FedGVI improves FL robustness to model misspecification.
problem Limited robustness in FL approaches to model misspecification.
method Probabilistic Federated Learning framework that generalizes previous methods.
result FedGVI provides robust and calibrated predictions under model misspecification.
Paper tackles SBI under model misspecification, presenting robust strategies.
problem Challenges in SBI under model misspecification.
method Three key strategies: robust summary statistics, generalised Bayesian inference, and error modelling.
result Empirical results show vulnerabilities of SBI and effectiveness of misspecification-robust alternatives.
The paper tackles misspecification in contextual bandits by incorporating arm-specific variables.
problem Misspecification in contextual bandits due to unexplained inter-arm heterogeneity.
method Develops robust contextual bandit algorithms (RoLinUCB and RoLinTS) that incorporate arm-specific variables to address misspecification.
result The developed algorithms bound the n-round Bayes regret and show superior performance in various misspecification scenarios. UDA improves ABI robustness but fails under certain prior misspecifications.
problem Robustness of ABI in noisy real-world data.
method Systematic evaluation of UDA across various misspecification scenarios.
result UDA aligns summary spaces but can fail under prior misspecifications.
New method improves robustness of double robust estimators under complete misspecification.
problem Improper performance of double robust estimators when all nuisance functions are misspecified.
method DR+ACC, an adaptive correction clipping method.
result DR+ACC ensures bounded error and maintains semiparametric efficiency.
New framework improves robust inference in HMMs under model misspecification.
problem Inference in general state-space HMMs under likelihood misspecification.
method Generalized Bayesian Inference (GBI) and Sequential Monte Carlo (SMC) methods.
result Improved performance in object tracking and Gaussian process regression.
Improves DRO with Bayesian Ambiguity Sets for model misspecification.
problem Overly conservative decisions due to misspecified models in DRO.
method Introduces DRO-RoBAS with robust posterior predictive distribution.
result Outperforms other Bayesian and empirical DRO approaches in out-of-sample performance.
Unified framework for corruption-robust linear bandits with optimal gap-dependent misspecification bounds.
problem Effective learning in linear bandits with corrupted rewards across different corruption models.
method Unified framework for analyzing strong and weak corruption, connection to gap-dependent misspecification, and specialized algorithm.
result Optimal bounds for gap-dependent misspecification in linear bandits.
Adapts to misspecification in contextual bandits using offline regression.
problem Unexpected regret due to misspecified reward models.
method Adapts to misspecification by reverting to a safe policy when necessary.
result Regret guarantees degrade gracefully with misspecification level.
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.
Paper introduces RVNP to improve SBI in misspecified models.
problem Misspecification in simulation-based inference leads to unreliable posterior estimation.
method RVNP uses variational inference and error modeling to bridge the simulation-to-reality gap.
result RVNP can recover robust posterior inference without hyperparameters or priors.
Proposes a robust algorithm for aligning large language models with human preferences.
problem Misspecification in preference models, reference policies, and reward functions.
method Doubly robust preference optimization algorithm.
result Superior and more robust performance compared to state-of-the-art algorithms.
Unified framework for robust A/B testing under model misspecification.
problem Improving sample efficiency in A/B testing with model misspecification.
method Unified framework for contextual bandit and dynamic settings, proving worst-case mean squared error bounds.
result Empirically validated approach using synthetic and real-world datasets.
Algorithm mitigates performance loss in constrained reinforcement learning with model misspecification.
problem Performance loss in reinforcement learning policies due to model misspecification in constrained control systems.
method Proposes an algorithm to handle constrained model misspecification in continuous control systems.
result Algorithm successfully mitigates performance loss in real-world reinforcement learning tasks.
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.
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are …
Bayesian framework improves robustness in nonlinear regression models.
problem Measurement error, model misspecification, and distributional misspecification in regression analyses.
method Joint Dirichlet process prior on latent covariate-response distribution, updating with posterior pseudo-samples.
result Improved stability and consistency in estimators under increasing measurement error.
New algorithm mitigates misspecification amplification in regression models with covariate shift.
problem Distribution shift and model misspecification in regression models.
method Developed a new algorithm inspired by robust optimization to avoid misspecification amplification.
result No misspecification amplification while still achieving optimal statistical rates.
Enhances predictive models against misspecification and outliers.
problem Suboptimal generalization under misspecification and outliers.
method Combines PACm ensemble bounds with a generalized logarithm score function. result Produces predictive distributions resistant to both misspecification and outliers.
A new framework for performative prediction robust to distributional misspecification.
problem Performative prediction models can be influenced by their own predictions, leading to suboptimal outcomes.
method Introduces distributionally robust performative prediction (DRPO) to approximate the true performative optimum (PO) robustly.
result DRPO provides provable guarantees as a robust approximation to the true PO when the nominal distribution map is misspecified.
Paper develops a new estimator for dynamic treatment effects in high-dimensional settings.
problem Time-varying confounding and model misspecification in estimating dynamic treatment effects.
method Sequential model doubly robust estimator with moment-targeting estimates.
result Root-N inference achieved under model misspecification, even with high-dimensional covariates.
