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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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48 results for “Misspecified Model”

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?stat.ML

The paper examines when importance weighting is needed for nonparametric and misspecified models.

problem When is importance weighting correction needed for covariate shift adaptation?

method Analysis of IW-corrected kernel ridge regression in various settings.

result The importance weighting correction is needed for nonparametric and misspecified models to obtain the best approximation of the true unknown function.

covariate shift importance weighting kernel ridge regression nonparametric models misspecified models

2023-03-07abs ↗pdf ↗

Decision-Focused Learning with Directional Gradientscs.LG

New PG losses improve decision optimization in misspecified models.

problem Improving decision optimization in models that are not perfectly specified.

method Introducing Perturbation Gradient (PG) losses to connect decision loss with directional derivatives and optimizing using gradient techniques.

result PG losses yield best-in-class policies asymptotically, even in misspecified settings.

decision optimization surrogate losses misspecified models directional derivatives gradient-based methods

2024-02-05abs ↗pdf ↗

Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Modelsstat.ML

Study non-asymptotic bounds for robust estimators under misspecified models.

problem Evaluate performance of robust estimators under adversarial conditions.

method Propose a general approach to adversarial risk analysis, including investigations on generalization and approximation errors.

result Establish non-asymptotic upper bounds for adversarial excess risk under Lipschitz loss functions.

Adversarial Risk Robust Estimation Misspecified Models Generalization Error Approximation Error

2023-09-02abs ↗pdf ↗

Conditional independence testing under misspecified inductive biasesstat.ML

New methods for CI testing under model misspecification.

problem Challenges in CI testing with misspecified models.

method Proposes new approximations and upper bounds for testing errors of regression-based CI tests.

result Introduces the Rao-Blackwellized Predictor Test (RBPT) robust against misspecified inductive biases.

conditional independence misspecified models regression-based tests testing errors Rao-Blackwellized Predictor Test

2023-07-05abs ↗pdf ↗

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscs.LG

Reward hacking exploits misspecified rewards, affecting agent capabilities and true performance.

problem Reward hacking in RL models exploiting reward misspecifications.

method Constructed four RL environments with misspecified rewards; analyzed agent capabilities and behavior.

result More capable agents exploit reward misspecifications, achieving higher proxy reward but lower true reward.

reward hacking misspecified rewards RL environments agent capabilities phase transitions

2022-01-10abs ↗pdf ↗

Robustness of Delta Hedging in a Jump-Diffusion Modelq-fin.MF

Suppose an investor aims at Delta hedging a European contingent claim $h(S(T))$ in a jump-diffusion model, but incorrectly specifies the stock price's volatility and jump sensitivity, so that any hedging strategy is calculated under a misspecified model. When does the erroneously computed strategy super-replicate the t…

2019-10-20abs ↗pdf ↗

Improving Mini-batch Optimal Transport via Partial Transportationstat.ML

Proposes m-POT to improve m-OT's misspecified mappings issue.

problem Misspecified mappings in mini-batch optimal transport.

method Partial optimal transport (POT) between mini-batch empirical measures.

result m-POT alleviates incorrect mappings compared to current methods.

mini-batch optimal transport partial transportation misspecified mappings domain adaptation color transfer

2021-08-22abs ↗pdf ↗

Improving the Accuracy of Amortized Model Comparison with Self-Consistencystat.ML

Self-consistency improves the accuracy of model comparison methods.

problem Improving the accuracy of model comparison methods when simulation models are misspecified.

method Supplement traditional simulation-based training with a self-consistency loss on unlabeled real data.

result Self-consistency training improves model comparison accuracy, especially in open-world scenarios.

model comparison amortized BMC self-consistency misspecified models unlabeled data

2025-08-28abs ↗pdf ↗

Probabilistic Digital Twin for Misspecified Structural Dynamical Systems via Latent Force Modeling and Bayesian Neural Networkscs.LG

Framework predicts responses in misspecified systems using GPLFM and BNNs.

problem Predicting responses in dynamical systems with model misspecification.

method Integrates GPLFM and BNNs for uncertainty-aware inference and prediction.

