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 optimization algorithm improves convergence in unconstrained problems.
Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…
We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari- ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally determini…
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged b…
Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower Bound (ELBO) of the true data likelihood with respect to a variational distribution. However, freely enriching the family of variational distribution is challenging since…
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation method for likelihood estimation.
DeepONet learns operators for PDEs with varying parameters and initial conditions.
EXOC framework uses auxiliary variables for counterfactual fairness in machine learning.
Paper models graph edge dependencies using latent variables for community detection.
Paper extends ICA to ISA with auxiliary variables for better speech representation learning.
Study improves interpretability in generative models by disentangling latent variables in scientific datasets.
New MCMC methods use auxiliary variables to sample from intractable distributions.
We introduce a new family of MCMC samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalisation over the auxiliary variables yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well…
New formulas derived for scalar curvature in generalized Ricci flow.
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costly to evaluate directly. It operates by introducing additional auxiliary variables into the model and form an extended target distribution, w…
This note compares two recently published machine learning methods for constructing flexible, but tractable families of variational hidden-variable posteriors. The first method, called "hierarchical variational models" enriches the inference model with an extra variable, while the other, called "auxiliary deep generati…
Bayesian deep learning improves geostatistical mapping with auxiliary data.
Generative model disentangles dark matter halo properties.
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes. This formulation allows us to design efficient simulations for Hawkes processes with differing exponentially decaying intensities. We demonstrate that inter-arrival times can be decomposed into simpler…
Paper defends diffusion models from membership inference attacks using Langevin dynamics.
Bayesian models that mix multiple Dirichlet prior parameters, called Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring mixing weights and parameters of mixed prior distributions seems tricky, as sums over Dirichlet parameters complicate the joint distribution of model parameters. This paper s…
In this paper, we propose a new algorithm to speed-up the convergence of accelerated proximal gradient (APG) methods. In order to minimize a convex function , our algorithm introduces a simple line search step after each proximal gradient step in APG so that a biconvex function is minimi…
The Cartan equivalence method is utilized to deduce an invariant characterization of the scalar third-order ordinary differential equation which admits the maximal seven-dimensional point symmetry Lie algebra. The method provides auxiliary functions which can be used to efficiently obtain the point t…
Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., identifiability). Recently, the very first identifiability proofs for nonlinear ICA have been proposed, leveraging the temporal structure of the…
VAV method optimizes learning rate for faster, stable SGD convergence.
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
New method identifies latent sources from nonlinear mixtures without auxiliary variables.
New divergence identity for scalar curvature helps prove rigidity of tensors.
New framework IIA identifies innovations in general nonlinear vector autoregressive processes.
Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, e…
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using sparsity optimization…
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
LDF combines neural networks with probabilistic models for data fusion.
New method improves understanding of machine learning model performance.
Paper proposes a transfer learning framework for tensor Gaussian graphical models.
SCC clusters data with supervising variables for better interpretation.
New algorithm samples neural network posteriors efficiently.
New MCMC method speeds up quantum physics simulations by a factor of 100.
Study market-to-book ratios using Stochastic Portfolio Theory.
Random forests and LASSO methods improve small area estimation using auxiliary data.
Optimizes decision-making with uncertain variables using auxiliary observations.
The paper reviews identifiability in linear and nonlinear models, from Gaussian to non-Gaussian.
New method disentangles latent variables in nonstationary data.
New method combines domain changes and sparse mixing for better latent variable learning.
Many popular first-order optimization methods (e.g., Momentum, AdaGrad, Adam) accelerate the convergence rate of deep learning models. However, these algorithms require auxiliary parameters, which cost additional memory proportional to the number of parameters in the model. The problem is becoming more severe as deep l…
We study the graded geometric point of view of curvature and torsion of Q-manifolds (differential graded manifolds). In particular, we get a natural graded geometric definition of Courant algebroid curvature and torsion, which correctly restrict to Dirac structures. Depending on an auxiliary affine connection K, we int…
Kernel methods identify treatment effects with unobserved confounding using negative controls.
Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently pr…