Conformal Prediction Regions match Imprecise Highest Density Regions under consonance.
problem Matching conformal prediction regions with highest density regions.
method Using consonance and the Imprecise Probability theory of clouds.
result Imprecise Highest Density Regions are equivalent to Conformal Prediction Regions under consonance.
DIR-ST2 delineates imprecise regions using spatio-temporal-textual data.
problem Finding precise boundaries of imprecise regions without clear boundaries.
method DIR-ST2 uses iterative DBSCAN clustering with spatio-temporal-textual information. result DIR-ST2 outperforms state-of-the-art methods in delineating imprecise regions. Develops a category-theoretic approach to interpret conformal prediction.
problem Interpreting conformal prediction as a quantitative uncertainty tool.
method Category-theoretic approach to represent and decompose conformal prediction.
result Decomposes conformal prediction into two steps: predictive distributions and prediction regions.
An imprecise SHAP method explains class probabilities with limited data.
problem Explaining class probabilities with limited training data.
method New approach for computing feature marginal contributions and general approach to interval-valued Shapley values.
result The imprecise SHAP method improves explanation of class probabilities.
New method uses conformalization to create classification regions from ambiguous labels.
problem Creating provable guarantees in classification with uncertain labels.
method Conformal methods applied to credal regions for classification problems.
result New method provides smaller and more disentangled prediction sets.
Study on how imprecise medical data affects predictions in hyperthyroidism.
problem Impact of imprecise medical data on prediction results.
method Formulated a model for data imprecisions, generated imprecise samples, defined measures to evaluate impacts, and performed experiments.
result Small imprecisions can lead to large ranges of predicted results, potentially causing mis-labeling and inappropriate actions.
This work introduces a new metric for comparing imprecise probability models.
problem Quantifying differences between imprecise probability models.
method Integral imprecise probability metric framework based on Choquet integral.
result IIPM enables comparison across different imprecise probability models and quantifies epistemic uncertainty.
The paper analyzes when credal sets stabilize under iterative updates in machine learning.
problem When do credal sets stabilize under iterative updates in machine learning?
method Fixed-point theorems for credal set updates.
result The paper provides the first analysis of credal set stability.
Paper introduces novel survival models for handling censored data.
problem Complex data structures and heavy censoring in survival analysis.
method Combines imprecise probability theory with attention mechanisms.
result Proposed models, especially iSurvJ, outperform traditional methods.
Estimates transition rates of continuous-time Markov chains using imprecise probabilistic methods.
problem Estimating transition rate matrix from a finite-duration process.
method Imprecise probabilistic framework with conjugate priors and discrete-time analysis for hyperparameter determination.
result Continuous-time estimator with simple closed-form expression derived from discrete-time model.
Walley's Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or i…
Study generalizes property elicitation to imprecise probabilities.
problem Minimizing risk over imprecise probability distributions.
method Maximin risk minimization over a set of imprecise probabilities.
result Conditions for elicitability of IP-properties.
We give an overview of two approaches to probability theory where lower and upper probabilities, rather than probabilities, are used: Walley's behavioural theory of imprecise probabilities, and Shafer and Vovk's game-theoretic account of probability. We show that the two theories are more closely related than would be …
Two strategies extend multi-label chaining for imprecise probability estimates.
problem Handling imprecise probability estimates in multi-label classification.
method Adapting multi-label chaining to use convex sets of distributions (credal sets).
result Adapted approaches produce relevant cautiousness on hard-to-predict instances.
Paper tackles learning time series models from noisy timestamps.
problem Learning time series detection models from temporally imprecise labels.
method Proposes a general learning framework accommodating different base classifiers and noise models.
result Significantly outperforms alternatives on real mobile health data.
Paper introduces imprecise logistic regression for handling uncertain data.
problem Uncertainties in data prevent traditional logistic regression from being applied effectively.
method Develops imprecise logistic regression model using intervals of possible values.
result Clearly expresses epistemic uncertainty in predictions.
The study establishes stability in WMOT, crucial for finance with imprecise data.
problem Stability in weak martingale optimal transport for finance with imprecise data.
method Established stability through rigorous mathematical analysis.
result Stability of WMOT is proven, with applications to VIX futures and Brownian motion.
Optimizes trading strategy for cointegrated assets with bounded risk.
problem Maximizing profit from cointegrated assets with risk constraints.
method Formulates as convex optimization problem, then generalizes to bounded risk.
result Optimal strategy remains efficiently solvable even with bounded risk.
New methods detect targets from imprecisely labeled hyperspectral data.
problem Challenges in acquiring labeled hyperspectral data.
method Multi-Target MI-ACE and MI-SMF methods that learn target signatures from imprecisely labeled samples.
result Effective at learning target signatures and performing target detection.
We introduce imprecise Markov semigroups to handle uncertainty in Markov processes.
problem Uncertainty in transition probabilities and invariant measures of Markov processes.
method Topology, geometry, and probability techniques to analyze ergodic limits under model uncertainty.
result Uniform long-term bounds collapse asymptotically in certain regimes.
New tools connect CP to GF inference for better probabilistic prediction.
problem Lack of versatility in conformal prediction for quantifying evidence.
method Imprecise probability theory and generalized fiducial inference.
result Establishes a formal connection between CP and GF inference.
This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively …
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
Researchers develop a new method to learn from incomplete data.
problem Learning from incomplete data with imprecise probabilities.
method Credal sum-product networks (SPNs) for robust probabilistic representations.
result Imprecise SPNs can capture robustness to missing data.
