Bayesian method corrects bias in class-aggregated values.
problem Bias in aggregated values from classification algorithms.
method Bayesian inference with constraints on model parameters.
result Outperforms existing methods in mean squared error.
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48 results for “aggregation bias”
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysiscs.LG
Proposes a novel MTL approach based on bias-variance analysis.
problem Improving multi-task learning performance through shared knowledge.
method Two-phase iterative aggregation of targets and features using bias-variance analysis.
result Validation on synthetic and real-world datasets demonstrates the effectiveness of the proposed method.
We develop a general multivariate aggregation property which encompasses the distinct versions of the property that were introduced by Neuberger [2012] and Bondarenko [2014] independently. This way, we classify new types of model-free realised characteristics for which risk premia may be estimated without bias. We focu…
Aggregation challenges causal interpretation of IV estimators.
problem Aggregation of fine-grained components into an aggregate treatment variable.
method Characterization of conditions for identifying aggregate causal effects.
result Standard IV estimators cannot identify aggregate causal effects due to ambiguous dependencies.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
problem Model bias in traditional physics-based models across different income and education levels.
method Physics-guided GNNs with refined neighbor selection and weights.
result Preserves equitable performance across different sensitive groups in the Delaware River Basin.
CARE improves LLM aggregation by accounting for shared confounders.
problem LLM judges' correlated errors due to shared latent confounders.
method CARE explicitly models judges' scores as true quality and confounders.
result CARE reduces aggregation error by up to 26.8% across various benchmarks.
Improved disentanglement through learned feature aggregation.
problem Disentangling latent factors in images.
method Variational autoencoder trained on regionally aggregated feature maps from ImageNet.
result 2nd place in NeurIPS 2019 disentanglement challenge.
AEA dynamically aggregates ensemble targets for actor-critic learning.
problem Static ensemble aggregation methods struggle with overestimation bias and variance.
method Adaptive Ensemble Aggregation (AEA) dynamically constructs ensemble-based targets.
result AEA converges to optimal variance reduction and maximal Fisher information.
Study improves fair opinion aggregation by balancing voter attributes.
problem Aggregation of opinions can be biased by voter attributes.
method Combines majority voting and D&S model with fairness options.
result Effective combination of Soft D&S and fairness options for different data types.
New model reduces bias in crowdsourced pairwise comparisons.
problem Crowdsourced pairwise comparisons are biased due to perceptual factors.
method factorBT model accounts for irrelevant factors affecting worker answers.
result factorBT produces more accurate rankings than previous models.
Intersectional constraints improve selection outcomes by reducing inequality.
problem Persistent inequality and reduced utility in selection processes due to implicit bias.
method Introducing intersectional constraints to mitigate the adverse effects of implicit bias in selection processes.
result Intersectional constraints can recover almost all the utility achievable in the absence of implicit bias, offering a significant advantage over non-intersectional constraints.
We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of synthetic time-series with known properties. We use the results from the synthe…
Bias correction needed after deep learning regression training.
problem Systematic error accumulation in deep learning regression models.
method Adjust bias of the machine learning model post-training.
result Bias correction efficiently solves error accumulation.
When does Subagging Work?stat.ML
Subagging improves regression tree performance, especially with many splits.
problem Improving regression tree performance with subsample aggregating.
method Formalized bias and variance dependencies, compared subagging to single trees, and analyzed optimal tree sizes.
result Subagging improves tree performance, especially with many splits.
In order to improve forecasts, a decisionmaker often combines probabilities given by various sources, such as human experts and machine learning classifiers. When few training data are available, aggregation can be improved by incorporating prior knowledge about the event being forecasted and about salient properties o…
Two new algorithms reduce feature space while preserving non-linear relationships.
problem High-dimensional data and overfitting issues.
method Bias-variance analysis for non-linear transformations and generalized linear models.
result Competitive performance on regression and classification tasks.
AI-generated variables bias regression estimates; methods correct for invalid inference.
problem Bias in regression estimates due to AI-generated variables.
method Two methods: bias correction and joint estimation.
result Valid inference restored through proposed methods.
