Chiseling finds valid subgroups interactively, improving on existing methods.
problem Finding valid subgroups with inferential guarantees in regression and causal inference.
method Interactive subgroup refinement with inferential validity guarantees.
result Chiseling identifies better subgroups than existing methods with inferential guarantees.
Paper examines LLM capability benchmarks through construct validity, favoring nomological account.
problem Linking theoretical capabilities to empirical measurements in LLMs.
method Contrasts three frameworks: nomological, inferential, and causal.
result Nomological account provides best foundation for LLM research.
The article introduces inferential moments for analyzing uncertain multivariable systems.
problem Handling inference tasks in uncertain multivariable systems.
method Bayesian inference and quantification of inferential moments.
result Quantification of inferential moments and their connection to mutual information.
Post-ADC inference corrects bias in statistical inference after active data collection.
problem Bias in inference after active data collection.
method Post-ADC inference framework that corrects bias from both ADC process and data-driven target construction.
result Valid inference for data collected by SMBO methods like GP-UCB and TPE.
Paper develops methods for statistical inference with SGD in nonconvex optimization.
problem Statistical inference for nonconvex optimization problems.
method Proposes two online inferential procedures combining SGD and bootstrap techniques.
result Establishes error convergence rates and asymptotically valid bootstrap confidence intervals.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis o…
Kernel ridge regression inference for nonstandard data.
problem Inferential theory for kernel ridge regression with nonstandard data.
method Constructs valid and sharp confidence sets using anti-symmetric multipliers.
result Develops a test for match effects in school matching mechanisms.
The paper improves Lasso inference methods for survey data.
problem Improving inference methods for survey data.
method Extends Lasso inferential methods to survey data.
result Establishes asymptotic validity of inference procedures in survey environments.
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…
PAIR-CI calibrates CI tests for causal discovery with incomplete data.
problem Miscalibration of CI tests when imputing incomplete data.
method Integrates multiple imputation directly into the inferential procedure via a paired permutation design.
result PAIR-CI reduces false positive rates to below 5% in simulations.
New categorization of community detection methods to avoid pitfalls.
problem Misuse of community detection methods in practice.
method Descriptive vs. inferential approaches.
result Inferential methods provide clearer insights into network formation.
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…
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.…
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…
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.
problem Optimizing statistical accuracy vs. computational efficiency in Bayesian inference.
method Case study on Gaussian inferential models with diagonal plus low-rank precision matrices, analyzing Bayesian posterior inference and frequentist uncertainty quantification errors.
result Lower-rank models reduce variance and accelerate convergence but increase posterior inference error.
Paper introduces ML for rare-event prediction in patent quality estimation.
problem Lack of predictive modeling in econ, management, tech forecasting.
method Introduces ML approach for optimizing predictive performance.
result Demonstrates synergy between ML and inferential statistics.
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…
Novel framework for Bayesian reinforcement learning infers value function distributions.
problem Bayesian reinforcement learning's challenges in inferring value function distributions.
method Inferential Induction framework for Bayesian reinforcement learning, developing Bayesian Backwards Induction algorithm.
result Proposed algorithm is competitive with state-of-the-art methods.
This paper explores using SSIM for better image generation in generative models.
problem Improving perceptual quality in generated images using ℓ2 norm. method Theoretical discussion and practical implementation of SSIM in generative models and autoencoders.
result SSIM can be used in generative models and autoencoders to generate better images.
Bayesian uncertainty quantification is flawed, according to new research.
problem Flawed interpretation of Bayesian uncertainty quantification.
method Discussion of Bayesian updating and optimization-based perspective, proposing measures of quality.
result Bayesian uncertainty quantification is not coherent with optimization-based perspective.
This paper develops embeddings that preserve likelihood-based statistical inference.
problem Modern machine learning embeddings destroy the geometric structure required for likelihood-based inference.
method Developed a rigorous theory of likelihood-preserving embeddings and introduced the Likelihood-Ratio Distortion metric.
result Controlling the distortion Δn is necessary and sufficient for preserving inference. Gradient-flow optimization is reinterpreted as a statistical inference problem.
problem Optimizing training duration and assessing model performance in deep learning.
method Develops a statistical framework for gradient-flow training, treating it as a random-effects model.
result Establishes asymptotic optimality for prediction and reduces reliance on validation splits.
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…
Framework assesses variable importance for heterogeneous treatment effects.
problem High-risk domains need reliable methods to assess treatment effect heterogeneity.
method Inferential framework based on Shapley values and semiparametric theory.
result Valid inference on variable importance for heterogeneous treatment effects.
