This work examines uncertainty sampling in binary classification using equivalent loss.
problem Lack of consensus on proper uncertainty definition and theoretical guarantees for active learning.
method Systematically examines uncertainty sampling via equivalent loss, proving its optimality.
result Established that uncertainty sampling optimizes against equivalent loss, providing theoretical guarantees.
Improves image quality in generative models by estimating pixel-wise aleatoric uncertainty.
problem Lack of quantitative assessment of image quality in diffusion models.
method Estimate pixel-wise aleatoric uncertainty during sampling phase using a perturbation scheme designed for diffusion models.
result Uncertainty-guided sampling leads to better sample generation quality as shown by FID scores.
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the …
This study improves uncertainty quantification in seismic inversion.
problem Uncertainty in seismic inversion due to limited data and model diversity.
method Integrates ensemble methods with importance sampling.
result More accurate uncertainty quantification in velocity models.
Thompson sampling is an efficient algorithm for sequential decision making, which exploits the posterior uncertainty to address the exploration-exploitation dilemma. There has been significant recent interest in integrating Bayesian neural networks into Thompson sampling. Most of these methods rely on global variable u…
Characterizes uncertainty in high-dimensional linear classification models.
problem Assessing uncertainty in high-dimensional linear classification models.
method Approximate message passing algorithm for posterior marginals, closed-form formula for joint statistics.
result Closed-form formula for joint statistics between logistic classifier, Bayesian uncertainty, and ground-truth probit uncertainty.
New method reduces uncertainty in AI-driven Monte Carlo simulations.
problem Epistemic uncertainty in AI surrogate models affects Monte Carlo sampling outcomes.
method Penalty Ensemble Method (PEM) modifies Metropolis acceptance rule to increase rejection probability in uncertain regions.
result PEM enhances reliability of Monte Carlo simulations by reducing uncertainty propagation.
Improved uncertainty estimation through diverse sampling in neural networks.
problem Enhancing uncertainty estimation for machine learning models.
method Data-driven correlations and determinantal point processes-based sampling for dropout layers.
result State-of-the-art results in uncertainty estimation for regression and classification tasks.
New framework compares credal sets for hypothesis testing with epistemic uncertainty.
problem Comparing distributions with partial ignorance and epistemic uncertainty.
method Credal two-sample testing framework for convex sets of probability measures.
result Direct integration of epistemic uncertainty in hypothesis testing.
We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were pr…
When the cost of misclassifying a sample is high, it is useful to have an accurate estimate of uncertainty in the prediction for that sample. There are also multiple types of uncertainty which are best estimated in different ways, for example, uncertainty that is intrinsic to the training set may be well-handled by a B…
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertainty estimation techniques directly on the agent's value estimating neural network to detect OOD samples. The focus of our work lies in analyzi…
Paper reduces uncertainty in predictive models using neural networks and Gaussian processes.
problem High epistemic uncertainty in predictive models.
method Adaptive sampling approach with prediction interval-generation neural networks and Gaussian processes.
result Method consistently converges faster to minimum epistemic uncertainty levels.
Proposes a sample-efficient method for uncertainty estimation in deep learning.
problem Inaccurate uncertainty estimation in deep learning models, especially with limited data.
method Probabilistic Neighbourhood Component Analysis (PCA) for sample-efficient uncertainty estimation.
result Demonstrates superior uncertainty quantification compared to state-of-the-art methods.
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.
Proposes DGCN with trajectory sampling for data-efficient policy search in MBRL.
problem Improving data efficiency in model-based reinforcement learning.
method Combines trajectory sampling and DGCN for uncertainty propagation in probabilistic world models.
result Improves sample-efficiency over other uncertainty propagation methods and probabilistic models.
TS-RSR improves batch Bayesian Optimization by minimizing redundancy and focusing on high uncertainty points.
problem Efficient batch selection in Bayesian Optimization to reduce redundancy and improve performance.
method TS-RSR uses Thompson Sampling to minimize a regret to uncertainty ratio for batch selection.
result TS-RSR outperforms state-of-the-art batch BO algorithms on various test functions.
