Bias - variance decomposition of the expected error defined for regression and classification problems is an important tool to study and compare different algorithms, to find the best areas for their application. Here the decomposition is introduced for the survival analysis problem. In our experiments, we study bias -…
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48 results for “expectation bias”
Paper measures cognitive bias in positive feedback trading using diffusion process estimates.
problem Measuring cognitive bias in positive feedback trading behavior.
method Conditional estimates of diffusion processes to quantify bias, proving asymptotic properties.
result Bias in positive feedback trading converges to zero over time, leading to adaptive expectations.
Algorithm bounds causal queries under selection bias.
problem Selection bias affects causal analysis.
method Proposes a new algorithm to address both identifiable and unidentifiable causal queries.
result The likelihood of available data is unimodal, enabling bounds on causal queries.
Paper analyzes ECE bias and provides bounds for its estimation.
problem Understanding the estimation bias in ECE for machine learning models.
method Information-theoretic approach to analyze bias in uniform mass and uniform width binning strategies.
result Established upper bounds on ECE estimation bias and optimal number of bins.
Improves robust bias-aware prediction under covariate shift.
problem Challenges in machine learning due to distributional shift between source and target data.
method Extends representer theorem to RBA setting, using reweighted kernel expectation.
result Better performance of RBA classifier on synthetic and natural covariate shift datasets.
DCEM algorithm reduces bias in machine learning models trained on selective labels.
problem Bias in machine learning models trained on selective labels.
method Disparate Censorship Expectation-Maximization (DCEM) algorithm.
result DCEM improves bias mitigation without sacrificing discriminative performance.
New theory shows how learning algorithms can create a bias towards negative outcomes.
problem Negativity bias in adaptive learning algorithms.
method Generalization of the Hot Stove Effect to settings with negative estimates leading to smaller sample sizes.
result Negativity bias persists even when negative estimates do not lead to avoidance.
We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset …
We investigate the accuracy of the two most common estimators for the maximum expected value of a general set of random variables: a generalization of the maximum sample average, and cross validation. No unbiased estimator exists and we show that it is non-trivial to select a good estimator without knowledge about the …
Study shows significant differences in recommendation bias between model-based and memory-based algorithms.
problem Recommendation bias disparity across different algorithms and item categories.
method Examined bias disparity in a range of collaborative recommendation algorithms and item categories.
result Significant differences found between model-based and memory-based algorithms.
Depth uncertainty networks don't improve with bias correction, contrary to expectations.
problem Improving performance in active learning with overparameterised models like NNs.
method Depth uncertainty networks, compared to underparameterised models, show no improvement in performance with bias correction.
result Depth uncertainty networks do not improve with bias correction, unlike underparameterised models.
New method corrects risk estimation bias, improving backtesting results.
problem Underestimation of risk by existing methods, especially in small samples.
method Proposes a new algorithm for bias correction using generalized Pareto distributions.
result The new algorithm leads to improved efficiency in estimating risk with heavy tails or heteroscedasticity.
New algorithm corrects risk estimation bias for heavy-tailed data.
problem Underestimation of risk in banking and insurance due to bias in estimation procedures.
method Proposes a new algorithm for bias correction and applies it to generalized Pareto distributions.
result The algorithm leads to more accurate risk estimation, especially in heavy-tailed data.
Asynchronous Gibbs sampling can accurately estimate expectations of functions of all variables under certain conditions.
problem Estimating expectations of functions of all variables in graphical models.
method Coupling synchronous and asynchronous Gibbs samplers to control expected Hamming distance, using concentration of measure results.
result The bias in estimating expectations of polynomial functions is smaller than the standard deviation of the function value in the true model.
We analyze bias in post-bandit inference for stable index algorithms.
problem Bias in post-bandit inference for stable index algorithms.
method Empirical fluid approximation of sampling dynamics.
result Sharp leading-order expressions for bias and expected Z-statistic.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
BELA infers labels for unlabeled data at lower cost.
problem Efficiently labeling large unlabeled datasets.
method Supervised splitting with bias-reduction techniques.
result BELA outperforms existing adaptive labeling strategies.
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.
