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.
A universal framework for constructing confidence sets using sequential likelihood mixing.
problem Constructing reliable confidence sets for realizable likelihood functions.
method Sequential likelihood mixing, integrating Bayesian inference and regret inequalities.
result Establishes fundamental connections and provable coverage guarantees for various inference techniques.
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.
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.
Active learning method for ABC statistics selection reduces expert work and improves posterior estimates.
problem Handling intractable likelihood functions in models with domain knowledge.
method Active learning method for selecting summary statistics in ABC.
result Better posterior estimates than existing methods, especially with limited simulation budget.
Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.
problem Cause-effect inference in location-scale noise models with misspecified noise distributions.
method Residual independence testing as an alternative to likelihood-based model selection.
result Residual independence testing is more robust to noise misspecification.
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.
Improves Bayesian predictive performance in misspecified models.
problem Misspecification gap between inferential and predictive risks.
method Develops a multi-sample loss (PACm) to bridge the gap. result Empirical study shows improved predictive distribution.
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.
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. Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure t…
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.
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.
SC improves robustness in model comparison for misspecified models.
problem Model misspecification challenges in amortized Bayesian inference.
method Parameter posterior-based methods augmented with SC training.
result SC improves robustness under model misspecification.
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this assumption to prove that AIC itself is asymptotically …
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelihood by a pseudo-likelihood, that is the exponential of a loss function enjoying suitable robustness properties. In this paper, we build a ps…
New method improves simulation-based inference by avoiding model misspecification.
problem Inefficient parameter estimation for models with intractable likelihoods.
method Proposes a robust SNL method with additional adjustment parameters.
result Demonstrates more accurate point estimates and uncertainty quantification.
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.
Develops FSC for maxima nominated samples, improving classification in rare-event data.
problem Combining labeled and unlabeled data in rare-event scenarios.
method Introduces a latent representation to account for maxima nomination sampling.
result Improves classification performance in rare-event contamination mixtures.
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.
A learning algorithm achieves logarithmic regret in a market making model.
problem Learning the price sensitivity parameter in a market making model.
method Maximum-likelihood estimator with regularization, based on HJB equation.
result Regret upper bound of order ln^2 T in expectation.
Volatility estimation based on high-frequency data is key to accurately measure and control the risk of financial assets. A Lévy process with infinite jump activity and microstructure noise is considered one of the simplest, yet accurate enough, models for financial data at high-frequency. Utilizing this model, we prop…
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.
Maximum likelihood estimator performance in logistic regression analyzed.
problem Performance of maximum likelihood estimator in logistic regression.
method Sharp non-asymptotic guarantees for existence and excess logistic risk.
result Sharp guarantees for the existence and excess risk of MLE in logistic regression.
In a regression setup with deterministic design, we study the pure aggregation problem and introduce a natural extension from the Gaussian distribution to distributions in the exponential family. While this extension bears strong connections with generalized linear models, it does not require identifiability of the par…
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate likelihood or fit a fast emulator model for efficient statistical inf…
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.
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.
MLE works best for covariate shift without modifications.
problem OOD generalization under covariate shift.
method Maximum Likelihood Estimation (MLE) without modifications.
result MLE achieves minimax optimality for covariate shift under well-specified setting.
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.
Jointly learns feature and sample relevancies for robust sparse recovery.
problem Sparse recovery sensitivity to data contaminants like outliers or misspecified noise.
method Jointly learns feature and sample relevancies via marginal likelihood optimization.
result Consistent sparse and robust prediction models across diverse tasks.
We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models. This framework addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneous multitask learning. Our work has three main …
Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortco…
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.
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
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.
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.
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.
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
problem Reliably quantifying predictive uncertainty for complex models
method Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty
result Asymptotically equivalent to a flat-prior Laplace approximation under correct likelihood specification, recovers robust sandwich covariance under misspecification
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…
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 Kmax{εextmis,εexttol}, improving existing bounds. 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…
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.
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.
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…
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.
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 …
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some consistent estimators have been proposed earlier, the problem of statistical efficienc…