On-device research index

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.

168,742 papers · 148 categories

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48 results for “misspecified priors”

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 ↗

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 ↗

Metalearning Linear Bandits by Prior Updatestat.ML

Bayesian metalearning improves performance in linear bandits with misspecified priors.

problem Improper priors lead to suboptimal performance in sequential decision-making.

method Proves performance bounds for metalearning priors in stochastic linear bandits and develops a metalearning algorithm.

result Metalearning can improve performance by learning the prior from multiple tasks.

Bayesian statistics sequential decision-making metalearning linear bandits regret bounds

2021-07-12abs ↗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 ↗

Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integrationstat.ML

Statsformer validates and adapts LLM-derived semantic priors for improved supervised learning.

problem Unreliable semantic priors from LLMs can degrade supervised learning performance.

method Adapts LLM-derived feature scores into a family of learner-specific prior-injection mechanisms, calibrating their influence using out-of-fold validation.

result Improves prediction performance by adaptively downweighting unreliable LLM priors, ensuring a guardrailed statistical learning system.

LLM priors supervised learning semantic integration out-of-fold validation oracle guarantee

2026-01-29abs ↗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 ↗

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 ↗

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 ↗

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 ↗

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 ↗

Rates of Convergence of Generalised Variational Inference Posteriors under Prior Misspecificationmath.ST

Study improves convergence rates for GVI under prior misspecification.

problem Improving convergence rates for GVI under prior misspecification.

method Proves rates of convergence and robustness to prior misspecification in GVI framework.

result Establishes sufficient conditions for existence and uniqueness of GVI posteriors.

Generalised Variational Inference rates of convergence prior misspecification Polish spaces concentration of measures

2025-10-03abs ↗pdf ↗

Large-Scale Model Selection with Misspecificationstat.ME

Model selection is crucial to high-dimensional learning and inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality. Yet both …

2018-03-17abs ↗pdf ↗

Rényi Neural Processescs.LG

Rényi Neural Processes replace KL divergence with Rényi divergence to improve NP performance.

problem Parameterization coupling in Neural Processes leads to prior misspecification.

method Propose Rényi Neural Processes (RNP) by replacing KL divergence with Rényi divergence.

result Significant performance improvements in real-world problems, including better log-likelihoods.

Neural Processes Rényi divergence Parameterization coupling Prior misspecification Log-likelihood improvements

2024-05-25abs ↗pdf ↗

Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Controlmath.OC

New method reduces over-pessimism in Bayesian control under parameter uncertainty.

problem Over-pessimism in Bayesian control due to misspecified priors.

method Distributionally robust Bayesian control (DRBC) with strong duality and optimization.

result Validated algorithm on synthetic and real data, reducing over-pessimism.

Bayesian control distributionally robust policy evaluation parameter uncertainty duality

2025-06-24abs ↗pdf ↗

Residual Pathway Priors for Soft Equivariance Constraintscs.LG

RPPs improve deep learning models with soft equivariance constraints.

problem Balancing expressiveness and inductive biases in deep learning.

method Introducing Residual Pathway Priors (RPPs) to convert hard constraints into soft priors.

result RPPs enable models to learn structured solutions while retaining flexibility.

Residual Pathway Priors Soft Equivariance Deep Learning Inductive Biases Equivariance

2021-12-02abs ↗pdf ↗

Gaussian Imagination in Bandit Learningcs.LG

Gaussian prior and likelihood improve bandit learning performance.

problem Improving bandit learning with misspecified Gaussian distributions.

method An agent with a bounded information ratio interacts with a Bernoulli bandit based on a Gaussian prior and likelihood.

result The regret increase is at most linear in the square-root of the time horizon for diffuse distributions.

bandit learning Gaussian distributions misspecification regret analysis

2022-01-06abs ↗pdf ↗

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 ↗

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

The paper examines Gaussian process means under misspecified likelihoods and smoothness.

problem Accuracy of Gaussian process approximations under misspecified smoothness and likelihood.

method Analysis of Gaussian process properties under misspecified conditions.

result The accuracy of Gaussian process approximations is influenced by experimental design and kernel choice.