Robustifies elicitable functionals to handle small distribution misspecifications.
problem Determining uniquely optimal forecasts under distributional misspecification.
method Integrates statistical robustness into elicitable functionals using Kullback-Leibler divergence.
result Robust elicitable functionals admit unique solutions at the boundary of uncertainty regions.
In rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in RA arises since the homogeneity assumption fails to be satisfied in the complex real-world situation. Existing robust RAs usually resort to…
This paper compares VaR estimation methods under tail misspecification, finding importance sampling underestimates VaR.
problem Tail misspecification in VaR estimation.
method Importance sampling and moment-based VaR bracketing.
result Importance sampling underestimates VaR under heavy-tailed returns, while moment-based methods are robust.
Optimized α-posteriors reduce KL divergence from true posterior in parametric misspecification.
problem Reduction of KL divergence from true posterior in parametric model misspecification.
method Derivation of Bernstein-von Mises theorem and optimization of α-posteriors. result Optimized α-posteriors minimize KL divergence from true posterior, especially in severe misspecification. 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.
This work improves understanding of projection robust optimal transport distances.
problem Understanding the behavior of minimum Wasserstein estimators in high-dimensional and misspecified models.
method Adopting projection robust (PR) optimal transport, establishing statistical properties, proposing IPRW distance, and providing asymptotic guarantees.
result Established fundamental statistical properties and proposed new distances that outperform Wasserstein distances empirically.
SBI provides more accurate pole positions than chi-squared minimization in model misspecification.
problem Accurate pole position estimation in pi-pi scattering models.
method Simulation Based Inference (SBI) method compared to chi-squared minimization.
result SBI leads to more robust predictions of pole positions in models of pi-pi scattering.
Robust deep neural networks estimate multi-dimensional functional data robustly.
problem Estimating location function from multi-dimensional functional data robustly.
method Deep neural networks with ReLU activation, robust to outliers and model misspecification.
result Uniform convergence rates for robust deep neural network estimators.
We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penal…
Maximizes robustness in Bayesian experimental design under model uncertainty.
problem Brittleness of Bayesian experimental design under model misspecification.
method Formulates as a max--min game, uses Sibson's α-MI, and adopts PAC-Bayes framework.
result Establishes robust belief update and conditional information gain measure.
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.
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.
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm call…
Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
problem Simulation-based inference (SBI) of latent parameters is hindered by simulator misspecification.
method Misspecification-Aware Simulation-Based Inference (MA-SBI) turns side-channel text into a posterior correction.
result MA-SBI matches the oracle posterior across 10 seeds and two backbones.
Improved Bayesian inference via variational approximations of generalized rho-posteriors.
problem Robust Bayesian inference under model misspecification and data contamination.
method Introducing a modified ρ-posterior and using PAC-Bayesian analysis with variational approximations. result Theoretical guarantees for tractable inference with competitive robustness and computational efficiency.
Unified framework for robust linear predictions without distributional assumptions.
problem Robust linear predictions in the presence of outliers and model misspecification.
method Unified robust framework for linear prediction problems on Hilbert spaces, using Median of Means (MoM) approach.
result Achieves an error rate of \(O(\max\left\{|\mathcal{O}|^{1/2}n^{-1/2}, |\mathcal{I}|^{1/2}n^{-1}
ight\}+ε)\) for misspecification level \(ε\), matching best-known rates.
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…
Robust Bayesian inference improves model performance on discrete data.
problem Misspecification of discrete-valued models leads to poor inference and prediction.
method Total Variation Distance (TVD) for discrepancy, efficient estimator and inference method.
result Our approach significantly improves predictive performance on various data.
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.
New method improves neural posterior estimation for simulators.
problem Model misspecification in simulation data.
method Robust Neural Posterior Estimation (RNPE) to handle discrepancies.
result Naive NPE leads to unreliable inference, RNPE performs well.
New method improves experimental design under model misspecification.
problem Improving experimental design under time and budget constraints with model misspecification.
method Developed a new acquisition function (R-IDeA) that targets representativeness, informativeness, and de-amplification.
result The new method outperforms methods focusing on representativeness or informativeness alone.
Improved likelihood-free inference for high-dimensional models.
problem Challenges in likelihood-free inference for high-dimensional parameter spaces.
method Bayesian optimization-based approach with misspecification-robust characterisation.
result Efficient inference in 100-dimensional space with real data application.
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.
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.
Label smoothing improves model robustness against misspecification.
problem Improving model robustness against model misspecification.
method Introducing modified label smoothing (MLSLR) that maintains consistent probability estimation while modifying the loss function.
result MLSLR exhibits higher robustness against model misspecification than conventional label smoothing.
Study shows improper learning can outperform proper learning in misspecified models.
problem Misspecification in probabilistic prediction models.
method Investigates the performance of proper and improper learning strategies in misspecified models.
result Improper learning can achieve lower regret compared to proper learning, especially in high-dimensional settings.
Decision trees perform well in complex interactions, even when interactions are not fully accounted for.
problem Interpreting complex interactions in machine learning models.
method Experiments on datasets and two methods for robust GLMs.
result Tree depth compensates for model misspecification, enhancing performance in complex scenarios.