Efficient variance estimation for kernel ridge regression.
problem Estimating variance in kernel ridge regression efficiently.
method Random projection approach to estimate variance.
result Optimal variance estimator for various kernels.
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48 results for “estimation variance”
This study reviews techniques to estimate volatility and price Variance Swaps.
problem Estimating historical volatility and pricing Variance Swaps.
method Review of existing techniques.
result Discussion of various methods to estimate volatility and price Variance Swaps.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
problem Pathwise gradient estimators in variational inference have high variance, leading to inefficient optimization.
method Apply zero-variance control variates to pathwise gradient estimators.
result Zero-variance control variates can significantly reduce the variance of pathwise gradient estimators without requiring complex assumptions.
Jackknife variance estimation validated for generalized U-statistics.
problem Uncertainty quantification for subsampling-based estimators.
method Jackknife variance estimation for generalized U-statistics with row-wise weak law.
result Jackknife and delete- variance estimators are ratio-consistent for generalized U-statistics.
VarGrad reduces variance in ELBO gradient estimation for variational inference.
problem Improving the variance of gradient estimators in variational inference.
method VarGrad uses a new log-variance loss to estimate the ELBO gradient, achieving lower variance than the score function method.
result VarGrad offers a lower variance gradient estimator compared to other methods.
We find an unbiased estimator for MMD variance.
problem Efficiently estimating the variance of MMD estimators.
method Extending and correcting previous work, we derive an unbiased estimator for MMD variance.
result We provide a truly unbiased estimator for MMD variance with no additional computational cost.
Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networksstat.ML
Paper improves confidence intervals and variance estimation for deep learning models.
problem Improving confidence intervals and variance estimation in deep learning models.
method Residual-based framework for conditional variance estimation; robust bootstrap procedure for confidence intervals.
result First non-asymptotic bounds for variance estimation using ReLU networks.
The paper develops estimators for variance in graph structures using fused lasso.
problem Variance estimation in graph-structured problems.
method Developed linear time estimator for homoscedastic case and total variation regularization estimator for heteroscedastic case.
result Minimax rates and consistency for variance estimation in various graph structures.
REBAR reduces gradient variance in discrete latent models.
problem High variance in gradient estimates for models with discrete latent variables.
method Introduces a continuous relaxation of discrete variables and a novel control variate to produce low-variance, unbiased gradient estimates.
result State-of-the-art variance reduction on generative modeling tasks, leading to faster convergence.
New estimator accurately estimates mean of real-valued distributions without variance knowledge.
problem Estimating the mean of real-valued distributions without prior variance knowledge.
method Introduces a novel estimator that converges sub-Gaussian and works across distributions with bounded variance.
result The estimator achieves accuracy of σ·(1+o(1))√(2log(1/δ)/n) with parameters n, δ, and σ².
New estimator reduces bias and variance issues in mutual information estimation.
problem Difficulty in using variational MI estimators due to bias/variance tradeoffs and self-consistency issues.
method Developed a new estimator based on a unified perspective of variational approaches, focusing on variance reduction.
result Empirical results show improved bias-variance trade-offs compared to existing estimators.
Bayesian methods reduce variance in subspace identification for small data sets.
problem High variance in traditional subspace identification methods for large models or small sample sizes.
method Investigation of Bayesian estimation solutions (regularized and shrinkage estimators) for subspace identification.
result Bayesian estimators reduce estimation risk by up to 40% compared to traditional methods.
Improves gradient estimation for discrete distributions with variance reduction techniques.
problem Excessive variance in gradient estimation for discrete distributions.
method Stein operators for discrete distributions and control variates.
result Substantially lower variance in gradient estimation.
Paper introduces VDE, a variance-reduced determinant estimator.
problem Estimating determinants with low variance and efficiency.
method Combines variational inference and spherical normalizing flows.
result VDE achieves zero variance in ideal cases, requiring only one sample.
Study on variance of policy gradient in simple RL environments.
problem Understanding variance of policy gradient estimators in continuous RL.
method Analyzes REINFORCE estimator in linear-quadratic environments with Gaussian noise.
result Derives and validates bounds on estimator variance empirically.
Novel estimator reduces diffusion model variance.
problem High variance in score function estimation for diffusion models.
method Uses nearest neighbour samples to estimate the score function.
result Significant decrease in variance, leading to improved model performance.