New method tackles label noise on imbalanced datasets by considering class-specific uncertainty.
problem Label noise and class imbalance in imbalanced datasets.
method Epistemic and aleatoric uncertainty-aware class-specific noise modeling.
result Proposed ULC framework improves performance on imbalanced datasets.
Optimizes sampling for faster convergence in Bayesian experimental design and uncertainty quantification.
problem Efficiently selecting samples for faster convergence in Bayesian experimental design and uncertainty quantification.
method Output-weighted acquisition functions leveraging likelihood ratio to guide sampling towards relevant regions.
result Superiority of the proposed method in uncertainty quantification and rare event identification.
Density-Softmax improves uncertainty estimation and robustness without sampling, reducing model size and latency.
problem Sampling-based uncertainty estimation methods suffer from large model size and high latency.
method Combines a Lipschitz-constrained feature extractor with the softmax layer to create a sampling-free deterministic framework.
result Density-Softmax reduces over-confidence under distribution shifts and achieves competitive results in uncertainty and robustness.
New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
Uncertainty sampling, a popular active learning algorithm, is used to reduce the amount of data required to learn a classifier, but it has been observed in practice to converge to different parameters depending on the initialization and sometimes to even better parameters than standard training on all the data. In this…
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there are enormous benefits to a system that is not only accurate but also has a sense for when it is not sure. Existing proposals center around Ba…
The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variabil…
Paper quantifies uncertainty in probabilistic models using Gaussian Processes.
problem Assessing reliability of probabilistic machine learning predictions.
method Systematic framework for estimating epistemic and aleatoric uncertainty, using Gaussian Processes and Monte Carlo sampling.
result Effective approach for quantifying prediction confidence in probabilistic models.
Paper presents a method to summarize HMC samples for neural networks, providing meaningful uncertainty estimates.
problem Lack of interpretable summary statistics for HMC samples in neural networks due to permutation symmetry.
method Introducing a transpositions metric to quantify permutations and using rebasin method to summarize HMC samples.
result Compact representation of HMC samples provides meaningful uncertainty estimates for each weight in a neural network.
Proposes a new method for uncertainty estimation in neural networks.
problem Estimating uncertainty in neural networks.
method Samples outputs from Gaussian distributions parametrized by mean and variance sub-layers.
result Achieves better uncertainty quality than other methods.
LLMs generate answers under incomplete context, and their uncertainty should scale with missing information.
problem Evaluating the quality of LLM answers under incomplete context.
method A controlled framework with varying context availability, and two uncertainty measures (sampling-based confidence and response entropy) evaluated on SQuAD.
result Response entropy increases with context removal and explains more variance in accuracy than confidence, suggesting it is a more responsive uncertainty measure.
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.
A new method improves uncertainty estimation in deep learning, especially for hard-to-label samples.
problem Improving uncertainty estimation for hard-to-label samples in deep learning.
method Introduces Fisher Information Matrix (FIM) to dynamically reweight objective loss terms.
result Consistently outperforms traditional evidential neural networks in uncertainty estimation tasks.
Optimizes latent space of VAEs using decoder uncertainty to generate valid objects.
problem Lack of robustness in optimizing VAE latent space for black-box properties.
method Importance sampling-based estimator of decoder epistemic uncertainty to guide optimization.
result Improves trade-off between black-box objective and validity of generated samples.
VGE provides a practical approach to uncertainty estimation in ensemble models.
problem Uncertainty estimation in ensemble models using additive decomposition breaks down.
method Variance-Gated Ensembles (VGE) introduces a differentiable framework with a signal-to-noise gate.
result VGE provides a Variance-Gated Margin Uncertainty (VGMU) score and Variance-Gated Normalization (VGN) layer.
The standard taxonomy of predictive uncertainty is inconsistent with standard measures.
problem Uncertainty taxonomy and measure inconsistency
method Proof of inconsistency
result Uncertainty is not reducible to data collection
SCS identifies a range of plausible equally weighted portfolios, quantifying selection uncertainty.
problem Uncertainty in selecting the best equally weighted portfolio subset.
method Introduces Selection Confidence Set (SCS) for EWPs, covering plausible portfolios with high probability.
result SCS quantifies selection uncertainty and covers the unknown optimal selection with high probability.