GCNs improve regression tasks by aggregating neighbor signals.
problem GCNs' statistical properties in regression tasks are poorly understood.
method Examined two GCN convolutions and their impact on learning error.
result GCNs have a bias-variance trade-off that depends on neighborhood size and topology.
New aggregation method improves GNN robustness to structural perturbations.
problem Graph Neural Networks (GNNs) are vulnerable to adversarial attacks that manipulate graph structure.
method Proposes a robust aggregation function with a breakdown point of 0.5, inspired by robust statistics.
result Improves GNN robustness by a factor of 3 on Cora ML and 5.5 on Citeseer, and 8 for low-degree nodes.
Local adaptive methods in FL can accelerate convergence but introduce bias, which is corrected.
problem The effect of using adaptive optimization methods for local updates in federated learning.
method Proposed correction techniques to overcome the bias introduced by local adaptive methods.
result Correction techniques can achieve faster convergence and higher test accuracy than baseline methods.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
This paper tackles bias in federated learning without compromising data privacy.
problem Bias in federated learning models.
method Three pre-processing and in-processing methods to mitigate bias.
result Proposed methods are effective even with skewed data distributions or a small number of participating parties.
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing err…
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learningcs.LG
Federated learning can be vulnerable to adversarial attacks, which this work addresses.
problem Federated learning's adversarial vulnerability when deployed.
method Bias-Variance decomposition for federated learning, proposing Fed_BVA framework.
result Fed_BVA framework generates adversarial examples to improve robustness.
New method creates synthetic pseudo-panel data to study transport preferences.
problem Aggregation bias in cross-sectional data.
method Conditional Variational Autoencoder (CVAE) framework for probabilistic model.
result Revealed dynamics of transport preferences and categorized individuals.
The paper addresses privacy in rank aggregation using randomized responses.
problem Preserving privacy while aggregating pairwise rankings.
method Adaptive debiasing method for randomized response rankings.
result Established minimax rates for estimation errors and optimal privacy guarantees.
This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.
problem Learning from aggregated responses in privacy-sensitive settings.
method Investigates bag-level and instance-level loss functions, and introduces an interpolating estimator.
result Instance-level loss can be seen as a regularized form of bag-level loss, leading to improved estimators.
Federated learning improves by unbiased gradient aggregation and controllable meta updating.
problem Gradient biases and inconsistency between target and optimization objectives in federated averaging.
method Unbiased gradient aggregation with keep-trace gradient descent and gradient evaluation strategy, controllable meta updating with small data samples.
result Faster convergence and higher accuracy with different network architectures in various FL settings.
Uncovering Representation Bias for Investment Decisions in Open-Source Large Language Modelsq-fin.CP
This study examines representation bias in open-source Qwen models for investment decisions.
problem Representation bias in financial applications of large language models.
method Balanced round-robin prompting over 150 U.S. equities, constrained decoding, token-logit aggregation.
result Firm size and valuation increase model confidence, while risk factors decrease it.
FedAVOT improves federated learning by aligning user distributions.
problem Partial client participation leads to biased and unstable updates in federated learning.
method Formulates aggregation as masked optimal transport to align availability and importance distributions.
result Achieves a standard O(1/√T) rate, independent of the number of participating users per round.
Study proposes a multi-agent framework to mitigate bias in sentiment analysis.
problem Bias in sentiment analysis models.
method Integrates multiple LLMs, incorporates dialogue sessions, and uses probabilistic prediction.
result KCS+IBC reduces entropy and increases variance, suggesting improved balance between aggregation and diversity.