Improved optimization methods for discrete distributions reduce bias in gradient estimation.
problem Estimating gradients for discrete distribution parameters is challenging.
method Analyzed and proposed methods to reduce bias in gradient estimation, including Gumbel-Softmax and piece-wise linear continuous relaxation.
result Reduced bias leads to better performance in variational inference and binary optimization tasks.
We analyze bias-variance of margin losses.
problem Understanding model overfitting/underfitting.
method Bias-variance decomposition for strictly convex margin losses.
result Expected risk decomposes into central model risk and data variation.
Ensembles improve classifier performance by reducing bias, not variance.
problem Improving classifier performance through ensemble methods.
method Extended bias-variance decomposition for classification tasks, introducing dual reparameterization.
result Ensembling reduces bias in classifiers, contrary to the traditional view.
New matching estimators correct bias in multivariate settings without smoothing parameters.
problem Bias in nearest-neighbor and matching estimators in multiple dimensions.
method Polynomial least squares fits on Voronoi tessellations.
result Novel estimators converge at rate under mild smoothness assumptions.
New method debiases feature importance in Random Forests.
problem MDI feature importance measure incorrectly assigns high importance to noisy features.
method Derive a new analytical expression for MDI and propose MDI-oob debiased feature importance measure.
result MDI-oob achieves state-of-the-art performance in feature selection from Random Forests.
Study finds conformity bias drives music sampling traditions.
problem How frequency-based bias drives cultural diversity in music sampling.
method Agent-based simulations in approximate Bayesian computation framework.
result Sampling patterns at population-level consistent with conformity bias.
Study on bias of constant-step stochastic approximation with Markovian noise.
problem Understanding the bias in stochastic approximation algorithms with Markovian noise.
method Infinitesimal generator comparisons to analyze bias, Lyapunov equation for time-averaged bias, Richardson-Romberg extrapolation for bias reduction.
result Bias of the algorithm is of order and time-averaged bias is , where is a constant.
Confirmation bias leads to biased estimates in noisy data analysis.
problem Confirmation bias affects scientific conclusions in noisy data environments.
method Investigation of confirmation bias in Gaussian mixture models using K-means and EM algorithms.
result Estimates from algorithms are biased and resemble initial hypotheses, not the noise.
This paper improves distributed regression by correcting bias in regularization kernel networks.
problem Improving the performance of distributed regression with biased base algorithms.
method Develops a bias-corrected version of regularization kernel network for distributed regression.
result Achieves optimal learning rates in both single and distributed regression settings.
BR-SNIS reduces bias in self-normalized IS without increasing variance.
problem Bias in self-normalized IS.
method Iterated sampling-importance resampling (ISIR) to form a bias-reduced estimator.
result Significant reduction in bias without increasing variance.
A new algorithm reduces bias in estimating model parameters.
problem Efficient estimation of model parameters in non-linear state-space models.
method Parisian particle Gibbs (PPG) algorithm for bias reduction in online learning.
result Non-asymptotic bounds on bias and variance for PPG.
Geometric framework analyzes bias in variational inference for posterior functionals.
problem Analyzing the bias of posterior functionals under variational approximations.
method Developed a geometric framework to evaluate the bias of posterior functionals using the variational tangent space.
result The leading-order bias of a posterior functional is determined by its component orthogonal to the variational tangent space.
Stochastic EM with biased MCMC improves inference stability.
problem Intractable E-step in EM algorithm.
method Stochastic approximation with biased MCMC.
result ULA is more stable and sometimes faster than MALA.
Adaptive TBPTT controls gradient bias in RNNs for faster convergence.
problem Choosing optimal truncation length in TBPTT for RNNs is difficult.
method Adaptive TBPTT converts lag selection to bias control, estimating optimal truncation length during training.
result Adaptive TBPTT improves convergence rate and computational efficiency in RNNs.
Unbiased estimation of riskq-fin.RM
The estimation of risk measures recently gained a lot of attention, partly because of the backtesting issues of expected shortfall related to elicitability. In this work we shed a new and fundamental light on optimal estimation procedures of risk measures in terms of bias. We show that once the parameters of a model ne…
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Biasstat.ML
SGD's uncertainty quantified in non-convex learning problems.
problem Uncertainty quantification in non-convex learning problems.
method Asymptotic normality of SGD iterates and bias characterization.
result SGD iterates are asymptotically normally distributed around the expected value of the invariant distribution.
