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.
Unified approach for predicting missing segments in partially observed functions.
New approach to handle ranking function variation in zero-shot NAS.
New method approximates diffusion process posteriors using moment functions.
The calculus of variations for lagrangians which are not functions on the tangent bundle, but sections certain affine bundles is developed. We follow a general approach to variational principles which admits boundary terms of variations.
In this short paper, we re-derive the Bochner formula for the Laplacian by considering local variations of volume. The derivation is rooted in the fact that the Laplacian of a function measures the volume variation along the flow of the gradient vector of the function. Possible extensions of this approach/technique are…
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning. It has been applied to approximate the maximum likelihood estimator and to carry out Bayesian inference, however, quantification of uncertainty with vari…
New method tightens variational representations of divergences for faster learning.
A new meta-learning method using shared variational inference.
Minimal submanifolds are found as energy concentration sets in variational problems.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
Neural networks solve variational inequalities for optimal stopping problems.
In Bayesian machine learning, the posterior distribution is typically computationally intractable, hence variational inference is often required. In this approach, an evidence lower bound on the log likelihood of data is maximized during training. Variational Autoencoders (VAE) are one important example where variation…
Gradient-based optimization improves variational empirical Bayes regression.
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based …
Solves wealth maximization problem using variational analysis.
Optimizes MCMC chains with neural control variates.
We propose a simple and general variant of the standard reparameterized gradient estimator for the variational evidence lower bound. Specifically, we remove a part of the total derivative with respect to the variational parameters that corresponds to the score function. Removing this term produces an unbiased gradient …
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…
Derives FPDE for equity-linked insurance pricing.
FTIP uses normalizing flows to improve posterior inference in function space.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
EigenVI uses orthogonal function expansions for efficient variational inference.
VES-Gamma adapts EI using information-theoretic principles.
We describe and analyze some novel approaches for studying the dynamics of Ising spin glass models. We first briefly consider the variational approach based on minimizing the Kullback-Leibler divergence between independent trajectories and the real ones and note that this approach only coincides with the mean field equ…
NeuralFLoC unifies registration and clustering of functional data, overcoming phase variation challenges.
We propose a novel hierarchical model for multitask bipartite ranking. The proposed approach combines a matrix-variate Gaussian process with a generative model for task-wise bipartite ranking. In addition, we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior m…
NVGD uses neural networks to infer distributions without kernel choices.
A new method solves variational inequality problems with multiple constraints without needing optimal Lagrange multipliers.
Stein variational neural network ensembles improve diversity and uncertainty estimation.
We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring …
The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method. Although there is overwhelming empirical evidence in the literature showing its success, there is relative…
Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance fu…
New variational inference approach using Hilbert space for robotic state estimation.
Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose black-box learning and inference algorithms for undirected models that optimize a variational approximation to the log-likelihood of the model. Central to our approach is an upper bound on the log-…
Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian process models are capable of learning from large-scale data, standard strategies for sparsifying the model can prevent the approximation of …
Study finds loops with specific curvature exist using Hardy's inequality.
Generative ParVI learns flexible sampling from posterior distributions.
New method uses quantum annealing and VAN for better statistical mechanics calculations.
TADDAA improves accuracy diagnostics for variational approximations.
Minimal variations guide unsupervised learning for better downstream tasks.
Paper addresses variational inference issues in Bayesian neural networks.
New RL approach infers optimal policies via variational inference.
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so far has been to use a factorised posterior distribution, decoupling the transition function from the system states. This is not exact in gene…
Using Vovk's outer measure, which corresponds to a minimal superhedging price, the existence of quadratic variation is shown for "typical price paths" in the space of càdlàg functions possessing a mild restriction on the jumps directed downwards. In particular, this result includes the existence of quadratic variation …
New variational principle found for non-variational differential equations.
New approach to meta-learning with variational Bayes for unlabeled data.
Variational problems that involve Wasserstein distances have been recently proposed to summarize and learn from probability measures. Despite being conceptually simple, such problems are computationally challenging because they involve minimizing over quantities (Wasserstein distances) that are themselves hard to compu…
Time-variant value function transfer method for RL.