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 article introduces inferential moments for analyzing uncertain multivariable systems.
New categorization of community detection methods to avoid pitfalls.
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…
Chiseling finds valid subgroups interactively, improving on existing methods.
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.
Novel framework for Bayesian reinforcement learning infers value function distributions.
This paper explores using SSIM for better image generation in generative models.
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…
New DR method improves robustness in high-dimensional treatment effects.
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…
New method improves model explainability.
New approach to topic modelling with covariates for large text corpora.
The paper explores how invertibility affects the complexity of encoder models in VAEs.
iWGAN improves GANs by stabilizing training and preventing mode collapse.
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…
The paper improves Lasso inference methods for survey data.
Paper develops methods for statistical inference with SGD in nonconvex optimization.
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…
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 …
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…
Breiman's paper sparked debate on the future of statistics and machine learning.
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an ever-increasing volume of work on SGD, much less is known about the statistical inferential properties of SGD-based predictions. Taking a fu…
Proposes a semi-Bayesian nonparametric estimator for MMD in GOF tests and GANs.
Simple method for estimating missing panel data entries with confidence intervals.
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…
Randomized gradient-based ensemble improves prediction accuracy.
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.…
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new gene…
Paper examines LLM capability benchmarks through construct validity, favoring nomological account.
Post-ADC inference corrects bias in statistical inference after active data collection.
New method compares community detection algorithms without ground truth.
New synthetic data analysis reveals high type 1 error rates.
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
UCB algorithm provides stable sample means for sequential data.
Bayesian DL model improves DCMs for better predictive and inferential performance.
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. …
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distribution in a Bayesian model allows one to make inferential statements about potentially observable random quantities given observed data. Th…
The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vas…
Profile graphical models represent multivariate dependence under varying risk factors.
FNNs can be made more interpretable with statistical methods.
Bayesian uncertainty quantification is flawed, according to new research.
Paper introduces ML for rare-event prediction in patent quality estimation.
How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of s…
Paper introduces a new robust method for estimating Pareto tail index from grouped data.
This work improves mixing rates for Bayesian CART, a key component of BART.
The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in optimizing a utility function over a fixed period of time (finite horizon). In this manuscript, we develop an inferential procedure based on temporal difference residuals for …
This paper develops embeddings that preserve likelihood-based statistical inference.