result Systematic propagation of uncertainty from diagnosis to prediction.

digital twin misspecified systems latent force modeling Bayesian neural networks dynamical systems

2025-11-27abs ↗pdf ↗

Sequential prediction under log-loss and misspecificationcs.LG

Study on sequential prediction with log-loss, focusing on well-specified and misspecified cases.

problem Sequential prediction with log-loss under different specification conditions.

method Analysis of cumulative regret in well-specified and misspecified cases for a Gaussian location hypothesis class.

result Cumulative regrets in well-specified and misspecified cases asymptotically coincide for the

d

-dimensional Gaussian location hypothesis class.

sequential prediction log-loss cumulative regret misspecified case PAC regret

2021-01-29abs ↗pdf ↗

Experimental Design for Non-Parametric Correction of Misspecified Dynamical Modelsstat.ML

We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system's evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithful…

2017-05-02abs ↗pdf ↗

Enhancing naive classifier for positive unlabeled data based on logistic regression approachstat.ML

Improved classifier for PU data using logistic regression.

problem Analysis of Positive Unlabeled data under SCAR assumption.

method Fitting misspecified logistic regression model to PU data.

result The classifier performs on par or better than competitors on real data sets.

Positive Unlabeled Data Logistic Regression Misspecified Model F1 Measure Naive Classifier

2023-06-05abs ↗pdf ↗

Uncertainty quantification using martingales for misspecified Gaussian processesstat.ML

New method improves GP uncertainty quantification for misspecified priors.

problem Uncertainty quantification for GPs under incorrect priors.

method Constructs a confidence sequence using martingale techniques.

result Empirically outperforms standard GP methods in robustness and utility for Bayesian Optimization.

Gaussian processes Bayesian optimization misspecified priors martingales confidence sequences

2020-06-12abs ↗pdf ↗

Robust utility maximization for diffusion market model with misspecified coefficientsq-fin.PM

The paper studies the robust maximization of utility of terminal wealth in the diffusion financial market model. The underlying model consists with risky tradable asset, whose price is described by diffusion process with misspecified trend and volatility coefficients, and non-tradable asset with a known parameter. The …

2009-11-16abs ↗pdf ↗

Misspecified Gaussian Process Bandit Optimizationcs.LG

New algorithms for optimizing functions with noisy feedback, even when the model is misspecified.

problem Optimizing a black-box function with noisy bandit feedback, especially when the model is misspecified.

method Developed two algorithms based on Gaussian process methods: EC-GP-UCB and Phased GP Uncertainty Sampling.

result Achieved optimal dependence on misspecification error without prior knowledge, and effective in stochastic contextual settings.

Gaussian process kernelized bandit misspecified model regret minimization black-box optimization

2021-11-09abs ↗pdf ↗

Improved Algorithms for Misspecified Linear Markov Decision Processescs.LG

Improved algorithm for misspecified MLMDPs with bounded regret and space/time complexities.

problem Misspecified linear Markov decision processes.

method Proposes an algorithm with three desirable properties: bounded regret, bounded space/time complexities, and no need for misspecification input.

result Regret scales as

K \max \{ \varepsilon_{ ext{mis}}, \varepsilon_{ ext{tol}} \}

, improving existing bounds.

misspecified MLMDP regret bounded complexity Sup-Lin-UCB contextual bandits

2021-09-12abs ↗pdf ↗

Convergence Analysis of Nyström Subsampling in Covariate Shift Adaptation for Misspecified casestat.ML

Nyström subsampling with Tikhonov regularization for covariate shift adaptation under misspecified case

problem Adaptation to misspecified covariate shift

method Regularized Nyström subsampling with Tikhonov regularization

result Upper bounds on excess risk

Nyström covariate shift misspecified Tikhonov regularization adaptation

2026-06-20abs ↗pdf ↗

Misspecified Nonconvex Statistical Optimization for Phase Retrievalstat.ML

Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models. To address this issue, we take a first step towards taming model misspecification by studying the high-dimensional sparse phase retrieval problem with misspecified link…

2017-12-18abs ↗pdf ↗

Plug-in Performative Optimizationcs.LG

Plug-in method improves performative prediction accuracy.