Paper proposes robust estimators for heavy-tailed data with infinite variance.
problem Developing robust estimators for heavy-tailed data with infinite variance.
method Proposes two robust estimators: ridge log-truncated M-estimator and elastic net log-truncated M-estimator.
result Demonstrates robustness of log-truncated estimations over standard estimations through simulations and real data analysis.
Estimates Markov chain variance efficiently without storing samples.
problem Estimating the asymptotic variance of Markov chain functions.
method Linear stochastic approximation of Poisson equation solution.
result Optimal MSE convergence rate with finite sample guarantees.
Kernel ridge regression imputation with consistent variance estimation for handling missing data.
problem Handling missing data in statistical analysis.
method Kernel ridge regression imputation combined with entropy method for variance estimation.
result Root-n consistency of the imputation estimator in a Sobolev space setting.
We present a set of log-price integrated variance estimators, equal to the sum of open-high-low-close bridge estimators of spot variances within subsequent time-step intervals. The main characteristics of some of the introduced estimators is to take into account the information on the occurrence times of the high a…
A new gradient estimator reduces variance near boundaries for binary latent variables.
problem Explosive gradient variance near boundaries in binary latent variable models.
method Introduces a new gradient estimator (bitflip-1) and an aggregated estimator (UGC) that uses either bitflip-1 or DisARM for each coordinate.
result UGC has uniformly lower variance than DisARM and achieves optimal optimization objectives.
Paper proposes variance reduction for Markov chains, especially useful in MCMC.
problem Reducing variance in Markov chain additive functionals.
method Minimizes asymptotic variance of functionals over control variates.
result Significantly reduces overall finite sample variance in simulations.
The paper estimates variance of random sections on complex manifolds.
problem Estimating variance of random holomorphic sections on compact Kahler manifolds.
method Analyzes a sequence of smooth Hermitian holomorphic line bundles on a compact Kahler manifold X, considering specific probability measures.
result Provides variance estimates for various measures including Gaussian and Fubini-Study measures.
Paper analyzes NCE method for unnormalized models, reducing asymptotic variance.
problem Estimating parameters of unnormalized models with high asymptotic variance.
method Proposes a method to reduce asymptotic variance by estimating auxiliary distribution parameters and analyzing objective function forms.
result NCE estimator is consistent and asymptotically normal, with reduced variance.
Paper proposes new estimators for error variance in high-dimensional linear models.
problem Estimating error variance in high-dimensional linear models.
method Proposes natural lasso and organic lasso estimators.
result Natural lasso estimator maximizes a penalized likelihood objective and has provably good performance.
A new method reduces variance in training discrete latent variable models.
problem High variance in stochastic gradient estimators for discrete latent variable models.
method Double control variates for score function estimators using Taylor expansions.
result Our method can have lower variance compared to other estimators.
New objective reduces bias and variance in reinforcement learning derivatives.
problem Estimating derivatives in reinforcement learning with unknown dynamics.
method Derives an objective function compatible with any advantage estimators, allowing trade-off between bias and variance.
result Demonstrates effectiveness in both theoretical and practical settings.
Paper improves Gumbel-Softmax estimator variance reduction.
problem Challenges in gradient estimation for models with discrete latent variables.
method Rao-Blackwellization applied to straight-through Gumbel-Softmax estimator.
result Reduces mean squared error and variance of Gumbel-Softmax estimator.
Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.
problem Asymptotic variance reduction in general integration problems.
method Characterization of conditions for asymptotic variance reduction using normal distributions.
result Asymptotic variance reduction is guaranteed for normal distributions in moment matching Monte Carlo.
This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…
The paper provides concentration inequalities for Markov chain variance estimators.
problem Estimating the variance of Markov chains with concentration properties.
method Martingale decomposition method for uniformly geometrically ergodic Markov chains.
result Explicit control of the p-th moment of the OBM estimator difference and dependence on p and mixing time.
ARM policy gradient reduces variance for binary actions.
problem High variance in policy gradients for binary actions.
method Augment-Reinforce-Merge (ARM) policy gradient estimator.
result ARM estimator achieves significant variance reduction and faster convergence.
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.
Algorithm estimates common mean from Gaussian variables with unknown variances.
problem Estimating common mean from Gaussian variables with different unknown variances.
method Intuitive and efficient algorithm using Subset-of-Signals model as benchmark.
result Improved estimation error by polynomial factors compared to previous work.
