New method estimates volatility for processes with jumps of unbounded variation.
problem Estimating volatility of processes with jumps of unbounded variation.
method Developed a new volatility estimator using debiasing of truncated realized quadratic variation.
result Method outperforms existing alternatives in simulations.
New method estimates volatility for Lévy processes with unbounded jumps efficiently.
problem Efficient estimation of volatility for Lévy processes with unbounded jumps.
method Developed a new estimator based on high-order expansions of truncated moments.
result Method outperforms existing alternatives in estimating volatility.
CVIB uses information theory to learn counterfactuals from MNAR data without RCTs.
problem Debiasing learning from missing-not-at-random (MNAR) data in recommendation systems.
method CVIB, a variational information bottleneck, separates task-aware mutual information into factual and counterfactual parts.
result CVIB significantly enhances both shallow and deep models in recommendation systems.
A new pricing controller handles resource constraints to infer target prices effectively.
problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.
Proposes a three-stage debiasing framework to improve out-of-distribution accuracy.
problem Inaccurate uncertainty estimations in bias-only models damage ensemble-based debiasing performance.
method Calibrates the bias-only model to improve its uncertainty estimations, creating a three-stage ensemble-based debiasing framework.
result The three-stage debiasing framework consistently outperforms traditional methods in out-of-distribution accuracy.
Paper develops PGMM framework for debiased inference on nonparametric IV estimators.
problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.
D3M debiases models by selectively removing problematic examples.
problem Model failures on underrepresented subgroups.
method Isolates and removes specific training examples that cause failures.
result Efficiently trains debiased classifiers with minimal example removal.
Paper debiases multiple word embedding biases simultaneously.
problem Reduction of multiple biases in word embeddings.
method Joint multiclass debiasing approach using Word Embeddings Association Test (WEAT).
result Demonstrates reduction or complete elimination of bias in word embeddings.
We consider a square-integrable semimartingale and investigate the convex order relations between its discrete, continuous and predictable quadratic variation. As the main results, we show that if the semimartingale has conditionally independent increments and symmetric jump measure, then its discrete realized variance…
A new method debiases multiple target parameters without IFs.
problem Debiasing multiple target parameters in nonparametric models.
method Kernel Debiased Plug-in Estimation (KDPE) using TMLE and reproducing kernel Hilbert spaces.
result KDPE simultaneously debiases all pathwise differentiable target parameters.
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
New learning rules achieve optimal sample complexity for weakly supervised classification.
problem Learning from label proportions in weakly supervised settings.
method Debiased proportional square loss and EasyLLP learning rule.
result Achieves optimal sample complexity in both realizable and agnostic settings.
A self-supervised debiasing method using rank regularization mitigates spurious correlations in neural networks.
problem Spurious correlations cause biases in deep neural networks, affecting generalization.
method Spectral analysis of latent representations, rank regularization, self-supervised pretraining, debiasing of downstream tasks.
result The proposed framework significantly improves generalization performance and outperforms supervised debiasing approaches.
A new debiasing method for high-dimensional regression with applications to PCR.
problem Debiasing in high-dimensional statistics with i.i.d. samples and sub-Gaussian covariates.
method Spectrum-Aware Debiasing using rescaled gradient descent with spectral information.
result Achieves debiasing in broader contexts with structured dependencies, heavy tails, and low-rank structures.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
A debiasing method improves nonparametric regression's statistical properties.
problem Lack of theoretical guarantees for modern nonparametric regression methods.
method Model-free debiasing method incorporating a correction term.
result Debiased estimator satisfies pointwise and uniform risk convergence, asymptotic normality.
Develops methods for structured variational inference with star-structured models.
problem Inference in models with interdependent variables.
method Star-structured variational inference, existence, uniqueness, self-consistency proofs, approximation error bounds, gradient-based algorithm.
result First results for existence, uniqueness, and self-consistency of variational approximations in star-structured models.
