Simplified tutorial on doubly robust learning for causal inference.
problem Challenges in applying doubly robust methods due to complexity and software barriers.
method Combines propensity score and outcome modeling for robust causal inference.
result Makes doubly robust learning accessible through simplified methodology and practical examples.
Proposes DR-ACI for causal effect intervals with temporal dependence.
problem Causal effect intervals under temporal dependence.
method Doubly robust adaptive conformal inference (DR-ACI).
result Constructs prediction intervals for causal effects.
Enhances robustness in experimental design through Generalised Bayesian inference.
problem Poor inference and estimates of information gain when statistical model is incorrectly specified.
method Generalised Bayesian (Gibbs) inference framework applied to experimental design.
result GBOED enhances robustness to outliers and incorrect assumptions about noise distribution.
Adversarial robustness of amortized Bayesian inference is studied, showing it can be improved.
problem Adversarial robustness of amortized Bayesian inference.
method Simulation-based estimation, regularization scheme based on Fisher information.
result Adversarial robustness can be improved with a regularization scheme.
Proposes two-stage robust and sparse distributed inference for large-scale data.
problem Statistical inference in large-scale, high-dimensional, and outlier-contaminated data.
method Two-stage approach: model selection with robust Lasso, fusion of local selections, and bootstrap methods for inference.
result Robust and computationally efficient inference procedures for variable selection, confidence intervals, and standard deviation approximations.
Improved diffusion sampling for inverse problems with faster and more robust inference.
problem High computational cost and lack of robustness in diffusion posterior sampling.
method Amortized variational inference with explicit likelihood guidance.
result Improved trade-off between inference speed and robustness to unseen degradations.
Unified framework improves robust causal inference, overcoming Gaussian barriers and optimization issues.
problem Improving robust causal inference in non-Gaussian settings.
method Combines gamma-Divergence, GNC, and Gatekeeper mechanism.
result Enhanced robustness and global optimization in causal effect estimation.
New Stein operator improves robustness in model inference.
problem Improving robustness in inference for unnormalized models.
method Density-power weighted Stein operator (γ-Stein operator). result Robust methods for goodness-of-fit testing and posterior approximation.
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.
Paper proposes a new method for selective inference in robust regression.
problem Statistical inference after removing outliers identified by robust methods.
method Conditional SI using piecewise-linear homotopy continuation.
result Proposed method is applicable to a wide class of robust regression and outlier detection methods.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
problem Determining causal directionality in quantum systems under MNAR observation.
method Integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
result Achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics.
New method improves robustness and efficiency of inference from complex models.
problem Inability of existing methods to handle outliers in simulator-based models.
method Generalised Bayesian inference with neural approximation of weighted score-matching loss.
result Provable robustness to outliers and computational efficiency.
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.
Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.
problem Byzantine robustness in distributed learning systems.
method Variance reduced median-of-means (VRMOM) estimator for Byzantine robust distributed inference.
result Achieves a fast convergence rate with only a constant number of rounds of communications.
New method improves active statistical inference by reducing noise.
problem Inaccurate uncertainty estimates in active sampling lead to noisy results.
method Robust sampling strategies that interpolate between uniform and active sampling based on uncertainty scores.
result The robust sampling ensures that the estimator is never worse than uniform sampling and usually outperforms active inference.
New method uses constrained transport metric for robust Bayesian inference.
problem Flexible Bayesian models with many uninterpretable parameters.
method Exponentially tilted empirical likelihood with a novel Wasserstein metric, combined with a prior.
result Superior performance compared to state-of-the-art robust Bayesian inference methods.
Plug-in robust NPE method adapts summaries independently of pretrained NPE.
problem Misspecification of neural posterior estimators under test data distribution.
method Minimum-distance summaries using maximum mean discrepancy (MMD).
result Substantial robustness gains with minimal additional overhead.
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.
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.
Bayesian inference uses Stein discrepancy for robustness in intractable likelihoods.
problem Intractable likelihoods in Bayesian inference.
method Generalised Bayesian inference with Stein discrepancy as the loss function.
result Robust generalised posteriors with closed form or accessible using MCMC.
Paper develops robust methods for panel data with latent groups, improving inference under group separation violations.
problem Inference in latent group panel models under group separation violations.
method Selective conditional inference approach to derive conditional distribution of coefficients given estimated group structure.
result Valid inference under violations of group separation, superior to traditional asymptotic methods.
DM framework improves robustness and efficiency in latent-mixture models.
problem Efficient and robust inference in latent-mixture models.
method Divergence-minimization framework with monotonic convergence and robustness guarantees.
result DM yields consistent and asymptotically normal estimators under correct specification.
Extends robust methods for causal inference, improving estimator performance.
problem Estimating causal effects in the presence of latent confounders.
method Minimax kernel machine learning for doubly robust functionals.
result Proposed method leads to robust and high-performance estimators.
Paper develops robust policy evaluation for reinforcement learning with outlier and heavy-tailed rewards.
problem Outlier contamination and heavy-tailed rewards in reinforcement learning.
method Develops a fully online robust policy evaluation procedure and efficient statistical inference.
result Establishes the Bahadur-type representation of the estimator and develops an online inference procedure.
