New method uses untrusted data for more precise causal analysis.
problem Causal questions with limited trusted data.
method Incorporates untrusted data and trains richer models.
result Tighter, sounder prediction intervals.
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168,657 papers · 148 categories
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45 results for “Meta-analysis”
Develops exact and invariant study-based decompositions for network meta-analysis.
problem Lack of exact contribution decompositions in network meta-analysis.
method Contrast-space projection formulation of NMA, study-based definition of direct and indirect evidence.
result Exact covariance-aware decompositions of NMA estimator into direct and indirect contributions.
A novel robust meta-analysis model using the distribution for outlier accommodation and detectionstat.ME
A new meta-analysis model detects and accommodates outliers.
problem Outliers in meta-analysis studies can skew results.
method Proposes a novel Meta model using the distribution for robustness.
result Demonstrates superior performance in detecting and accommodating outliers.
Meta-analysis improves personalized treatment rules across multiple sites.
problem Lack of generalizability in learning individualized treatment rules across different medical sites.
method Developed a method for individual-level meta-analysis of ITRs, borrowing sign-coherency information between sites.
result Jointly learned site-specific ITRs with improved generalizability.
Meta-analysis finds people value insurance for low-probability risks more than expected.
problem Low-probability risks insurance demand is lower than expected.
method Conducted a meta-analysis of contingent valuation studies.
result Average stated willingness to pay (WTP) for insurance is 87% of expected losses.
New approach estimates treatment effects from decentralized data.
problem Estimating treatment effects from multiple studies with limited data.
method Three classes of ATE estimators derived from Plug-in G-Formula.
result Asymptotic variance of estimators for linear models derived.
The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional me…
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perfo…
Proposes a method to correct for covariate shift in meta-analysis of randomized trials.
problem Invalidation of standard IPD meta-analysis due to covariate shift across studies.
method Placebo-anchored transport framework that treats source-trial outcomes as proxy signals and target-trial placebo outcomes as gold labels.
result Yields target-identified effect estimates in connected targets and a principled screen--then--transport procedure in disconnected targets.
Proposes a new method to handle data heterogeneity in causal inference.
problem Challenges of collaborating between different data centers due to heterogeneity.
method Collaborative inverse propensity score weighting estimator to adjust for distribution shift.
result Significant improvements over traditional meta-analysis methods when dealing with increased heterogeneity.
ChatGPT's medical response accuracy is 56%, but studies vary widely.
problem Lack of standard guidelines for evaluating ChatGPT's performance in medicine.
method Systematic review and meta-analysis of 17 studies.
result ChatGPT's overall integrated accuracy in medical queries is 56%.
Universal approach combines OOD detection scores for robustness.
problem Combining diverse OOD detection scores for robustness.
method Quantile normalization to p-values, meta-analysis, probabilistic interpretation.
result Significantly improved robustness and performance across diverse OOD detection scenarios.
We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose…
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research has been pioneered by NeuroSynth, a database collecting both brain activation coordinates and associated text across a large cohort of neuro…
The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to …
Unified framework combines dependent microbiome tests.
problem Combining dependent microbiome association tests.
method Generalized meta-analysis framework for dependent tests.
result The vanilla Cauchy combination is a special case.
Neural network pruning lacks standardized benchmarks and metrics.
problem Lack of standardized benchmarks and metrics in neural network pruning.
method Meta-analysis of 81 papers, controlled conditions, ShrinkBench framework.
result Neural network pruning community lacks standardized benchmarks and metrics.
The primary goal of this study is doing a meta-analysis research on two groups of published studies. First, the ones that focus on the evaluation of the United States Department of Agriculture (USDA) forecasts and second, the ones that evaluate the market reactions to the USDA forecasts. We investigate four questions. …
Meta Off-Policy Estimationstat.ML
Combines multiple OPE estimators into a more accurate and efficient estimate.
problem Offline evaluation of recommender systems using biased data.
method Meta-analysis of correlated OPE estimators, accounting for inter-estimator correlation.
result Improved statistical efficiency and accuracy in estimating policy value.
Mixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects component is linear. We then develop an efficient approach for a broad problem class that allows nonlinear measurements, priors, and constrain…
The study assesses machine learning generalization using various data set characteristics.
problem Estimating confidence in machine learning predictions and assessing generalization capabilities.
method Meta-analysis of 109 classification data sets, modeling generalization as a function of various characteristics.
result The convex hull of the training data is relevant for assessing machine learning generalization, challenging the common assumption about the curse of dimensionality.
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem…
Latent Noise Injection improves synthetic data generation for privacy and statistical alignment.
problem Slow convergence of generative models in high-dimensional settings.
method Latent Noise Injection using Masked Autoregressive Flows (MAF).
result Synthetic data closely reflects the underlying distribution, especially in high-dimensional settings.