This work investigates implicit bias in multiclass separable data using a novel geometry-aware optimizer.
problem Understanding implicit bias in overparameterized models on multiclass separable data.
method Introduces NucGD, a geometry-aware optimizer enforcing low-rank structures through nuclear norm constraints.
result NucGD enables scalable training and characterizes the impact of stochastic optimization dynamics.
There exist a number of reinforcement learning algorithms which learnby climbing the gradient of expected reward. Their long-runconvergence has been proved, even in partially observableenvironments with non-deterministic actions, and without the need fora system model. However, the variance of the gradient estimator ha…
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confoundingstat.ML
Estimates causal effects with selection bias and confounding using regression.
problem Estimating causal effects in presence of selection bias and confounding.
method Two-step regression estimator (TSR) that corrects for selection bias and accounts for confounding.
result TSR estimator reduces variance and is validated in simulations.
New insights into bias mitigation show DRO isn't a complete solution.
problem Bias in machine learning systems across different data subsets.
method Theoretical analysis of Distributionally Robust Optimization (DRO) and data curation.
result Neither DRO nor data curation alone can fully address bias issues.
To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational speed. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness …
The paper addresses sampling bias in risk-based active learning.
problem Sampling bias in active learning leads to poor decision-making performance.
method The paper uses a semi-supervised Gaussian mixture model with an EM algorithm to counteract sampling bias.
result The EM algorithm effectively incorporates pseudo-labels for unlabelled data, reducing sampling bias.
Short-horizon bias causes meta-optimization to favor small learning rates.
problem Short-horizon bias in meta-optimization leads to suboptimal learning rates.
method Analyzes a noisy quadratic cost function and runs meta-optimization experiments on benchmark datasets.
result Meta-optimization chooses too small a learning rate, even with a long time horizon.
The paper explores the trade-off between bias and variance in high-dimensional models.
problem Understanding the unavoidable trade-off between bias and variance in high-dimensional statistical models.
method Proposes a general strategy to obtain lower bounds on the variance of estimators with a specified bias, and applies it to various statistical models.
result Shows the extent to which the bias-variance trade-off is unavoidable and quantifies the performance loss for methods that do not balance it.
The hidden tail of empirical distributions is analyzed using extreme value theory.
problem Understanding the bias between in-sample mean and true statistical mean for large .
method Extreme value theory applied to empirical distributions and their moments.
result The hidden moment of order 0 for power law distributions follows an exponential distribution with expectation .
This paper explains the theoretical inductive bias of Isolation Forest.
problem Lack of theoretical foundation explaining Isolation Forest's success.
method Formulated the growth process of iForest as a random walk, derived expected depth function using transition probabilities.
result Established a theoretical understanding of iForest's effectiveness and parameter adaptability.
Boundary effects inflate variance in Gaussian processes, leading to acquisition bias.
problem Boundary-induced acquisition bias in Gaussian processes.
method Traced root cause to geometric mechanism of kernel truncation at domain boundaries.
result Boundary effects create distortion that worsens with dimensionality, affecting acquisition behavior.
The main aim of this paper is to inspect the properties of survey based on households inflation expectations, conducted by Reserve Bank of India. It is theorized that the respondents answers are exaggerated by extreme response bias. Latent class analysis has been hailed as a promising technique for studying measurement…
Extends Thompson sampling for RL with fewer episodes.
problem Limited episodes in RL settings.
method Batch Bayesian optimization over episodes to learn action bias terms.
result Significantly outperforms standard Thompson sampling.
Paper introduces new importance metrics for machine learning models, linking them to CATE.
problem Interpreting black-box models' importance metrics due to data dependence and non-parametric nature.
method Introduces MVIM and CVIM, proposing permutation-based estimation and bias-variance decomposition.
result MVIM and CVIM have a quadratic relationship with CATE, addressing bias in correlated predictors.