Proposes robust ITRs integrating multiple datasets to handle posterior shift.
problem Posterior shift in conditional outcome distributions between source and target populations.
method Distributionally robust approach with closed-form solution and adaptive uncertainty tuning.
result Achieves superior performance compared to existing methods in simulations and real-data applications.
Bayesian inference for inverse problems using mean-shift interacting particles
problem Bayesian inference for inverse problems
method Amortized mean-shift interacting particles
result Improves accuracy of Bayesian inference by reducing the number of samples needed
Two approaches improve conformal Bayes for label shift, one post-hoc and one in-training.
problem Improving prediction sets for target domain under label shift.
method Two complementary approaches: post-hoc calibration and in-training adaptation.
result In-training adaptation achieves up to 43% width reduction at unchanged coverage.
SJS model predicts label shifts in multinomial datasets.
problem Predicting label shifts in multinomial datasets.
method Sparse joint shift model for dataset shift.
result Valid predictions and class prior probabilities estimates.
FJS method improves multinomial classification accuracy.
problem Improving multinomial classification accuracy under dataset shift.
method Derive FJS representation and propose alternative methods.
result Factorizable joint shift is not fully identifiable without additional assumptions.
Proposes a new method for conformal prediction under covariate shift with posterior drift.
problem Improving classification performance in target domains with limited training data.
method Weighted conformal classifier that leverages source and target samples.
result Demonstrates favorable asymptotic properties and practical utility.
Bayesian model averaging fails under covariate shift, affecting neural networks' performance.
problem Bayesian model averaging's failure in neural networks under covariate shift.
method Explained the issue and proposed novel priors to improve robustness.
result Bayesian model averaging is problematic under covariate shift, especially with linear feature dependencies.
Conformal Bayes under label shift: post-hoc calibration vs. in-training adaptation
problem Bayesian prediction sets under label shift
method Post-hoc calibration vs. In-training adaptation
result Both strategies achieve valid coverage equally in an unbiased training regime
DeepCCG adapts classifiers to representation shifts in one step.
problem Adapting classifiers to shifts in continuous representation.
method Empirical Bayesian approach using class conditional Gaussian classifier and KL divergence for selection.
result DeepCCG reduces performance change due to representation shifts.
Anchor-TS uses median anchoring to improve online decision-making from offline data with distribution shift.
problem Improving online decision-making from offline data with distribution shift.
method Sample-Mean Anchored Thompson Sampling (Anchor-TS) with median anchoring.
result Anchor-TS safely leverages offline data to accelerate online learning and reduces regret.
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…
The paper analyzes recalibration methods for binary classifiers under distribution shift.
problem Recalibrating binary classifiers to match a target prior probability.
method Analysis of distribution shift assumptions and proposal of new recalibration methods.
result QMM methods provide conservative results for risk weights functions.
Bayesian neural networks show complex posterior distributions that HMC can capture effectively.
problem Understanding and approximating the high-dimensional, non-convex posterior of Bayesian neural networks.
method Full-batch Hamiltonian Monte Carlo (HMC) on modern architectures.
result HMC provides a robust and comparable representation of the BNN posterior, with significant performance gains over standard training and deep ensembles.
Bayesian neural networks improve uncertainty quantification with unlabelled data.
problem Over-confidence in predictions on covariate-shifted data.
method Approximate Bayesian inference using posterior regularisation with pseudo-labels from unlabelled data.
result Significant improvement in uncertainty quantification accuracy on covariate-shifted data.
Bayesian framework estimates label shift for improved classifier performance.
problem Label shift in supervised learning leading to degraded classifier performance.
method Bayesian framework with dynamic Dirichlet priors and online EM algorithms.
result Significant improvements in classifier accuracy over state-of-the-art methods.
WBCP improves conformal prediction for distribution shifts using weighted Dirichlet posteriors.
problem Handling distribution shifts in conformal prediction.
method Generalizes Bayesian Quadrature Conformal Prediction (BQ-CP) to arbitrary importance-weighted settings.
result WBCP maintains coverage guarantees while providing richer uncertainty information.