GUESS improves surrogate model accuracy with adaptive sampling.
problem Creating accurate surrogate models with limited data.
method Gradient and Uncertainty Enhanced Sequential Sampling (GUESS) using predictive uncertainty and Taylor expansion.
result GUESS achieved highest sample efficiency compared to other strategies.
UBMF tackles fault diagnosis in imbalanced industrial data with enhanced accuracy and adaptability.
problem Fault diagnosis challenges in imbalanced industrial data.
method Integrates four key modules: data perturbation, cross-task feature extraction, uncertainty-based filtering, and Bayesian meta-knowledge integration.
result Achieves an average improvement of 42.22% across ten diagnostic tasks.
TULiP estimates uncertainty for deep learning models safely.
problem Reliable uncertainty estimation for deep learning models in the open world.
method TULiP considers a hypothetical perturbation, bounds its effect, and computes uncertainty from sampled predictions.
result TULiP achieves state-of-the-art performance in OOD detection benchmarks.
Beam search improves UQ in LLMs by reducing duplicates and variance.
problem Peaked distributions in multinomial sampling lead to duplicates and high variance in uncertainty estimates.
method Employ beam search to generate candidates for consistency-based UQ, providing a theoretical lower bound and empirical evaluation.
result Beam search achieves smaller error than multinomial sampling, leading to state-of-the-art UQ performance.
A simple uncertainty measure improves deep bandit performance.
problem Efficient exploration in complex environments with deep neural networks.
method Sample Average Uncertainty (SAU) for estimating outcome uncertainty directly.
result SAU matches the uncertainty of Thompson Sampling and its regret bounds.
COPS optimizes deep learning by selecting informative samples with uncertainty estimation.
problem Mitigating high costs in labeling and computational resources for deep learning.
method COPS (unCertainty based OPtimal Sub-sampling) selects data with input and output uncertainty for linear softmax regression.
result COPS outperforms baseline methods in deep learning tasks, minimizing expected loss.
Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.
problem Uncertainty in overparameterized models, especially in low-data regimes.
method Twin-Bootstrap Gradient Descent (Twin-Boot) trains two identical models on independent bootstrap samples and uses their divergence to guide learning.
result Improves calibration and generalization, yields interpretable uncertainty maps.
Novel neural network models quantify uncertainty for deep classifiers.
problem Deep networks' overconfidence and ignorance about uncertainty.
method Variational autoencoders and GANs generate out-of-distribution samples.
result Better uncertainty estimates for in- and out-of-distribution samples.
New method improves deep learning models in noisy label classification.
problem Improving deep learning models in noisy label classification.
method Analyzes loss and uncertainty changes during training, designs a new robust training method.
result Significantly outperforms other state-of-the-art methods in various deep learning models.
A new Bayesian optimization method tackles constrained optimization with uncertainties.
problem Optimizing functions with uncertain constraints.
method Bayesian optimization with a new acquisition criterion.
result The new criterion optimizes both objective function improvement and constraint reliability.
RISA improves VFL by using imputed samples with low uncertainty.
problem Limited overlapping samples constrain VFL performance.
method Imputing non-overlapping samples and using evidence theory to select reliable imputed samples.
result Significant performance gains achieved, especially with limited overlapping samples.
Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a randomized approach to detecting such perturbations based on minimum uncertainty…
A new Thompson Sampling framework handles uncertainty by imputing missing data.
problem Handling uncertainty in contextual bandit problems.
method Generative model to impute missing outcomes, fit policy, and select actions.
result Established a state-of-the-art regret bound that depends on generative model quality.
Paper studies robust MDPs, improving sample complexity and asymptotic performance.
problem Optimal robust policy and value function in robust MDPs with generative models.
method Improves prior results on non-asymptotic and asymptotic performances of robust MDPs, considering various uncertainty sets.
result Improved sample complexity and asymptotic normality of optimal robust value function.