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.
The paper provides theoretical guarantees for transformation-based models in variational inference.
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
This paper finds a unique partition of a sample space for estimating continuous distributions.
Estimates neural drift for stochastic equations, improving inference on noisy data.
A novel MCMC method clusters data faster and more accurately.
Bayesian non-parametric model adapts to concept drifts in streaming data.
Proposes a non-parametric method for deep discrete latent variable models.
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
The paper reviews methods for estimating individual treatment effects using non-parametric regression models.
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cover. Infere…
Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixtur…
This work develops a non-parametric test for relational independence in non-i.i.d. data.
The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models …
This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.
Bayesian econometrics improves nowcasting during pandemics.
Estimates conditional Brenier maps using entropic optimal transport.
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hi…
New method for estimating counterfactual means in adaptive experiments.
Near-optimal tests and confidence sequences for non-parametric data.
New method speeds up SDE inference by matching moments to FPK equation.
Study evaluates policies in partially observable environments without full model specification.
PClean automates Bayesian data cleaning for specific datasets.
Estimates classifier errors without ground truth using algebraic geometry.
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of…
The paper proposes a new evaluation framework for causal inference models.
We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension of the conditioning variable is larger than the sample size , estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic…
KSD Thinning uses KSD to thin MCMC samples efficiently.
Elliptical processes extend Gaussian models with heavier tails.
We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through u…
Bayesian non-parametric model selects latent dimensions automatically.
Bayesian graph learning improves graph representation accuracy.
A new method speeds up DPMM inference for federated learning.
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite their impressive performance, the techniques have a limited capability to incorporate the uncertaint…
We address challenges in estimating parameters from adaptively collected data.
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allo…
Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the …
Method recovers causal diffusion mechanisms from steady-state data without parametric assumptions.
A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal var…
Bayesian inference using stochastic neural networks ensembles.
A method detects changes in heterogeneous data streams over graph nodes.
Generative dynamic texture models (GDTMs) are widely used for dynamic texture (DT) segmentation in the video sequences. GDTMs represent DTs as a set of linear dynamical systems (LDSs). A major limitation of these models concerns the automatic selection of a proper number of DTs. Dirichlet process mixture (DPM) models w…
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest. The key object that defines an ExGM is often referred to as a graphon. This non-parametric perspective on network modeling poses challenging questions on how to make inference on the graphon und…
The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …
Paper proposes a machine learning-based method for estimating mediation effects.
Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we presen…
Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. In streaming contexts, learning must also …
Neural networks estimate SDEs with jump noise using a Tamed-Milstein scheme.
Efficiently models event-based data with general parametric kernels.