New methods improve inference after prediction without strong model assumptions.
problem Improper inference after prediction can lead to invalid results.
method Angelopoulos et al. (2023) and Wang et al. (2020) propose corrections to inference.
result Angelopoulos et al. method controls type 1 error and provides correct coverage.
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
problem Uncertainty quantification for the optimal assortment in multinomial logit models.
method The paper proposes a novel inferential framework to test properties of the optimal assortment in multinomial logit models, reducing the problem to detecting the sign change point of marginal revenue gaps.
result The asymptotic normality of the marginal revenue gap estimator and the construction of a maximum statistic to detect the sign change point.
Paper develops conformalized survival analysis method for better prediction.
problem Survival analysis models often misspecify and require strong assumptions.
method Uses conformal prediction to wrap around any survival prediction algorithm.
result Lower predictive bounds provide guaranteed coverage without strong assumptions.
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 method for debiased inference without assuming exact solutions in inverse problems.
problem Dealing with inverse problems where exact solutions may not exist.
method Nonparametric instrumental variable analysis without structural equations.
result Valid inference on functionals of inverse problems without assuming exact solutions.
DRF improves confidence and uncertainty assessment for multivariate conditional distributions.
problem Estimating multivariate conditional distributions with confidence and uncertainty.
method Developed a bootstrap approximation of the asymptotic distribution of DRF to derive inferential tools.
result Asymptotic coverage guarantees for confidence regions and hypothesis testing.
Measures dependence between two systems using Bayesian model comparison.
problem Quantifying dependence between two systems in a dataset.
method Bayesian model comparison of independence and dependence models.
result Dependence measure quantifies evidence for dependence in data.
Paper introduces a new robust method for estimating Pareto tail index from grouped data.
problem Limited robust methods for estimating Pareto tail index from grouped data.
method Method of Truncated Moments (MTuM)
result Inferential justification and validation of MTuM through simulation study.
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
Understanding the effect of a particular treatment or a policy pertains to many areas of interest, ranging from political economics, marketing to healthcare. In this paper, we develop a non-parametric algorithm for detecting the effects of treatment over time in the context of Synthetic Controls. The method builds on c…
We develop a new modeling framework for Inter-Subject Analysis (ISA). The goal of ISA is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. It has important applications in neuroscience to explore the functional connectivity between brain regions under natural …
The paper explores how invertibility affects the complexity of encoder models in VAEs.
problem The complexity of the encoder model in VAEs when the generative map is invertible.
method Formalizes the concept of strong invertibility and analyzes the complexity of the encoder model.
result Strongly invertible generative maps allow for simpler encoder models, while non-invertible maps require exponentially larger encoders.
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 …
iWGAN improves GANs by stabilizing training and preventing mode collapse.
problem Stable and effective training of GANs with mode collapse.
method iWGAN combines auto-encoders and WGANs using iterative primal dual optimization.
result iWGAN provides a clear stopping criterion and mitigates mode collapse.
We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the ti…
Breiman's paper sparked debate on the future of statistics and machine learning.
problem The tension between traditional statistical modeling and model-free machine learning approaches.
method Discussion of the implications of machine learning's success and the need for new inferential approaches.
result The importance of understanding 'why' and 'if' questions in machine learning is now recognized.
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…
Study uncovers statistical optimality of nonconvex tensor completion methods.
problem Estimating a low-rank tensor from incomplete and corrupted observations.
method Two-stage estimation algorithm for nonconvex optimization.
result Nonconvex tensor completion achieves optimal ℓ2 accuracy. Bayesian approach improves uncertainty in deep learning models.
problem Uncertainty quantification in deep learning models.
method Bayesian point of view, Gaussian approximability, semi-parametric Bernstein-von Mises theorems.
result Bayesian credible regions have valid frequentist coverage, providing theoretical justification for deep learning.
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…
In the 70s a novel branch of statistics emerged focusing its effort in selecting a function in the pattern recognition problem, which fulfils a definite relationship between the quality of the approximation and its complexity. These data-driven approaches are mainly devoted to problems of estimating dependencies with l…
Imputation-Powered Inference improves subpopulation efficiency in missing data settings.
problem Complex missing data patterns challenge standard inference methods.
method Imputation-Powered Inference (IPI) combines blackbox imputation with bias correction.
result IPI provides valid and efficient M-estimation under MCAR blockwise missingness.