Gaussian processes misspecified likelihoods misspecified smoothness experimental design kernel hyperparameters

2020-01-29abs ↗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 ↗

The Interpolating Information Criterion for Overparameterized Modelsstat.ML

A new criterion selects models in overparameterized settings.

problem Model selection for overparameterized models with more parameters than data.

method Establishes Bayesian duality and introduces the Interpolating Information Criterion.

result The Interpolating Information Criterion selects models in overparameterized settings.

model selection overparameterized models interpolating estimators information criteria Bayesian duality

2023-07-15abs ↗pdf ↗

Random Effect Banditscs.LG

The paper improves bandit algorithms by incorporating random-effect models.

problem Improving statistical efficiency in multi-armed bandit problems with misspecified priors.

method Introduces a random-effect model to bandits, estimating arm means and designing a UCB algorithm ReUCB.

result Derives an upper bound on the Bayes regret of ReUCB, showing improved performance over Thompson sampling.

bandits regret minimization random effects UCB algorithm Bayes regret

2021-06-23abs ↗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 ↗

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 ↗

Active Learning via Regression Beyond Realizabilitycs.LG

New active learning framework for multiclass classification beyond realizability assumption.

problem Active learning in non-realizable settings with convex model classes.

method Surrogate risk minimization, epoch-based fitting, aggregation of models.

result Achieves label and sample complexity comparable to prior work in non-realizable settings.

active learning multiclass classification surrogate risk non-realizability convex models

2025-05-31abs ↗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 ↗

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 ↗

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 ↗

Less Greedy Equivalence Searchcs.LG

LGES speeds up causal discovery while maintaining accuracy.

problem Causal discovery from observational data with computational and accuracy limitations.

method LGES modifies GES by avoiding certain edge insertions, using prior knowledge, and leveraging interventional data.

result LGES outperforms GES in speed, accuracy, and robustness to misspecified knowledge.

causal discovery equivalence search greedy algorithm sample limit computational cost

2025-06-27abs ↗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 ↗

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 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 ↗

The fine print on tempered posteriorscs.LG

New insights on Bayesian models show stochasticity doesn't always improve accuracy.

problem The impact of stochasticity in Bayesian models and the role of temperature parameters.

method Detailed investigation, empirical testing, and PAC-Bayesian analysis.

result Stochasticity does not generally improve test accuracy and the coldest temperature is often optimal.

Bayesian models stochasticity temperature PAC-Bayesian Frequentist metrics

2023-09-11abs ↗pdf ↗

Active Regression by Stratificationstat.ML

We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se…

2014-10-22abs ↗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 ↗

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 ↗

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 ↗

Comments on the Du-Kakade-Wang-Yang Lower Boundscs.LG

Researchers compare lower bounds and tractability in reinforcement learning.

problem Intractability of reinforcement learning with misspecified representations.

method Comparison of lower bounds and eluder dimension approach.

result Reconciliation of interpretations between different lines of work.

reinforcement learning sample complexity misspecified representation eluder dimension

2019-11-18abs ↗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 ↗

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 ↗

Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Meansstat.ML

Bayes-assisted confidence sequences improve efficiency for bounded means.

problem Efficient uncertainty quantification for bounded IID means without parametric assumptions.

method Bayesian working predictive model selects adaptive martingale updates maximizing predictive log-growth.

result Asymptotically log-optimal performance with informative priors reducing width and sampling effort.

Bayesian statistics confidence sequences empirical likelihood Wasserstein consistency Dirichlet-process mixtures

2026-05-08abs ↗pdf ↗

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrapstat.ML

Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally intensive, is serial in nature and mixes poorly between posterior modes. Further, all models are misspecified, which brings into question the val…

2019-02-08abs ↗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 ↗

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 ↗

On Equivariant Model Selection through the Lens of Uncertaintycs.LG

New method selects equivariant models using uncertainty metrics.

problem Selecting equivariant models among pretrained ones with varying symmetry biases.

method Uncertainty-aware model selection using frequentist, Bayesian, and calibration-based measures.

result Bayesian model evidence often misaligns with predictive performance.

equivariant models model selection uncertainty symmetry Bayesian

2025-06-23abs ↗pdf ↗

Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Banditscs.LG

New algorithms for best arm identification in bandits robust to misspecified parameters.

problem Inconsistent learning performance of traditional MAB algorithms when parameters are misspecified.

method Proposes two classes of asymptotically near-optimal algorithms for statistically robust MAB under fixed-budget pure exploration.

result Establishes fundamental performance limits and proposes algorithms that are asymptotically near-optimal.

multi-armed bandits risk-averse best arm identification statistical robustness misspecified parameters

2020-08-28abs ↗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 ↗

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 ↗