problem Learning under performative feedback with slow convergence rates.

method Plug-in performative optimization using models.

result Plug-in method can be superior to model-agnostic strategies.

performative prediction plug-in optimization misspecified models strategic classification performative risk

2023-05-30abs ↗pdf ↗

Statistical Inference for Misspecified Contextual Banditsstat.ML

Statistical inference for misspecified contextual bandits is challenging due to adaptivity issues.

problem Statistical inference for misspecified contextual bandits

method Inverse-probability-weighted Z-estimation framework

result Consistent and asymptotically normal estimator with sandwich variance estimator

contextual bandits statistical inference inverse-probability-weighted Z-estimation consistency

2026-06-21abs ↗pdf ↗

Addressing Misspecification in Simulation-based Inference through Data-driven Calibrationstat.ML

RoPE framework calibrates misspecified simulators for reliable inference.

problem Misspecification compromises reliability of simulation-based inference.

method Data-driven calibration using optimal transport and a small calibration set.

result RoPE framework improves inference accuracy and uncertainty calibration.

simulation-based inference misspecification calibration optimal transport deep generative modeling

2024-05-14abs ↗pdf ↗

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlocs.LG

New method improves uncertainty quantification for large batch sizes and misspecified models.

problem Challenges in tuning algorithms for accurate uncertainty quantification in large batch sizes and misspecified models.

method Proposes new discrete-time approximations to SGD and SGLD, proving error bounds for practical tuning.

result Quantitative, non-asymptotic error bounds for accurate predictions of covariance and autocorrelation time.

uncertainty quantification stochastic gradient descent Langevin dynamics large batch size misspecified models

2026-05-29abs ↗pdf ↗

Learning Robust Statistics for Simulation-based Inference under Model Misspecificationstat.ML

Proposes a method to improve SBI under model misspecification.

problem Unreliable inference from SBI methods under model misspecification.

method Introduces a regularized loss function to penalize statistics that increase model-data mismatch.

result Demonstrates superior performance and robust inference in misspecified scenarios.

Simulation-based inference Approximate Bayesian computation Neural posterior estimation Model misspecification Regularized loss function

2023-05-25abs ↗pdf ↗

Arbitrage Opportunities in Misspecified Stochastic volatility Modelsq-fin.PR

There is vast empirical evidence that given a set of assumptions on the real-world dynamics of an asset, the European options on this asset are not efficiently priced in options markets, giving rise to arbitrage opportunities. We study these opportunities in a generic stochastic volatility model and exhibit the strateg…

2010-02-26abs ↗pdf ↗

Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guaranteesstat.ML

Paper develops a method to predict spatial point processes with guarantees.

problem Predicting the number of events in space with uncertainty.

method Regularized method to learn spatial models with out-of-sample guarantees.

result Method provides valid prediction intervals even when model is misspecified.

spatial point process intensity function regularization out-of-sample guarantees misspecified model

2020-07-03abs ↗pdf ↗

Mean Square Prediction Error of Misspecified Gaussian Process Modelscs.LG

Nonparametric modeling approaches show very promising results in the area of system identification and control. A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios. Gaussian process regression represents one approach which provid…

2018-11-16abs ↗pdf ↗

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settingsmath.NA

This paper presents a convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with misspecified settings where a test integrand is less smooth than a Sobolev RKHS based on which a quadrature rule is constructed. We pr…

2017-09-01abs ↗pdf ↗

Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shiftstat.ML

Paper analyzes spectral algorithms under covariate shift, providing convergence rates.

problem Addressing distributional mismatch in regression models.

method Incorporates importance weights into spectral algorithms in RKHS.

result Establishes minimax-optimal convergence rates for misspecified cases.

spectral algorithms covariate shift misspecified regression nonparametric regression reproducing kernel Hilbert space

2025-09-05abs ↗pdf ↗

Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Modelsstat.ME

A new method improves efficiency in finding optimal personalized treatment rules.

problem Heteroscedasticity and misspecified treatment-free effect models affect optimal ITR estimation.

method E-Learning framework that accounts for covariate-treatment dependent variance of residuals.

result E-Learning framework improves efficiency of optimal ITR estimation.

