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.
ECI improves time series prediction uncertainty quantification by smoothing miscoverage error.
problem Challenges in uncertainty quantification for time series prediction due to temporal dependence and distribution shift.
method Error-quantified Conformal Inference (ECI) by smoothing quantile loss function and introducing adaptive feedback scale.
result ECI achieves valid miscoverage control and tighter prediction sets than existing methods.
EBUCB framework achieves optimal regret with bounded approximate inference error.
problem Theoretical gap between practical performance and theoretical justification of Bayesian bandit algorithms with approximate inference.
method Enhanced Bayesian Upper Confidence Bound (EBUCB) framework that accommodates bandit problems with approximate inference.
result EBUCB achieves optimal regret order O(logT) under certain conditions on inference error. Selective inference controls Type I error in k-means clustering tests.
problem Inflated Type I error in classical hypothesis tests for k-means clusters.
method Selective inference approach to control Type I error.
result Proposes a computable finite-sample p-value for selective inference.
Selective inference framework for CART trees to control error rates and coverage.
problem Inference on CART trees does not control Type 1 error rates and coverage.
method Selective inference framework conditioning on tree estimation, efficient algorithms.
result Proposes tests and intervals for CART trees with selective error control.
The study sets lower bounds on MMSE for inferring sensitive features from noisy data.
problem Estimating sensitive features from noisy observations of correlated features.
method Adversarial evaluation framework based on MMSE estimation with theoretical lower bounds.
result Derives closed-form bounds for linear models, showing optimality in noise variance.
This paper analyzes error in SKI for Gaussian Processes, providing conditions for linear time inference.
problem Lack of rigorous theoretical error analysis for SKI.
method Proved error bounds for SKI Gram matrix, examined error effects, provided practical guidelines.
result Identified two dimensionality regimes for SKI's scalability-accuracy trade-offs.
ConvMMD improves inference in noisy data.
problem Inference degradation due to measurement error in noisy data.
method Convolutional Maximum Mean Discrepancy (convMMD) for inference with noisy, heteroscedastic observations.
result Established consistency and asymptotic normality of the convMMD-based estimator.
Study proposes method to estimate causal effects from noisy treatment data.
problem Estimating causal effects from noisy treatment data without side information.
method Deep latent variable model with neural network parameterization and amortized importance-weighted variational objective.
result Causal effect estimates are identifiable without side information and measurement error variance knowledge.
Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.
problem Inefficiency of Hamiltonian Monte Carlo on ReLU neural networks.
method Analysis of Hamiltonian Monte Carlo with leapfrog integrator for Bayesian neural network inference.
result Leapfrog HMC for ReLU networks has a large local error rate of Ω(ε), leading to inefficiency. The paper analyzes the error accumulation in a compositional score-based algorithm for SBI.
problem How to effectively combine multiple observations to improve parameter inference.
method Study of the GAUSS algorithm's compositional score and its mean squared error.
result Established an upper bound on the mean squared error of the compositional score.
L-HNNs improve Bayesian inference by reducing gradient requirements and improving ESS.
problem Efficient Bayesian inference with complex target densities.
method Latent Hamiltonian Neural Networks (L-HNNs) with NUTS, incorporating online error monitoring.
result L-HNNs in NUTS with online error monitoring required 1--2 orders of magnitude fewer numerical gradients and improved ESS by an order of magnitude.
Bayesian framework tackles measurement error in covariates.
problem Misleading inference due to corrupted covariates.
method Bayesian Nonparametric Learning framework robust to misspecification.
result General framework for Classical and Berkson error models.
The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, …
Neural networks improve nonparametric regression with measurement errors.
problem Nonparametric regression with measurement errors.
method Proposes a neural network design using FNN, normalizing flow, and inference network.
result Neural network approach is more flexible and superior or comparable to classical methods.
Paper models entropy-based impact of soft errors on neural network inference.
problem Estimating impact of radiation-induced faults on neural network inference.
method Entropy-based statistical models for SEU and MBU across layers.
result Accurate models to evaluate error-resiliency of neural network topologies.
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of post-hoc accuracy measures that are both theoretically justified and computationally …
We study the effects of approximate inference on the performance of Thompson sampling in the k-armed bandit problems. Thompson sampling is a successful algorithm for online decision-making but requires posterior inference, which often must be approximated in practice. We show that even small constant inference error …
Improves spatio-temporal forecasting by reducing errors between training and inference.
problem Accumulation of small errors in Seq2Seq models during inference due to different distributions of training and inference phases.
method Curriculum learning based on Temporal Progressive Growing Sampling to replace some ground-truth context with generated predictions.
result Better models long-term dependencies and outperforms baseline approaches on two datasets.
Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experiments. Using observational data to answer causal questions is risky: subjects who receive different treatments also differ in other ways that a…
ALO-CV approximates leave-one-out error in proportional regime.
problem Estimating generalization error in high-dimensional settings.
method Developed new analysis for ALO-CV, showed consistency under strong convexity.
result ALO-CV approximates leave-one-out error up to negligible error.
Proposes selective inference for testing differences in means between clusters.
problem Inflated type I error rate when testing differences in means between clusters.
method Selective inference approach to control selective type I error rate.
result Controls selective type I error rate by accounting for data-driven cluster definition.
The paper shows how sketching data can simplify regression inference even when errors are heteroskedastic.
problem Performing robust inference with heteroskedastic errors using sketched data.
method Using random projections to sketch data, the paper shows that sketched estimates behave as if errors are homoskedastic.
result Estimation by random sampling does not have the same property, and sketched estimates are asymptotically normal with homoskedastic variance.