Local Gradient Descent with local steps converges to the centralized model in the interpolation regime.
problem Understanding the implicit bias of Local Gradient Descent in the interpolation regime.
method Analyzing the implicit bias of Local Gradient Descent for classification tasks with linearly separable data.
result The aggregated global model from Local-GD converges exactly to the centralized model in the interpolation regime.
pFedGame uses game theory for decentralized federated learning in dynamic networks.
problem Performance bottlenecks, data bias, model convergence issues, and model poisoning attacks in federated learning.
method pFedGame employs game theory to decentralize federated learning, avoiding a central aggregation server and addressing dynamic network challenges.
result pFedGame achieves higher accuracy (over 70%) in heterogeneous data compared to existing methods.
Paper proposes a new method combining random forests and Lasso selection.
problem Improving random forest performance by applying Lasso regression.
method Adaptive Lasso weighting applied to random forest predictions.
result Unified framework strictly outperforms other methods in simulations and real-world datasets.
The paper addresses bias in visual recognition models by reweighting observations.
problem Bias in deep neural networks trained on biased image databases.
method Reweighting observations based on known biasing mechanisms to form a nearly debiased estimator.
result The approach can remedy representativeness issues in visual recognition systems.
How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day sc…
SkewSize detects model biases by analyzing mistakes across subgroups.
problem Benchmarking model performance in the presence of spurious correlations.
method Introducing SkewSize, a metric that captures bias from model mistakes.
result SkewSize highlights biases not captured by other metrics.
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noiseq-fin.ST
Bayesian consensus improves accuracy of forecasts from miscalibrated sources.
problem Aggregating predictions from miscalibrated and noisy sources.
method Bayesian approach to adjust for bias and noise, using hierarchical models.
result Bayesian consensus estimator is unbiased and more efficient than alternatives.
To Bag is to Prunestat.ML
Random Forests automatically prune a latent 'true' tree, explaining their overfitting without tuning.
problem Difficulty in building bad Random Forests and overfitting without apparent consequences.
method Bootstrap aggregation and model perturbation in Random Forests.
result Randomized ensembles implicitly perform optimal early stopping out-of-sample, explaining overfitting.
Differential Beliefs in Financial Markets Under Information Constraints: A Modeling Perspectiveq-fin.MF
Study market efficiency under partial information using SDEs and optimization.
problem Market efficiency under partial information constraints.
method McKean-Vlasov-type SDEs, Wasserstein barycenters, KL divergence, convex optimization, optimal control, nonlinear filtering.
result Convergence of reduced-information market price processes to true price process under increasing information flow.
Siegel's paradox is a fundamental question in international finance about exchange rates for futures contracts and has puzzled many scholars for over forty years. The unorthodox approach presented in this article leads to an arbitrage-free solution which is invariant under currency re-denominations and is symmetric, as…
Regression networks improve few-shot classification with small data.
problem Generalizing to new classes with limited examples.
method Meta-learning classification by regressing closest approximations in class subspaces.
result Regression networks achieve excellent results in few-shot learning.
We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete on…
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
problem Lack of universal benchmark and evaluation framework for financial KG construction.
method Agentic and holistic evaluation principles, deterministic commit-then-justify judging protocol, binary and ordinal evaluations.
result Reflection-based extraction outperforms single-pass extraction in comprehensiveness, precision, and relevance.
DOLCE improves off-policy evaluation and learning by decomposing effects.
problem Bias in off-policy evaluation and learning due to policy mismatch.
method Uses lagged contexts and a moment-based training procedure to decompose and cancel bias.
result DOLCE achieves substantial improvements in off-policy evaluation and learning.
Estimates treatment effects in bipartite systems with partial eligibility and interference.
problem Randomized experiments in bipartite systems with partial treatment eligibility and interference.
method Formalizes eligibility-constrained bipartite experiments, defines PTTE and STTE, identifies conditions, develops ensemble estimators, introduces projection.
result Proposed estimators recover PTTE and STTE with low bias and variance, corrects interference bias in field experiments.
Deep chest X-ray classifiers show bias in predicting diagnoses.
problem Bias in deep learning classifiers predicting diagnoses from chest X-rays.
method Trained convolutional neural networks on multiple public datasets to predict 14 diagnostic labels.
result True positive rates vary significantly among different protected attributes, indicating bias.
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.