The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that …
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers. Taking inspiration from the COCO benchmark in optimization, we view this task as having critical importance to machine learning and statistics given the rate at which new samplers are proposed. The common hand-crafted exampl…
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, can…
The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scientific studies in various disciplines. In this work, we introduce a new method for controlling the FDR in meta-analysis of many decentralized…
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By e…
Paper proposes a novel method to assess treatment effect estimators using cross-validation.
problem Lack of ground truth to objectively assess treatment effect estimators in RCTs.
method Cross-validation-like methodology combining noisy difference-of-means estimate and aggregation across RCTs.
result Aggressive downweighting or truncation of large values reduces variance and improves treatment effect estimation.
We present the expected values from p-value hacking as a choice of the minimum p-value among independents tests, which can be considerably lower than the "true" p-value, even with a single trial, owing to the extreme skewness of the meta-distribution. We first present an exact probability distribution (meta-distrib…
Study finds little progress in medical machine learning benchmarks over 3 years.
problem Lack of meaningful progress in medical machine learning benchmarks for structured healthcare data.
method Comprehensive review and meta-analysis of benchmarks in medical machine learning for structured data.
result Deep recurrent models perform only better than logistic regression on certain clinical prediction tasks.
In many applications, one works with neural network models trained by someone else. For such pretrained models, one may not have access to training data or test data. Moreover, one may not know details about the model, e.g., the specifics of the training data, the loss function, the hyperparameter values, etc. Given on…
Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies th…
fedCI and fedCI-IOD enable federated causal discovery across diverse datasets with privacy and power enhancements.
problem Causal discovery across multiple datasets with privacy constraints and heterogeneity.
method federated conditional independence test (fedCI) and Integration of Overlapping Datasets (IOD) algorithm extension (fedCI-IOD).
result fedCI-IOD achieves comparable performance to fully pooled analyses, enhancing statistical power and privacy.
Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregationstat.ML
Paper introduces ZIPTF and C-ZIPTF for better tensor factorization of zero-inflated count data.
problem Inefficient tensor factorization for zero-inflated count data, especially in scRNA-seq.
method Zero Inflated Poisson Tensor Factorization (ZIPTF) and Consensus Zero Inflated Poisson Tensor Factorization (C-ZIPTF).
result ZIPTF and C-ZIPTF improve tensor factorization accuracy and consistency for zero-inflated count data.
Wisdom of the crowd, the collective intelligence derived from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual, and improve social decision-making and prediction accuracy. This can also integrate multiple programs or datasets, each as an individual, for…
KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan-Meier Plotscs.LG
KM-GPT automates IPD reconstruction from KM plots with high accuracy and scalability.
problem Manual digitization of IPD from KM plots is error-prone and lacks scalability.
method KM-GPT integrates advanced image preprocessing, multi-modal reasoning, and iterative reconstruction algorithms.
result KM-GPT generates high-quality IPD without manual input or intervention, achieving superior accuracy.
This work classifies strategies to incorporate spatial dependence in Random Forest models.
problem Spatial and temporal dependence in environmental data not adequately modeled by standard Random Forest.
method Taxonomy and systematic review of strategies to adjust Random Forest for spatially dependent data.
result 32 scientific documents reviewed, providing a comprehensive classification of strategies.
Deep learning predicts breast cancer with high accuracy from patient data.
problem Early detection of breast cancer from patient data.
method Feature selection and k-fold Monte Carlo cross-validation using deep learning.
result Deep learning model effectively distinguishes between cancer and healthy patients.
The ungrammatical sentence "The key to the cabinets are on the table" is known to lead to an illusion of grammaticality. As discussed in the meta-analysis by Jaeger et al., 2017, faster reading times are observed at the verb are in the agreement-attraction sentence above compared to the equally ungrammatical sentence "…
Exact computation of posterior distribution of mixture weights in hierarchical Bayesian modelsstat.ME
This paper computes exact posterior distributions of mixture weights in hierarchical Bayesian models.
problem Uncertainty in class membership or data-generating processes in heterogeneous data.
method Exact marginalization of mixture weights using dynamic programming and FFT for two components, and joint dynamic program for K >= 3 components.
result Exact posterior distributions of mixture weights are finite mixtures of Beta distributions, providing credible intervals and per-observation local false-discovery rates.
New BGs use diffusion models to improve sampling from complex distributions.
problem Sampling from complex, multi-modal distributions is challenging.
method Combines diffusion models with annealed Monte Carlo for improved sampling.
result Second-order denoising kernels can improve performance in high-dimensional spaces.
Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing me…
Fine-grained atlases improve fMRI analysis of brain activity.
problem Large fMRI datasets require scalable brain network summaries.
method Trained on millions of fMRI volumes, DiFuMo dictionaries of 64-1024 networks.
result Fine-grained atlases enhance classic fMRI analysis pipelines.