L-HNNs improve Bayesian inference efficiency by reducing gradient computation.
problem Efficient Bayesian inference with minimal gradient computation.
method Integrating L-HNNs into NUTS with online error monitoring.
result L-HNNs in NUTS outperform NUTS in complex posterior densities.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, rem…
We analyze errors in filtering algorithms using optimal transport.
problem Estimation errors in optimal transport-based filtering algorithms.
method Systematic analysis of estimation errors for conditional Brenier maps.
result Demonstrates effectiveness and practical potential of the optimal transport filtering algorithm.
New framework for network regression models accounting for community structure.
problem Inaccurate modeling of residual dependencies in network regression models.
method Modeling errors as community-based and exploiting exchangeability properties.
result Parsimonious standard errors for regression parameters.
This work shows how approximate reward models can significantly improve inference-time scaling.
problem Improving the efficiency of inference for large language models.
method Identifying the Bellman error of approximate reward models and using Sequential Monte Carlo (SMC) for inference.
result Approximate reward models can reduce computational complexity from exponential to polynomial in T. Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth…
New method for scalable inference in large-scale regression models with complex error structures.
problem Challenges in statistical inference for large-scale regression models with dependent errors.
method Generalized Method of Wavelet Moments with Exogenous variables (GMWMX).
result Statistical validity and scalability of GMWMX for linear models with complex error structures.
Proposes a robust method for high-dimensional linear models.
problem Inference in high-dimensional settings with heavy-tailed errors and clustered data.
method Residual randomization procedure for Lasso-based inference.
result Outperforms state-of-the-art methods in challenging settings.
EVODiff optimizes DM inference by reducing conditional entropy, improving image generation.
problem Slow and inaccurate inference in diffusion models.
method Entropy-aware variance optimization for efficient inference.
result Significant improvement in image generation quality and efficiency.
We consider active maximum a posteriori (MAP) inference problem for Hidden Markov Models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states. We develop an analytical approach to this problem for…
Paper tackles SBI under model misspecification, presenting robust strategies.
problem Challenges in SBI under model misspecification.
method Three key strategies: robust summary statistics, generalised Bayesian inference, and error modelling.
result Empirical results show vulnerabilities of SBI and effectiveness of misspecification-robust alternatives.
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Monte Carlo approaches. The key idea is to derive a subjective bound on the symmetrized KL divergence between the distribution achieved by an a…
si4onnx enables selective inference on deep learning models.
problem Establishing the reliability of AI systems through statistical significance of identified regions.
method Selective inference techniques implemented through a Python package.
result Controlled type I error rates for hypothesis testing on deep learning models.
Paper calculates the exact error of LDA models.
problem Bayesian generalization error in Latent Dirichlet Allocation (LDA).
method Theoretical analysis of learning coefficient using algebraic geometry.
result Exact asymptotic form of LDA's generalization error.
Bayesian method improves segmentation accuracy with noisy labels.
problem Annotation errors in semantic segmentation due to mislabeling and spatial correlations.
method Approximate Bayesian estimation with spatially correlated discrete distributions and variational inference.
result The method achieves performance comparable to clean labels under moderate noise levels.
New method warns of counterfactual non-identifiability in DSCMs.
problem Counterfactual inference from observational data is non-identifiable even without unobserved confounding.
method Prove counterfactual identifiability for monotonic generation mechanisms, provide impossibility result for general mechanisms, propose method for estimating worst-case errors.
result Non-identifiability of counterfactual inference from observational data, even in absence of unobserved confounding.
Cluster jackknife improves inference for staggered DID methods.
problem Over-rejection of CSDID in small clusters or treated clusters.
method Cluster jackknife for CSDID inference.
result Cluster jackknife greatly improves inference for CSDID.
Paper addresses fairness issues in error-prone outcomes.
problem Fairness in error-prone outcomes.
method Combining fair ML methods and measurement models.
result Using a latent variable model removes detected unfairness.
We use the GARCH model with a fat-tailed error distribution described by a rational function and apply it for the stock price data on the Tokyo Stock Exchange. To determine the model parameters we perform the Bayesian inference to the model. The Bayesian inference is implemented by the Metropolis-Hastings algorithm wit…
The paper connects ABC to GBI, suggesting ABC as a robustification strategy.
problem Approximate Bayesian Computation struggles with tractability in complex simulators.
method Reinterpreting ABC as an implicitly defined error model and suggesting GBI.
result ABC can be seen as a robustification strategy for approximating Bayesian posteriors.
HarDNN detects and protects CNNs from hardware errors.
problem Hardware errors in CNNs can corrupt inference output.
method Statistical error injection and heuristic vulnerability assessment.
result HarDNN improves CNN resilience with minimal additional computation.
New synthetic data analysis reveals high type 1 error rates.
problem Analyzing synthetic data for inference raises significant methodological challenges.
method Developed statistical inference tools and conducted a simulation study.
result Type 1 error rates are unacceptably high in synthetic data analysis.
Corrects errors in ILA for Bayesian inference in LGMs.
problem Error in ILA for non-Gaussian likelihoods in LGMs.
method Importance sampling scheme to correct ILA errors.
result Corrected posterior converges to the true posterior with increased samples.
A new estimator improves financial econometrics by providing reliable inference.
problem Poor performance of standard regression methods in financial economics with thick-tailed predictors.
method Developed an unbiased, consistent, and asymptotically normal estimator for linear regression.
result The new method delivers reliable inference under heteroskedasticity and quantile regression.