Personalized treatment rules Heteroscedasticity Misspecification Efficiency Optimal ITR

2021-09-06abs ↗pdf ↗

If Not Now, Then When? Model Risk in the Optimal Exercise of American Optionsq-fin.MF

This paper analyzes model risk in American put options using Heston volatility model.

problem Model risk in optimal exercise of American put options.

method Benchmark methodology of Hull and Suo [2002], Heston stochastic volatility model, numerical finite difference methods.

result Optimal exercise behavior is influenced by stochastic volatility dynamics and return-volatility correlation, creating model risk.

model risk American options optimal exercise Heston model stochastic volatility

2026-03-20abs ↗pdf ↗

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationstat.ML

ACE improves GBI for simulators by approximating cost functions, making inference more efficient.

problem Inference for misspecified simulators is overly restrictive.

method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).

result ACE provides accurate cost predictions and more efficient inference.

simulation-based inference Bayesian inference misspecified models amortized cost estimation Hodgkin-Huxley model

2023-05-24abs ↗pdf ↗

Inject Machine Learning into Significance Test for Misspecified Linear Modelscs.LG

Paper presents a machine learning method to improve significance tests for misspecified linear models.

problem Misspecification of linear assumptions in social science models leads to inaccurate significance levels.

method Apply machine learning to fit ground truth function, calculate linear approximation, and adjust the estimator.

result The method significantly outperforms linear regression for non-linear ground truth functions.

machine learning significance test linear approximation misspecified models social science

2020-06-04abs ↗pdf ↗

PAC

^m

-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regimecs.LG

Improves Bayesian predictive performance in misspecified models.

problem Misspecification gap between inferential and predictive risks.

method Develops a multi-sample loss (PAC

^m

) to bridge the gap.

result Empirical study shows improved predictive distribution.

Bayesian inference misspecification generalization empirical risk predictive performance

2020-10-19abs ↗pdf ↗

Misspecified Phase Retrieval with Generative Priorsstat.ML

Study phase retrieval under misspecified models using generative priors.

problem Estimating signals from phase measurements with model misspecification.

method Two-step approach: spectral initialization followed by iterative refinement.

result Statistical rate of order

\sqrt{(k\log L)\cdot (\log m)/m}

under suitable conditions.

phase retrieval misspecification generative priors two-step approach spectral initialization

2022-10-11abs ↗pdf ↗

Preconditioned Robust Neural Posterior Estimation for Misspecified Simulatorsstat.ME

Preconditioned neural posterior estimation improves reliability in misspecified models.

problem Reliability issues in neural posterior estimation for misspecified models.

method Preconditioning with data-dependent weights and forest-proximity scores to stabilize and improve accuracy.

result Preconditioned robust neural posterior estimation increases stability and accuracy over standard methods.

simulation-based inference neural posterior estimation misspecified models preconditioning robustness

2026-02-20abs ↗pdf ↗

Outlier-robust Kalman Filtering through Generalised Bayesstat.ML

Robust Kalman filtering method for outlier detection.

problem Outliers and misspecified measurement models in state-space models.

method Combines generalised Bayesian inference with Kalman filters for robustness and efficiency.

result Matches or outperforms other robust filtering methods at lower computational cost.

Kalman filtering robustness Bayesian inference outliers misspecified models

2024-05-09abs ↗pdf ↗

Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?cs.LG

Current OOD benchmarks overestimate model robustness to spurious correlations.

problem Spurious correlations degrade OOD performance, but benchmarks show the opposite.

method Analyze OOD datasets for spurious correlations and derive conditions for robustness.

result Current OOD benchmarks are misspecified and overestimate model robustness.

