The paper introduces H"older--Bayes, a robust generalised Bayesian framework that jointly infers model parameters and the proportion of contamination without specifying an outlier distribution. By applying the H"older divergence to a scaled model, each posterior draw identifies observations least compatible with the fitted model, and averaging these selections propagates uncertainty to outlier‑detection scores. The authors provide theoretical guarantees—including uniform boundedness of the posterior influence function, an $n^{-1/2}$ bound on posterior‑averaged excess risk, and a Bernstein–von Mises theorem—while also demonstrating practical performance through simulations and real‑data experiments.
arXiv:2602. 19126v2 Announce Type: replace Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets.
arXiv:2606. 16524v1 Announce Type: new Abstract: Engineered robust losses such as Huber, Student-$t$, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupted.
The paper introduces SME-BETEL, a semiparametric Bayesian method that merges score matching estimating equations with Bayesian exponentially tilted empirical likelihood to perform inference on models with intractable normalizing constants. SME-BETEL avoids evaluating these constants and eliminates the need for learning-rate calibration, while providing consistency, asymptotic normality, and a Bernstein‑von Mises theorem that guarantees asymptotically calibrated credible sets even under model misspecification. The authors extend the framework to mixed‑domain data, enabling robust inference for doubly‑intractable models such as spatial preferential sampling, and demonstrate its effectiveness through simulations and an ozone‑monitoring application.
arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.
Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose Wasserstein Filtering (WF), a novel sample selection framework that discards a fraction of suspicious samples and estimates the target distribution using the empirical measure of the remaining data.
The paper introduces a distributionally robust framework for survival analysis that simultaneously tackles latent subpopulation shift and outlier contamination. It employs an outer minimization to refine the nominal distribution by down-weighting contaminated samples and an inner maximization to target the most challenging subpopulation, directly handling non-decomposable survival losses such as the Cox partial log-likelihood. An alternating gradient-based algorithm, guided by KKT conditions, is developed, and experiments on simulated data and two benchmarks show improved worst-group performance and stable training under contamination.
arXiv:2607. 04236v1 Announce Type: cross Abstract: Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees.
arXiv:2610.27085v1 Announce Type: cross Abstract: Scientific inference often involves observations from multiple latent populations whose class memberships are uncertain and must be inferred from ava...
The paper introduces a two‑layer framework for computer‑model calibration that directly controls predictive miscoverage. First, a conditional flow‑matching model learns an amortized posterior for calibration parameters using simulator outputs perturbed with Gaussian‑process discrepancies. Second, conformal risk control arranges posterior draws into nested candidate sets and selects the smallest set whose fixed‑rank pointwise bands achieve a prescribed time‑averaged miscoverage on a synthetic calibration sample. The method yields bands that are about 20% the width of traditional KOH‑MCMC bands while maintaining near‑nominal miscoverage, and when applied to an age‑structured SEIR model of the 2010 Malawi measles outbreak, the bands are sharper and highlight structural inadequacies in the mechanistic model.
arXiv:2404. 02141v5 Announce Type: replace-cross Abstract: In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates.
ProximalFM is a transformer‑based model that uses prior‑data fitted networks (PFNs) to perform Bayesian proximal causal inference under hidden confounding. By training on synthetic data generated from structural causal models with oracle counterfactuals, it amortizes the Bayesian operator inversion into a single forward pass, producing posterior estimates of the conditional average treatment effect (CATE). The approach consistently outperforms prior methods across various proximal regimes, especially when latent confounding is strong and proxy variables are weakly informative, and it requires no dataset‑specific tuning.
arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.