A new method for CT using graph-based regularization.
problem Transfer calibrations between instruments without suitable transfer standards.
method Employing manifold regularization of PLS objective to enforce invariant projections in latent variable space.
result Implicit removal of inter-device variation in predictive directions.
Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict experimental observations. In this work, we propose a novel nonlinear technique for calibrating from simulations to experiments, or from low fi…
TransCal calibrates DA models with lower bias and variance.
problem Calibrating DA models to estimate accurate predictive uncertainty.
method Transferable Calibration (TransCal) in a unified hyperparameter-free optimization framework.
result TransCal achieves more accurate calibration with lower bias and variance.
Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specific calibration data are required to tailor the classifier for each subject. Transfer learning has be…
RECaST calibrates source models for target populations with uncertainty quantification.
problem Uncertainty in transfer learning predictions.
method Random effect calibration of source to target models.
result Nominal coverage of prediction sets in linear models, robust to nonlinear approximations.
This paper improves MI-based BCIs by applying transfer learning across all components.
problem Reducing calibration effort for new subjects in MI-based BCIs.
method Proposes TL in spatial filtering, feature engineering, and classification blocks, and adds data alignment.
result Integrating data alignment and sophisticated TL significantly improves classification performance and reduces calibration effort.
PAC-Net prunes deep models for better transfer learning.
problem Improving transfer learning with over-parameterized models.
method Prune, Allocate, Calibrate (PAC) approach.
result PAC-Net achieves state-of-the-art performance in inductive transfer learning.
Trimming helps in conformal prediction when it separates anomaly scores.
problem Effectiveness of trimming in conformal prediction under contamination.
method Analyse fixed-threshold trimming as a replacement of the contaminated calibration law with a retained law.
result Trimming helps when it separates anomaly scores, reducing clean-target coverage to a one-dimensional score-CDF transfer problem.
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.
Flexible framework for transfer learning with optimal rates.
problem Inference about a target population using related source data.
method Adaptive transfer learning framework allowing covariate-dependent relationships.
result Achieves minimax optimal rates of convergence by adapting to transfer relationship.
CCAC calibrates DNN classifiers on OOD datasets by separating mis-classified samples.
problem Calibrating DNN classifiers on out-of-distribution datasets is challenging.
method CCAC introduces an auxiliary class to map DNN output to calibrated confidence, separating mis-classified from correctly classified samples.
result CCAC consistently outperforms prior methods on various DNN models, datasets, and applications.
Unified study of out-of-distribution generalization across 172 datasets.
problem Measuring and improving robustness of transfer learning models.
method Collect and fine-tune 31k models on 172 dataset pairs, varying architectures and settings.
result In- and out-of-distribution accuracies increase jointly but their relation is dataset-dependent.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
problem Improving classification accuracy in high-dimensional data with shared signals across domains.
method Transfer learning for linear discriminant analysis, decomposing mean differences into common and domain-specific components.
result Deterministic limits for transfer performance, leading to optimal weights and corrections for bias.
Study shows how pretraining robustness transfers to downstream tasks.
problem Understanding how robustness is transferred from pretraining to downstream tasks.
method Theoretical analysis and practical validation of robustness constraints.
result Robustness of a linear predictor on downstream tasks can be constrained by the robustness of its underlying representation.
Study improves CNN medical image segmentation accuracy and reliability.
problem Over-confident predictions and silent failures in out-of-distribution data.
method Multi-task learning and spectral analysis of CNN feature maps.
result Joint multi-task learning models outperform dedicated models and detect OOD data more accurately.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
Deep learning framework predicts surface texture parameters and their uncertainties.
problem Predicting surface texture parameters and their uncertainties from multi-instrument datasets.
method Reproducible deep learning framework using multi-instrument dataset, quantile and heteroscedastic heads for uncertainty modeling, and post-hoc conformal calibration.
result High fidelity predictions (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918) and well-modelled uncertainty targets (Ra_uncert 0.9899, Rz_uncert 0.9955).
Framework improves target domain prediction using quantile matching.
problem Improving prediction accuracy in data-scarce target domains.
method Conditional quantile matching for distributional alignment.
result Empirical risk minimizer achieves tighter excess risk bound.
New method uses predictions to infer causal effects without labeled data.
problem Data labeling costs limit causal inference experiments.
method Prediction-Powered Causal Inferences (PPCI) using conditional calibration and transfer constraints.
result Valid causal inference achieved on experiments with no human annotations.
New algorithm reduces overfitting in neural networks.
problem Overfitting in neural networks.
method Integrates SMC with SGHMC for mini-batch sampling.
result SMCSGHMC outperforms SGD and deep ensembles.
Unified theory and practical insights for multicalibration boosting.
problem Ensuring fairness and robustness in predictions over diverse functions.
method Developed a unified framework for multicalibration boosting under weaker conditions.
result Boosting iterates converge to a Bregman projection, characterizing multicalibration.
A method corrects bias in estimating a high-dimensional classification rule using auxiliary outcomes.
problem Bias in estimating a high-dimensional classification rule using only one outcome.
method Robust transfer learning approach combining MTL and calibration steps.
result Final estimator achieves lower error than using only the target outcome.
A transfer learning method builds high-dimensional models using disparate datasets.
problem Building comprehensive prediction models with small sample sizes and limited features.
method Transfer learning approach using external data to build a reduced model and apply calibration equations.
result Proposes a penalized generalized method of moment framework for inference and one-step estimation.
Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.
problem High-stakes AI errors lead to heterogeneous losses, challenging traditional insurance assumptions.
method Analyzed binary classification performance to tail risk exposure, using CVaR to quantify extreme losses.
result CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization.
This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…
Paper proposes a probabilistic alignment method for domain adaptation.
problem Latent distribution mismatch and miscalibrated uncertainty in adapting large-scale models.
method Bayesian latent transport framework with PAC-Bayesian regularization.
result Reduction in latent manifold discrepancy and improved uncertainty calibration.
DRUM transfers cardiac arrest models across registries with missing covariates.
problem Clinical prediction models fail when key training covariates are unavailable at deployment.
method DRUM transfers models to target populations with structurally missing covariates, optimizing worst-case predictive performance over unknown target distributions.
result DRUM yields better-calibrated predictions and improved clinical classification performance across sites.
The paper calibrates shrinkage covariance estimators for spectral functionals in high dimensions.
problem Calibrating shrinkage covariance estimators for spectral functionals in high dimensions.
method Derives first-order null laws, distribution-free Davis-Kahan bands, and calibrated tests for spectral functionals under shrinkage.
result Calibrated tests and intervals for spectral functionals are provided, addressing the issue of estimation noise and shrinkage bias.
CAT improves domain adaptation for fault diagnosis by calibrating teacher network predictions.
problem Performance drops in deep learning models when applied to different data distributions.
method CAT uses post-hoc calibration techniques to calibrate predictions of the teacher network during self-training.
result CAT achieves state-of-the-art performance on most transfer tasks in domain-adaptive IFD.
A novel framework quantifies uncertainty using proper scores for various tasks.
problem Uncertainty quantification in machine learning for reliable applications.
method Proposes a general framework based on proper scores for epistemic, aleatoric uncertainty, and model calibration.
result Achieves state-of-the-art uncertainty estimation for large language models and generative models.
This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural net…
This paper develops convex surrogates for optimizing the multi-label F-measure.
problem Optimizing the F-measure for multi-label classification is computationally hard.
method Designing convex surrogate losses calibrated for the F-measure.
result The F-measure for multi-label problems has a rank of at most s2+1. Better uncertainty estimates for neural networks using Gaussian process priors.
problem Poor uncertainty estimates in neural networks, especially on out-of-distribution data.
method Characterize the function-space prior of an ensemble of infinitely-wide neural networks as a Gaussian process and use it to build a probabilistic model.
result The approach improves calibration of neural networks, especially under distributional shift.
OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.
problem Deep models trained on raw counts are vulnerable to distribution shift.
method OpFlow learns row-centered choice potentials and reconstructs flows by combining them with a calibrated origin scale.
result OpFlow improves robustness under environment shifts, as shown by controlled synthetic shifts and a real-world experiment.
We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian moti…
FoRDE uses input gradients to improve neural network ensembles.
problem Improving neural network ensembles for robustness and accuracy.
method Proposes FoRDE, an ensemble learning method based on ParVI, which repels function space by input gradients.
result FoRDE significantly outperforms DEs and other ensemble methods in accuracy and calibration.
Smartwatches are increasingly being used to recognize human daily life activities. These devices may employ different kind of machine learning (ML) solutions. One of such ML models is Gradient Boosting Machine (GBM) which has shown an excellent performance in the literature. The GBM can be trained on available data set…
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient descent (SGD) iterates with a modified learning rate schedule, has recently been sho…
Pipeline combines ETF preprocessing with tabular model for cross-modal inference.
problem Transferability of tabular models across different modalities.
method Fixed comparison object, ETF preprocessing, in-context inference.
result Pipeline is broadly competitive, runs faster, and produces well-calibrated probabilities.
The paper studies practical estimation and interpretation of Rényi transfer entropy.
problem Challenges in accurately estimating and interpreting Rényi transfer entropy.
method Systematic study of k-nearest neighbor estimator for Rényi entropy and transfer entropy.
result Effective estimates of effective Rényi transfer entropy can accurately capture directional information flow.
Objective: Using traditional approaches, a Brain-Computer Interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g.~by transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch …
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. M…
This paper introduces minimum-risk recalibration for probabilistic classifiers, improving their reliability and accuracy.
problem Improving the reliability and accuracy of probabilistic classifiers.
method Minimum-risk recalibration within the MSE decomposition framework, analyzing UMB method and label shift adaptation.
result The optimal number of bins for UMB scales with n1/3, resulting in a risk bound of approximately O(n−2/3). A new method combines multiple bounds and betting strategies for selective prediction, improving risk coverage in data-scarce settings.
problem Selective prediction with risk control in data-scarce domains.
method Combines concentration inequalities, multiple-testing corrections, and betting-based confidence sequences.
result Transfer-Informed Betting achieves tighter bounds and better coverage in data-scarce settings.
This paper improves risk bounds and calibration for smart predict-then-optimize method.
problem Improving risk bounds and calibration for smart predict-then-optimize method.
method Develops risk bounds and uniform calibration results for the SPO+ loss relative to the SPO loss.
result Empirical minimizer of the SPO+ loss achieves low excess true risk with high probability.
TradeFM learns market microstructure from trade events, improving financial model accuracy.
problem Lack of generalizable models for market microstructure.
method Generative Transformer model trained on billions of trade events, using scale-invariant features and universal tokenization.
result TradeFM generates rollouts that match key stylized facts of financial returns and outperforms existing models.
Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.