OOD benchmarks spurious correlations robustness model evaluation distribution shifts

2025-03-31abs ↗pdf ↗

Bayesian decision-making under misspecified priors with applications to meta-learningcs.LG

Bayesian algorithms perform well even with misspecified priors, especially in meta-learning.

problem Performance degradation of Bayesian algorithms with misspecified priors.

method Thompson sampling and meta-learning analysis with misspecified priors.

result Thompson sampling's performance degrades gracefully with misspecification, with a bound of

ilde{\mathcal{O}}(H^2 ε)

Bayesian decision-making Thompson sampling meta-learning misspecified priors performance degradation

2021-07-03abs ↗pdf ↗

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothnessmath.ST

Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultaneously quantifying uncertainty. However, this is only true when the model is well-specified, which is often not the case in practice. In thi…

2020-01-29abs ↗pdf ↗

Learning Curves of Stochastic Gradient Descent in Kernel Regressionstat.ML

Paper analyzes SGD in kernel regression, showing it outperforms offline methods.

problem Performance of SGD in kernel regression compared to offline methods.

method Analyzes Stochastic Gradient Descent (SGD) in kernel regression under misspecified models.

result SGD achieves min-max optimal rates up to constants, avoiding saturation.

SGD Kernel Regression Stochastic Gradient Descent Optimal Rates Misspecified Models

2025-05-28abs ↗pdf ↗

Sequential Structure-Sensitive Residual Diagnostics for PDE Inverse Problemsstat.ME

New diagnostic method detects misspecified models in inverse PDE problems.

problem Misleading residual-norm diagnostics in inverse PDE problems.

method Structure-sensitive sequential diagnostic using e-processes.

result Rejects fitted models that produce biased predictions.

inverse problems PDE residual diagnostics structured errors e-processes

2026-07-02abs ↗pdf ↗

Model Selection in High-Dimensional Misspecified Modelsmath.ST

Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implicitly that the model is correctly specified or of fixed dimensions. Yet model misspecification and high dimensionality are common in real app…

2014-12-23abs ↗pdf ↗

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiationcs.LG

Proposes a new reinforcement learning method to improve agent performance in control tasks.

problem Shortcomings of maximum likelihood estimation in model-based reinforcement learning.

method Directly optimizes expected returns using implicit differentiation of a Bellman optimality function.

result Empirical evidence shows improved performance in model misspecification regime.

reinforcement learning model-based control tasks implicit differentiation Bellman optimality

2021-06-06abs ↗pdf ↗

Adaptive Off-Policy Inference for M-Estimators Under Model Misspecificationstat.ME

Adaptive inference for $M$ -estimators in bandit data with model misspecification.

problem Challenges in off-policy inference for adaptively collected bandit data with a misspecified model.

method A novel approach to define a projected solution over a stationary evaluation policy, stabilizing variance with flexible methods.

result Valid inference for

M

-estimators in adaptive settings, even with unstable treatment policies.

off-policy inference adaptive data bandit algorithms model misspecification flexible approaches

2025-09-17abs ↗pdf ↗

Mislearning of Factor Risk Premia under Structural Breaks: A Misspecified Bayesian Learning Frameworkq-fin.PM

This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.

problem Investors' mislearning of factor risk premia under structural breaks in asset pricing models.

method Proposes a minimal Bayesian framework to study how investors learn under a misspecified model that underestimates structural breaks.

result Elevated mislearning is associated with stronger long-horizon returns and Sharpe ratios, consistent with an equilibrium premium for acute model uncertainty.

Bayesian learning factor risk premia structural breaks asset pricing mislearning

2026-03-23abs ↗pdf ↗

Analysis of regularized Nyström subsampling for regression functions of low smoothnessstat.ML

This paper studies a Nyström type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel. This case is less understood, in spite of its practical importance. To model su…

2018-06-03abs ↗pdf ↗

Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriorsstat.ML

Bayesian models for networks are often misspecified, leading to overconfident inference.

problem Real-world networks violate assumptions of geometry and link function in latent space models.

method Proposes a generalized posterior framework for random geometric graphs, using Link-Sequential R-SafeBayes to adaptively tune posterior regularization.

result Improved calibration and better link prediction performance demonstrated on synthetic and real-world networks.

Bayesian models latent space random geometric graphs misspecification calibration

2026-05-18abs ↗pdf ↗

Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressorsmath.ST

We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering th…

2019-06-10abs ↗pdf ↗