New Riemannian optimization improves variance estimation in mixed models.
problem Challenges in estimating variance parameters in linear mixed models due to constraints.
method Formulated as an optimization problem on a Riemannian manifold, using Riemannian gradient and Hessian.
result Yields higher quality variance parameter estimates compared to existing methods.
New method reduces density estimation variance for multivariate data.
problem Efficient multivariate density estimation with reduced dimensionality.
method Variance-Reduced Sketching (VRS) framework for multivariate density estimation.
result VRS framework significantly improves density estimation over existing methods.
We analyze the variance of Fisher information estimators in deep learning models.
problem Understanding the variance of Fisher information in deep learning models.
method Investigated two unbiased and consistent estimators of Fisher information matrix.
result The variance of estimators is influenced by the model's parametric structure.
Paper develops efficient mechanisms for estimating variance and covariance under differential privacy in the add-remove model.
problem Estimating variance and covariance under differential privacy in the add-remove model.
method Developed mechanisms based on the Bézier mechanism, a novel moment-release framework.
result Proved minimax optimality of the Bézier-based estimator in the high-privacy regime and demonstrated its better utility in instance-wise analysis.
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Samplingcs.LG
New IS methods fail to reduce variance in long-horizon MDPs.
problem High variance in off-policy evaluation for long-horizon domains.
method Conditional Monte Carlo analysis of IS methods.
result No strict variance reduction for per-decision or stationary IS methods in finite horizon MDPs.
Simplifies gradient estimation for variational inference with reduced variance.
problem Reducing variance in gradient estimation for variational inference.
method Proposes a new gradient estimator by removing the score function term from the reparameterized gradient.
result The new estimator has zero variance as the approximate posterior approaches the exact posterior.
U-statistics improve gradient estimation in importance-weighted variational inference.
problem High variance in gradient estimation for importance-weighted variational inference.
method Use U-statistics to average base gradient estimators on overlapping batches of size m, achieving lower variance.
result U-statistic variance reduction leads to modest to significant improvements in inference performance.
Machine learning reduces variance in online experiment results.
problem Reducing variance in randomized controlled trials.
method Machine learning regression-adjusted treatment effect estimator (MLRATE).
result MLRATE reduces estimator variance by over 70% in A/A tests.
New unbiased variance estimator for random forests using Hoeffding decomposition.
problem Uncertainty quantification in random forests with large kernel sizes and small sample sizes.
method Proposes a new Hoeffding decomposition view for variance estimation, establishing unbiased estimators and ratio consistency.
result Establishes the ratio consistency of the proposed variance estimator, justifying confidence interval coverage rates.
Fourier analysis improves REINFORCE for binary models.
problem Improving gradient estimation for binary latent variable models.
method Connecting Fourier spectrum of Boolean functions to REINFORCE and developing low-variance unbiased gradient estimators.
result REINFORCE estimates degree-1 Fourier coefficients of a Boolean function.
Two Fisher information matrix estimators are analyzed for neural networks, focusing on their variances and trade-offs.
problem Estimating the Fisher information matrix in neural networks due to its high computational cost.
method Examined two popular diagonal Fisher information matrix estimators and their variances in neural networks for regression and classification.
result The variances of the estimators depend on the non-linearity with respect to different parameter groups and should not be neglected.
New calibration methods improve fitting of weak variance-alpha-gamma process.
problem Improving fitting of a multivariate Lévy process.
method Comparison of three calibration methods: method of moments, maximum likelihood estimation, and digital moment estimation.
result Maximum likelihood estimation produces a better fit when a specific condition holds, while digital moment estimation produces a better fit when the condition is violated.
A new method eliminates reward estimation variance in sequential decision processes.
problem High variance in gradient estimation hinders sample efficiency in reinforcement learning.
method Proposes an unbiased method that completely eliminates variance under certain conditions.
result The proposed method significantly improves performance in challenging problems with delayed rewards.
Novel method estimates CV-based classifier performance variance.
problem Lack of rigorous variance estimation methods for CV-based classifiers.
method Influence Function (IF) approach to estimate variance of CV-based estimators.
result IF-based method shows small RMS error with some bias, but ad-hoc methods still perform better.