New method achieves faster calibration without randomization.
problem Calibrating probabilistic forecasts in adversarial settings.
method Using interval forecasts and the power of two choices.
result Achieves O(1/T) calibration error rate without randomization. Paper improves conformal prediction for imprecise training data.
problem Applying conformal prediction to partially labeled data.
method Generalizes conformal prediction for set-valued training and calibration data.
result Validates the proposed method and shows it outperforms baselines.
We develop a new statistical test for comparing variables with varying scales.
problem Comparing variables with different scales in multidimensional spaces.
method Order based on expectations of random variables, generalized stochastic dominance (GSD) order, regularized statistical test, linear optimization, imprecise probability models.
result Validated through multidimensional data from various fields.
New approach for handling uncertain probabilities.
problem Handling imprecise or uncertain probabilities.
method Introducing interval probability measures and updating rules.
result Formal solution to the Keynes-Ramsey controversy.
Novel path planning improves UAV detection of extreme anomalies.
problem Improper criteria for UAV anomaly detection.
method Mathematical criteria guiding UAV towards strong anomalies.
result Proposed approach outperforms traditional methods in various applications.
Corrects conditions in Fissler and Ziegel's 2016 paper.
problem Clarifying conditions in Fissler and Ziegel's 2016 paper.
method None, as it is a correction of existing results.
result Corrects conditions in Proposition 3.4 and Theorem 5.2(ii) of Fissler and Ziegel (2016).
In the presence of a layer of metaprobabilities (from uncertainty concerning the parameters), the asymptotic tail exponent corresponds to the lowest possible tail exponent regardless of its probability. The problem explains "Black Swan" effects, i.e., why measurements tend to chronically underestimate tail contribution…
We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing on direct…
CBDL uses credal sets to improve uncertainty quantification in deep learning.
problem Uncertainty in predictions and robustness to distribution shifts in deep learning.
method Train an infinite ensemble of Bayesian Neural Networks using credal sets.
result CBDL distinguishes between aleatoric and epistemic uncertainties and quantifies them better than single BNNs.
New framework improves model reliability under distribution shifts.
problem Lack of formal guarantees connecting shift magnitude to prediction reliability in TTA methods.
method Develops a PAC-Bayesian framework interpreting MMD-balls as credal sets.
result Establishes generalization bounds and provides epistemic uncertainty quantification.
AQEM improves precision in quantum metrology with feedback.
problem Estimating unknown parameters with higher precision than classical methods.
method Adaptive quantum-enhanced metrology using feedback to optimize quantum processes.
result Demonstrated improved precision in adaptive interferometric phase estimation.
Develops possibilistic VI using maxitive Donsker-Varadhan formulation.
problem Adapting VI to possibilistic framework to handle uncertainty.
method Maxitive Donsker-Varadhan formulation for possibilistic VI.
result CBOpt optimizers achieve competitive performance in image classification.
Impact of chosen behavioural factors on imprecision of present value is discussed here. The formal model of behavioural present value is offered as a result of this discussion. Behavioural present value is described here by fuzzy set. These considerations were illustrated by means of extensive numerical case study. Fin…
Limited supervision can enable reliable disentangled representation learning.
problem Learning disentangled representations without inductive biases is theoretically impossible.
method Investigated the impact of limited supervision (0.01--0.5% of data) on disentanglement methods.
result A small number of labeled examples (0.01--0.5\% of the data set) is sufficient for model selection.
Scales gradual pattern discovery from imprecise data.
problem Managing large collections of gradual patterns from imprecise data.
method Introduces gradualness thresholds and considers attribute value distribution.
result Eliminates noise patterns, showing a smaller set of meaningful patterns.
Paper introduces Isotonic Mechanism for better item scoring.
problem Noisy reviewer scores; owner prefers not to disclose true scores.
method Uses owner's ranking of items and raw scores to adjust and improve accuracy.
result Adjusted scores are significantly more accurate than raw scores.
Survey on 4-manifolds with specific curvature properties.
problem Understanding the structure of 4-manifolds with nonnegative Ricci curvature and Euclidean volume growth.
method Analysis of blow-downs and cone-like structures at infinity.
result Manifolds look like cones over spherical space forms at infinity.
The space of Lie algebra cohomology is usually described by the dimensions of components of certain degree even for the adjoint module as coefficients when the spaces of cochains and cohomology can be endowed with a Lie superalgebra structure. Such a description is rather imprecise: these dimensions may coincide for co…
Paper simulates LR fuzzy intervals with interval-valued cores.
problem Generating random fuzzy intervals with interval-valued cores.
method Developed algorithms for simulating LR fuzzy numbers with interval-valued cores.
result Numerically efficient algorithm for simulating fuzzy values.
Study enhances classifier robustness against noisy labels.
problem Impact of label noise on model performance in real-world scenarios.
method Integrates adversarial machine learning and importance reweighting techniques with CNN.
result Improved model resilience against noisy data.
Deep Causal Graphs model complex causal relationships using neural networks.
problem Limited applicability of parametric causal models to real-life datasets with non-linear relationships.
method Deep Causal Graphs, an abstract specification for neural networks to model causal distributions.
result Demonstrates expressive power in modelling complex interactions and provides true causal counterfactuals.
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…
Introduces a new local move in link diagrams.
problem Differentiating between two types of local moves.
method Defined and studied region freeze crossing change.
result Identified similarities and differences between region crossing change and region freeze crossing change.
Spatially constrained clustering divides landscapes into homogeneous regions with spatial contiguity.
problem Dividing landscapes into homogeneous patches with spatial contiguity and hierarchy.
method Developed a spatially constrained spectral clustering framework using a flexible kernel and recursive bisection.
result The proposed framework outperforms baseline methods in balancing region contiguity and homogeneity.