Study improves statistical inference for CATEs using Lasso and DML.
problem Estimating and inferring CATEs in high-dimensional settings.
method Doubly robust estimator, Lasso regularization, debiased Lasso, DML.
result TDL (triple/debiased Lasso) achieves n \sqrt{n} n -consistency and confidence intervals. Develops a direct debiased machine learning framework using Bregman divergence.
problem Reduces bias in machine learning estimates of causal effects or structural models.
method Neyman targeted estimation and generalized Riesz regression using Bregman divergence.
result Improves estimation of parameters of interest in causal models.
Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.
problem High-dimensional data challenges classical ridge regression's sparsity detection and bias issues.
method Debiasing and thresholding ridge regression, introducing a wild bootstrap for confidence regions and hypothesis testing, and a hybrid bootstrap for prediction intervals.
result Debiased and thresholded ridge regression can offer similar performance to thresholded Lasso and may be preferable in some settings.
DKGM improves image quality by debiasing kernel-based models.
problem Improving image quality in generative models without losing detail.
method Two-stage framework: KDE for density estimation, debiasing algorithm inspired by stochastic approximation.
result DKGM achieves comparable image quality to state-of-the-art models on CIFAR10.
A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.
problem Statistical inference for low-dimensional parameters in high-dimensional linear regression models.
method Mean-field variational Bayes approach, focusing on nuisance parameters and conditional distributions.
result Competitive numerical performance and theoretical guarantees for estimation and uncertainty quantification.
Paper develops a distributed debiased estimator for sparse statistical inference.
problem High computational costs in debiased estimator construction for high-dimensional models.
method Develops a multi-round distributed debiased estimator using both labeled and unlabelled data.
result Unlabeled data improves statistical rate of each iteration in distributed setup.
We simplify debiasing of LASSO estimates for practical computation.
problem Bias in LASSO estimates for high-dimensional data.
method Reformulate optimization problem to compute debiasing matrix W directly.
result Efficient closed-form solution for W with unique optimal solution.
COMMOD debiases models with minimal and interpretable changes.
problem Inconsistent and costly model updates in fair machine learning.
method Introduced COMMOD, a novel algorithm for algorithmic fairness that minimizes changes and makes them interpretable.
result COMMOD achieves comparable performance to state-of-the-art debiasing methods while making minimal and interpretable changes.
Bayesian inference corrected for bias in high-dimensional models.
problem Bayesian inference for high-dimensional regression models often produces biased credible sets.
method Debiasing approach based on Bernstein-von Mises theorem.
result Frequentist validity of debiased Bayesian posterior.
We propose a new method of measuring the third and fourth moments of return distribution based on quadratic variation method when the return process is assumed to have zero drift. The realized third and fourth moments variations computed from high frequency return series are good approximations to corresponding actual …
A theorem for debiasing machine learning with finite sample guarantees.
problem Calculating confidence intervals for machine learning functionals.
method Debiased machine learning based on bias correction and sample splitting.
result Nonasymptotic debiased machine learning theorem with finite sample guarantees.
ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation
problem Generative models for counterfactual outcomes
method ADIGen combines Riesz regression, causal invariance, and orthogonal statistical learning
result ADIGen controls counterfactual risk under general interventions
New method improves robustness of neural network-based debiasing.
problem Improving robustness of neural network-based debiasing.
method Moment-constrained learning for neural networks.
result Improved performance compared to state-of-the-art benchmarks.
Mitigates confirmation bias in SSL by adjusting pseudo labels dynamically.
problem Confirmation bias in semi-supervised learning leads to errors in pseudo labels.
method TaMatch framework adjusts scaling ratio to debias pseudo labels and dynamically adjusts target distribution.
result TaMatch significantly outperforms existing methods in SSL tasks.
Asymptotic analysis of short-maturity options on realized variance in local-stochastic volatility models.
problem Analyzing the behavior of short-maturity options on realized variance in local-stochastic volatility models.
method Large deviations theory and variational problems to solve rate functions for different cases.
result Explicit solutions for the rate function in the uncorrelated case and upper/lower bounds and expansions for the correlated case.