Enhances GNN robustness during inference using Conditional Random Fields.
problem Vulnerability of GNNs to adversarial attacks.
method Post-hoc approach using Conditional Random Fields (CRF).
result Improves robustness of GNNs across various models.
Robustness to outliers is a central issue in real-world machine learning applications. While replacing a model to a heavy-tailed one (e.g., from Gaussian to Student-t) is a standard approach for robustification, it can only be applied to simple models. In this paper, based on Zellner's optimization and variational form…
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.
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.
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…
New method improves Bayesian inference for parametric models, robust to misspecification.
problem Inference can be untrustworthy when parametric models are wrong.
method Adaptive nonparametric corrections for parametric Bayesian models using generalized Bayes.
result The method achieves robustness and efficiency, converging fast when the parametric model is close to true.
New estimators improve causal inference in machine learning studies.
problem Improving causal inference in machine learning models.
method Doubly-robust cross-fit estimators for average causal effect.
result Doubly-robust cross-fit estimators outperform other methods in simulations.
This work frames active inference through control as inference, offering robust control algorithms.
problem Active inference framework lacks practical sensorimotor control algorithms.
method Frame active inference through control as inference, presenting trajectory optimization as inference.
result AI may be framed as partially-observed CaI when the cost function is defined in observation states.
New algorithm improves Bayesian inference for complex models.
problem Poor performance of existing Bayesian approaches for simulators.
method Posterior bootstrap and maximum mean discrepancy estimators.
result Strong robustness and parallelizability of the new algorithm.
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.
AECF improves multimodal inference robustness and calibration.
problem Robustness and calibration issues in multimodal systems with missing inputs.
method Adaptive Entropy-Gated Contrastive Fusion (AECF) layer.
result Improves masked-input mAP by +18 pp at a 50% drop rate.
A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.
problem Incorrectly assuming normally distributed summary statistics in likelihood-free Bayesian inference.
method Wasserstein Gaussianization transformation combined with robust BSL and efficient Variational Bayes.
result Highly efficient and reliable approximate Bayesian inference for likelihood-free problems.
URSABench benchmarks Bayesian methods for deep learning models.
problem Scalability issues in Bayesian inference for deep learning.
method Open-source benchmark suite for assessing approximate Bayesian inference methods.
result Initial results show promise for addressing uncertainty and robustness in deep learning.
Paper extends prediction-powered inference using conformal prediction for robust and valid imputation.
problem Safe use of black-box ML models for imputing missing data with strong guarantees.
method Connecting prediction-powered inference with conformal prediction for valid and additional guarantees.
result First general prediction-powered procedure for e-values operating off-line.
Paper improves robustness of ABI with unlabeled data.
problem Robustness issues in ABI for out-of-simulation data.
method Semi-supervised approach using unlabeled data and self-consistency losses.
result Semi-supervised ABI with unlabeled data improves robustness significantly.
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
problem Challenges in Difference-in-Differences (DiD) estimation, especially heterogeneous treatment effects and non-linearities.
method Difference-in-Differences Bayesian Causal Forest (DiD-BCF) with PTA-based reparameterization.
result DiD-BCF provides superior performance and uncovers significant heterogeneity in treatment effects.
AMF-VI uses adaptive mixtures of flows for robust VI across diverse distributions.
problem Inconsistent behavior of single-flow models across different distributions.
method Sequential expert training of individual flows and adaptive global weight estimation via likelihood-driven updates.
result AMF-VI achieves lower negative log-likelihood and stable gains in transport metrics across various posterior families.
Adaptive learning method identifies and corrects corrupted data.
problem Robust learning from corrupted training sets.
method Identifies corrupted and non-corrupted samples with latent Bernoulli variables, formulates as likelihood maximization with marginalized latent variables, solved via variational inference and Expectation-Maximization.
result Improves over state-of-the-art by automatically inferring corruption level with minimal overhead.
Study improves robustness of Bayesian inference for cognitive models.
problem Outliers and contaminants affect parameter estimation in cognitive models.
method Robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks.
result Introducing contaminants from a Cauchy distribution increases robustness.
Researchers develop a new SMC sampler for Wishart processes to improve dynamic covariance inference.
problem Challenging inference of dynamic covariance in various scientific fields.
method Introduce Sequential Monte Carlo (SMC) sampler for the Wishart process.
result SMC sampling provides more robust estimates and out-of-sample predictions of dynamic covariance.
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.
Bayesian neural networks improve likelihood-free inference efficiency.
problem Efficient parameter inference from simulation models with uncertainty.
method Bayesian neural networks for summary statistics, adaptive sampling.
result More robust and efficient posterior estimation.
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mism…
Bayesian neural networks are vulnerable to adversarial attacks.
problem Adversarial robustness of Bayesian neural networks.
method Examination of adversarial robustness through three tasks: label prediction, adversarial example detection, and semantic shift detection.
result Bayesian neural networks are highly susceptible to adversarial attacks.