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.
Sparse GPs improved with nearest neighbor inducing variables.
Bayesian approach improves performance in Gaussian process models.
New method speeds up sparse Gaussian processes for large datasets.
The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.
Functional data analysis involves data described by regular functions rather than by a finite number of real valued variables. While some robust data analysis methods can be applied directly to the very high dimensional vectors obtained from a fine grid sampling of functional data, all methods benefit from a prior simp…
Study feature representations induced by dependence between variables.
Simple conditions for comonotonic additive risk measures from acceptance sets.
In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the e…
Rule-based classifiers quantify uncertainty using Bernoulli random variables.
We introduce and compare new variability measures based on risk quantiles.
We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsity-inducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual -norm and the group -norm by allowing the subsets to overlap. T…
Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.
ContrastiveVI+ models CRISPR screens with noisy guide efficiency.
The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.
New RL environments help AI learn causal relationships from visual data.
Paper tightens variational GP approximations for large datasets.
Efficient SGPRN model for imputation and visualization of missing data.
We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a representation-based approach (Artetxe et al., 2017). To train the model, we derive an efficient Viterbi EM algorithm. We provide empiric…
Kernel method improves instrumental variable regression rates.
For a complex polynomial in two variables we study the morphism induced in homology by the embedding of an irregular fiber in a regular neighborhood of it. We give necessary and sufficient conditions for this morphism to be injective, surjective. Particularly this morphism is an isomorphism if and only if the correspon…
Improves robustness of information bottleneck framework with sparsity-inducing prior.
Paper tackles causal effect identification in sub-population with latent variables.
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representation trick over the inducing variables called subspace inducing inputs. This is combined with certain m…
DSVNP uses global and local latent variables for improved neural process predictions.
Variable selection plays an important role in the high-dimensional data analysis. However the high-dimensional data often induces the strongly correlated variables problem. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. By a simulation study, we s…
In this paper, the estimation problem for sparse reduced rank regression (SRRR) model is considered. The SRRR model is widely used for dimension reduction and variable selection with applications in signal processing, econometrics, etc. The problem is formulated to minimize the least squares loss with a sparsity-induci…
DICCA maps multi-view data into a shared latent space with interpretable components.
New method for fitting graphical models with latent variables using regularized conditional likelihood.
New method for efficient Bayesian inference in GPSSMs.
We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification. State-of-the-art (stochastic) inference for Gaussian processes on very large datasets scales cubically in the number of 'inducing inputs', variables introduced to factorise the mo…
Concept modulation models unify identifiability and extrapolation in conditional latent variable models.
The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.
New method DDVI improves posterior inference for deep Gaussian processes.
Geometric phases describe how in a continuous-time dynamical system the displacement of a variable (called phase variable) can be related to other variables (shape variables) undergoing a cyclic motion, according to an area rule. The aim of this paper is to show that geometric phases can exist also for discrete-time sy…
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete late…
NICE learns a representation to avoid bad controls in causal inference.
In this paper, we extend the T-duality Hori maps in [arXiv:hep-th/0306062], inducing isomorphisms of twisted cohomologies on T-dual circle bundles, to graded Hori maps and show that they induce isomorphisms of two-variable series of twisted cohomologies on the T-dual circle bundles, preserving Jacobi form properties. T…
Ising models describe the joint probability distribution of a vector of binary feature variables. Typically, not all the variables interact with each other and one is interested in learning the presumably sparse network structure of the interacting variables. However, in the presence of latent variables, the convention…
This paper is about variable selection, clustering and estimation in an unsupervised high-dimensional setting. Our approach is based on fitting constrained Gaussian mixture models, where we learn the number of clusters and the set of relevant variables using a generalized Bayesian posterior with a sparsity indu…
We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model …
Variable selection is a fundamental task in statistical data analysis. Sparsity-inducing regularization methods are a popular class of methods that simultaneously perform variable selection and model estimation. The central problem is a quadratic optimization problem with an l0-norm penalty. Exactly enforcing the l0-no…
A new method normalizes activations to match batch normalization without batch dependence.
In this article we consider means of positive operators on a Hilbert space. We extend the theory of matrix power means to arbitrary operator means in the sense of Kubo-Ando. The basis of the extension is relying on ideas coming from differential geometry. We consider generalized Karcher equations for positive operators…
Jigsaw-VAE tackles feature imbalance in VAE latent variables, improving generalization across environments.
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises. Our …
The sequence of moments of a vector-valued random variable can characterize its law. We study the analogous problem for path-valued random variables, that is stochastic processes, by using so-called robust signature moments. This allows us to derive a metric of maximum mean discrepancy type for laws of stochastic proce…
Canonical gravity can be formulated by means of a densitized dreibein together with an SU(2) connection. These so-called Ashtekar variables are the fundamental quantities, loop quantum gravity is resting on. In this paper we review these variables from the perspective of fibre bundles. This is straightforward for the d…
Develops a new method for nonlinear dimension reduction using random features.