New method uses fractional posteriors for semiparametric inference with improved uncertainty quantification.
problem Semiparametric inference with nonparametric priors and fractional posteriors.
method Established a general Bernstein--von Mises theorem for fractional posterior distributions, proposed shifted-and-rescaled credible sets.
result Fractional posterior credible sets provide reliable uncertainty quantification but have inflated size; shifted-and-rescaled set is an efficient confidence set.
Bayesian framework improves uncertainty estimates under covariate shifts.
problem Neural networks' unreliable uncertainty estimates under covariate shifts.
method Adaptive prior conditioned on training and new covariates, amortized variational inference.
result Significantly improved uncertainty estimates under distribution shifts.
Generative models help make decisions under changing data distributions.
problem Making decisions based on historical data when the actual data distribution changes.
method Flow- and score-based generative models to represent and transform distributions.
result Generative models can learn nominal uncertainty, create stressed distributions, and produce conditional distributions.
Develops conformal Bayes for two-sided censored Gaussian regression under label shift.
problem Prediction under label shift with censored responses.
method Combines posterior predictive tilting with weighted conformal calibration.
result Restores marginal coverage with smaller prediction sets.
Paper tackles efficient risk estimation under dataset shift conditions.
problem Limited data from target population; auxiliary data available.
method Semiparametric efficiency theory; efficient and multiply robust estimators.
result Developed estimators for various dataset shift conditions.
GS-B3SE improves label shift estimation by smoothing priors on a graph.
problem Label shift adaptation when source and target distributions share conditional but not marginal probabilities.
method Graph-Smoothed Bayesian Black-Box Shift Estimator (GS-B3SE) places Laplacian-Gaussian priors on log-priors and confusion-matrix columns tied by a label-similarity graph. result GS-B3SE produces a tractable posterior with HMC or Newton-CG schemes, proving identifiability, contraction, and robustness. New method estimates grouping loss in neural networks to improve confidence scores.
problem Improving confidence scores in neural networks to reflect true posterior probabilities.
method Proposed an estimator to approximate the grouping loss.
result Modern neural networks exhibit grouping loss, especially in distribution shifts.
New criterion improves predictive evaluation in weighted inference scenarios.
problem Improving predictive evaluation in scenarios with different likelihoods for estimation and evaluation.
method Developed the posterior covariance information criterion (PCIC) to handle weighted likelihood inference.
result PCIC is asymptotically unbiased for quasi-Bayesian generalization error in weighted inference.
SwISS improves scalability of Bayesian inference for large datasets.
problem Scalability issues in Bayesian inference for large datasets.
method Divide-and-conquer approach with SwISS for recombining sub-posterior samples.
result SwISS accurately approximates the original posterior distribution.
A new method learns posterior and predictive distributions together, reducing computational cost.
problem Sequential two-stage Bayesian inference is computationally expensive.
method Amortized variational inference targeting posterior-predictive distribution.
result Efficient online inference with more accurate predictive distributions.
Exact posterior score estimation for solving linear inverse problems
problem Solving linear inverse problems
method Derive the exact posterior score and use it as a denoising training objective
result EPS outperforms training-free and training-based baselines on various metrics
This paper introduces a scalable benchmark for evaluating local posterior sampling in neural networks.
problem Degeneracy in neural network loss landscapes and its impact on SGMCMC algorithms.
method Development of a scalable benchmark for local posterior sampling.
result RMSProp-preconditioned SGLD is most effective at representing the local geometry of the posterior distribution.
The paper improves model robustness by regularizing posterior differences.
problem Improving model robustness in noisy input scenarios.
method Posterior differential regularization with f-divergence. result Regularizing with f-divergence improves model robustness. FMCPE improves SBI accuracy by correcting posterior estimators with flow matching.
problem Model misspecification in SBI leads to biased or overconfident posteriors.
method Flow Matching Corrected Posterior Estimation (FMCPE) trains a posterior approximator and corrects it using calibration samples.
result FMCPE consistently mitigates misspecification effects, improving inference accuracy and uncertainty quantification.
New method for estimating class proportions in open-set label shift data.
problem Estimating class proportions and distributions when test data includes novel classes.
method Semiparametric density ratio model framework with maximum empirical likelihood estimators and confidence intervals.
result Improved estimation accuracy and classification performance compared to existing methods.
Accelerates pulsar light curve inference with learned representations and optimization.
problem Computational expense of Markov chain Monte Carlo methods for posterior inference.
method Combining U-Net latent representations with local simulator-guided optimization.
result 120x reduction in inference time (24 hours to 12 minutes) with accuracy preserved.
