves ParVI methods by maintaining variational structure and reducing KL-divergence.
Survey of methods for solving smooth stochastic variational inequalities.
A new method combines Laplace and Variational Bayes for scalable inference.
Newton's method solves variational problems on manifolds.
New method accelerates energetic variational inference using particle dynamics.
New method reduces inference variance for faster optimization.
Derives new optimization methods using variational integrators.
Variational inference methods for latent variable statistical models have gained popularity because they are relatively fast, can handle large data sets, and have deterministic convergence guarantees. However, in practice it is unclear whether the fixed point identified by the variational inference algorithm is a local…
Improved sampling method using regularized Stein Variational Gradient Flow.
Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. …
Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis, and these efforts can hinder and deter us from quickly developing and exploring a variety of mod…
A practical guide to Variational Bayes methods.
FlowVAT improves variational inference for multi-modal distributions.
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) in…
We introduce a new algorithm for approximate inference that combines reparametrization, Markov chain Monte Carlo and variational methods. We construct a very flexible implicit variational distribution synthesized by an arbitrary Markov chain Monte Carlo operation and a deterministic transformation that can be optimized…
This paper introduces the variational Rényi bound (VR) that extends traditional variational inference to Rényi's alpha-divergences. This new family of variational methods unifies a number of existing approaches, and enables a smooth interpolation from the evidence lower-bound to the log (marginal) likelihood that is co…
This work improves VAEs using MCMC methods for better variational bounds.
Improved Bayesian uncertainty quantification using variational bagging.
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trad…
Fast variational Bayes methods improve geospatial data analysis speed and accuracy.
Semi-Implicit Variational Inference (SIVI) is improved with SIVI-SM using score matching.
This paper develops scalable control variates for Monte Carlo methods using stochastic optimization.
Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily …
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and cons…
New method improves variational inference for better posterior approximation.
A new variational method speeds up Bayesian phylogenetic inference.
Robustness to outliers is a central issue in real-world machine learning applications. While replacing a model to a heavy-tailed one (e.g., from Gaussian to Student-t) is a standard approach for robustification, it can only be applied to simple models. In this paper, based on Zellner's optimization and variational form…
Constructs surfaces with conical singularities using variational methods.
New method estimates volatility for processes with jumps of unbounded variation.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
This work proposes a new method for variational inference using Wasserstein gradient descent.
Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes to existing code-bases. In this paper, we propose Vprop, a method for Gaussian variational inference that can be implemented with two minor c…
Bayesian method improves SOM training for dynamic data.
Develops a variational method for ultrametric phylogenetic trees.
The paper studies stability of discrete planar curves using variational methods.
New clustering algorithm for time series data using RNN and variational Bayes.
New framework improves stochastic optimization for variational inference.
Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is largely limited by …
New definition of disentanglement for non-independent factors of variation.
Paper proposes a method to improve variational inference for sparse networks.
A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparabl…
Two popular classes of methods for approximate inference are Markov chain Monte Carlo (MCMC) and variational inference. MCMC tends to be accurate if run for a long enough time, while variational inference tends to give better approximations at shorter time horizons. However, the amount of time needed for MCMC to exceed…
New method improves inference for hierarchical models.
Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable proba- bility density functions. This largely limits the design and implementation of v…
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we introduce automatic structured variational inference (ASVI), a fully au…
A new method solves variational inequality problems with multiple constraints without needing optimal Lagrange multipliers.
New method for variational inference without conjugacy constraints.
Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables. This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more s…