arXiv research

The article introduces inferential moments for analyzing uncertain multivariable systems.

This paper proposes a unified framework to quantify local and global inferential uncertainty for high dimensional nonparanormal graphical models. In particular, we consider the problems of testing the presence of a single edge and constructing a uniform confidence subgraph. Due to the presence of unknown marginal trans…

Bayesian reinforcement learning (BRL) offers a decision-theoretic solution for reinforcement learning. While "model-based" BRL algorithms have focused either on maintaining a posterior distribution on models or value functions and combining this with approximate dynamic programming or tree search, previous Bayesian "mo…

Chiseling finds valid subgroups interactively, improving on existing methods.

iWGAN improves GANs by stabilizing training and preventing mode collapse.

We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models. This framework addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneous multitask learning. Our work has three main …

Paper examines LLM capability benchmarks through construct validity, favoring nomological account.

New categorization of community detection methods to avoid pitfalls.

With the proliferation of mobile devices and the internet of things, developing principled solutions for privacy in time series applications has become increasingly important. While differential privacy is the gold standard for database privacy, many time series applications require a different kind of guarantee, and a…

Study trade-offs between statistical and computational efficiency in variational inference.

This paper explores using SSIM for better image generation in generative models.

In this paper we propose a flexible and efficient framework for handling multi-armed bandits, combining sequential Monte Carlo algorithms with hierarchical Bayesian modeling techniques. The framework naturally encompasses restless bandits, contextual bandits, and other bandit variants under a single inferential model. …

We consider the problem of undirected graphical model inference. In many applications, instead of perfectly recovering the unknown graph structure, a more realistic goal is to infer some graph invariants (e.g., the maximum degree, the number of connected subgraphs, the number of isolated nodes). In this paper, we propo…

Post-ADC inference corrects bias in statistical inference after active data collection.

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using model point estimates to represent individual data items, we employ posterior distributions over models, thus taking into account in a princi…

Using first principles from inference, we design a set of functionals for the purposes of \textit{ranking} joint probability distributions with respect to their correlations. Starting with a general functional, we impose its desired behaviour through the \textit{Principle of Constant Correlations} (PCC), which constrai…

In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing valu…

The paper proposes a method to test properties of the optimal assortment in multinomial logit models.

Framework handles both exchangeable and non-exchangeable event sequences without tuning.

Modeling continuous movement of entities in latent space for interaction timing.

In high-dimensional linear models, the sparsity assumption is typically made, stating that most of the parameters are equal to zero. Under the sparsity assumption, estimation and, recently, inference have been well studied. However, in practice, sparsity assumption is not checkable and more importantly is often violate…

Measures dependence between two systems using Bayesian model comparison.

This paper develops embeddings that preserve likelihood-based statistical inference.

Framework assesses variable importance for heterogeneous treatment effects.

New approaches improve uncertainty quantification in autoregressive models for sequence data.

New tools connect CP to GF inference for better probabilistic prediction.

The paper explores how invertibility affects the complexity of encoder models in VAEs.

Proposes a semi-Bayesian nonparametric estimator for MMD in GOF tests and GANs.

The article compares predictor importance in classification problems with categorical outcomes.

PAIR-CI calibrates CI tests for causal discovery with incomplete data.

Breiman's paper sparked debate on the future of statistics and machine learning.

This paper defines systematic value investing as an empirical optimization problem. Predictive modeling is introduced as a systematic value investing methodology with dynamic and optimization features. A predictive modeling process is demonstrated using financial metrics from Gray & Carlisle and Buffett & Clark. A 31-y…

We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with crossed random effects. In the most general situation, where the dimensions of the crossed groups are arbitrarily large, streamlining is hindered by lack of sparseness in the underlying least squares system. Because of th…

Enhances neural processes for better context handling.

Profile graphical models represent multivariate dependence under varying risk factors.

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estima…

We propose a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations. We construct an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale…

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both tr…

New synthetic data analysis reveals high type 1 error rates.

New DR method improves robustness in high-dimensional treatment effects.

New method compares community detection algorithms without ground truth.

We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension $p$ can grow exponentially fast with the sample size $n$. Our method combines the de-biasing technique with the composite quantile function to construct an estimator that …

This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have $n$ repeated measurements for $M$ subjects. We consider a scenario where the number of fixed effects $p$ is large (and may be larger than $M$), but the n…

Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data col…

UCB algorithm provides stable sample means for sequential data.

Proposes a method to infer ranking properties and top-K rankings with uncertainty quantification.

New framework assesses LLMs' expertise using nonparametric ranking and confidence diagrams.

Unified framework for predicting data changes influenced by predictions.