Deep models memorize training data in geophysical inversion, leading to biased posterior distributions.
problem Memorization of training data biases learned priors in geophysical inverse problems.
method Casting generative models' training as maximum likelihood, we show memorization results in a reweighted empirical distribution for diffusion models, leading to Gaussian mixture priors and posteriors.
result Memorization leads to posterior distributions that are likelihood-weighted lookup among stored training examples, affecting full waveform inversion outcomes.
In recent years, the field of machine learning has made phenomenal progress in the pursuit of simulating real-world data generation processes. One notable example of such success is the variational autoencoder (VAE). In this work, with a small shift in perspective, we leverage and adapt VAEs for a different purpose: un…
This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering is performed using a probability metric evaluated between post…
Geometry-aware KDE model improves multiclass quantification.
problem Accurately estimating class prevalence for label shift adaptation.
method Log-ratio representations and Aitchison geometry for compositional data, shrinkage regularization.
result Competitive with state-of-the-art quantifiers, often improving over standard KDE-based baselines.
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…
TL-ANDI distills context from source data to improve transfer learning for TFMs.
problem Limited transfer learning due to context-size constraints and distribution shifts.
method TL-ANDI uses posterior-aware distillation to construct a compact source context and locally distills labels.
result Improves transfer performance by addressing context-size and distribution shifts.
The paper examines how updates to probabilistic models influence behavior based on evidence.
problem Understanding how updates to probabilistic models influence behavior based on evidence.
method Study of KL-regularized soft updates as Bayesian posterior updates within a single probabilistic model.
result Posterior updates determine relative incentives but not absolute rewards, which are ambiguous up to context-specific baselines.
Improved variational inference for geophysical inverse problems with data correction.
problem High computational cost and accuracy issues in Bayesian inference for geophysical inverse problems.
method Amortized variational inference with latent distribution correction using physics-based priors.
result Improved robustness of amortized variational inference under data distribution shifts.
Bayesian optimization enhanced with conformal prediction for better outcome reliability.
problem Uncertainty and model misspecification in Bayesian optimization.
method Conformal prediction to provide coverage guarantees and Bayesian optimization to select queries.
result Significant improvement in query coverage without sacrificing sample-efficiency.
New method samples from LLM posterior for coherent, useful responses.
problem Hallucinations in large language models.
method Posterior sampling for conditional generation, with calibration.
result Achieves statistical guarantees with higher downstream utility.
PostNet predicts uncertainty without OOD data, improving OOD detection and calibration.
problem Accurate uncertainty estimation for safe systems.
method PostNet uses Normalizing Flows to learn individual posterior distributions over predicted probabilities.
result PostNet achieves state-of-the-art results in OOD detection and uncertainty calibration.
A new method using mean shift clustering speeds up Bayesian evidence calculation.
problem Difficulty in Nested Sampling algorithm convergence and systematic errors.
method Mean shift cluster recognition method integrated into NestedFit.
result Significant reduction in computation time and uncertainty of Bayesian evidence.
Transfer learning framework for fragility modeling under domain shift and class imbalance
problem Data gaps in structural fragility modeling
method Transfer learning
result Improves failure detection and predictive stability in low-data regimes
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent. One exception is the PAC-Bayes theorem of Kakade, Sridharan, and Tewari (2008), which is established via Rademacher complexity theory by viewing Gibbs classifiers as linear operators. The goal of this paper is to extend thi…
MARCD uses generative scenarios to improve portfolio decisions during regime shifts.
problem Improving portfolio decisions under regime shifts and drawdowns.
method MARCD employs a Gaussian HMM for regime inference, a diffusion generator for scenario production, and a CVaR allocator with tail-weighted and crisis-aware components.
result MARCD reduces maximum drawdowns by 34% compared to baseline methods over 2020-2025.
This work tackles sequential data learning challenges by improving neural network robustness to non-iid distribution shifts.
problem Sequential data learning challenges, particularly non-iid distribution shifts across batches.
method Cramér-Rao-based regularization using Fisher Information Matrix to adapt to sequential covariate shifts.
result Achieves 19% accuracy improvement over state-of-the-art methods.