Automatic debiasing for causal and policy effects using Neural Nets and Random Forests.
problem Estimating causal and policy effects from high-dimensional or non-parametric regression functions.
method Automatic learning of Riesz representation using Neural Nets and Random Forests.
result Automatic debiasing method performs well compared to state-of-the-art algorithms.
Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection induces memory and correlation in the samples, and poses significant challenge. We consider the high-dimensional linear regression, where the s…
New debiasing methods for fine-tuned neural networks.
problem Bias in neural networks used for high-stakes decisions.
method Intra-processing methods designed for fine-tuned models.
result Demonstrated effectiveness of intra-processing methods across various datasets.
We provide a constructive, variational proof of Rivin's realization theorem for ideal hyperbolic polyhedra with prescribed intrinsic metric, which is equivalent to a discrete uniformization theorem for spheres. The same variational method is also used to prove a discrete uniformization theorem of Gu et al. and a corres…
We introduce wavelet-based methodology for estimation of realized variance allowing its measurement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator…
Unified theory and debiasing framework for random oblique projections in high dimensions.
problem Systematic statistical bias in random oblique projections induced by sampling.
method Unified non-asymptotic theory and debiasing framework.
result Sharp bias--variance characterizations and improved approximation accuracy.
We discuss the probabilistic properties of the variation based third and fourth moments of financial returns as estimators of the actual moments of the return distributions. The moment variations are defined under non-parametric assumptions with quadratic variation method but for the computational tractability, we use …
MAPPING debiases GNNs for fair node classification with limited leakage.
problem Graph Neural Networks inherit and exacerbate historical discrimination in high-stake domains.
method MAPPING uses distance covariance-based fairness constraints and adversarial debiasing.
result MAPPING achieves better trade-offs between fairness and utility, mitigating privacy risks.
Javanmard and Montanari propose a debiased estimator for high-dimensional regression.
problem Bias in high-dimensional regression models.
method Debiased LASSO estimator.
result Debiased LASSO yields asymptotically normal estimators and valid hypothesis tests.
New method for debiased inference without assuming exact solutions in inverse problems.
problem Dealing with inverse problems where exact solutions may not exist.
method Nonparametric instrumental variable analysis without structural equations.
result Valid inference on functionals of inverse problems without assuming exact solutions.
New models can't beat existing ones, so debiasing methods only slightly reduce needed labels.
problem Limiting scalability in model evaluation due to self-preferencing biases.
method Study of debiasing methods using a few high-quality labels to reduce model judgments.
result Debiasing methods can't decrease required ground truth labels by more than half when the judge is no more accurate than the model.
ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.
problem Improving debiased machine learning and policy effects estimation.
method Score matching and Riesz representer estimation.
result Estimates policy path for continuous treatments, improving interpretability.
New method neutralizes gender bias in word embeddings without losing semantic information.
problem Gender biases in word embeddings trained on human-generated corpora.
method Latent Disentanglement and Counterfactual Generation with siamese auto-encoder and gradient reversal layer.
result Our method outperforms existing debiasing methods in preserving semantic information and neutralizing gender biases.
VOLARE provides standardized realized volatility measures from financial data.
problem Lack of standardized realized volatility measures from ultra-high-frequency data.
method Asset-specific pipeline for cleaning and sampling data, providing a wide range of realized estimators.
result Comprehensive set of realized estimators for equities, exchange rates, and futures.
New method debiases selection bias in PU classification with exposure data.
problem Binary classification from positive and unlabeled data with selection bias.
method Automatic Debiased PUE (ADPUE) learning method.
result ADPUE outperforms traditional PU learning methods on various datasets.
Semi-supervised learning debiased for better performance.
problem Bias in SSL methods impairs theoretical guarantees and empirical performance.
method Simple debiasing approach to remove risk estimate bias.
result Debiased SSL methods provide better calibrated models.