On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

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48 results for “confidence learning”

Learning with Confidencecs.LG

A new concept of confidence in learning is defined and analyzed.

problem Understanding and quantifying trust in learning processes.

method Formal axioms, continuum measures, vector fields, loss functions.

result Confidence can be represented and optimized in learning.

confidence learning belief state Bayes Rule vector fields

2025-08-14abs ↗pdf ↗

PAC Confidence Sets for Deep Neural Networks via Calibrated Predictioncs.LG

Algorithm constructs confidence sets for deep neural networks with PAC guarantees.

problem Ensuring reliable predictions for deep neural networks with high confidence.

method Combines calibrated prediction and learning theory bounds.

result Constructs PAC confidence sets for various deep models.

deep neural networks confidence sets PAC guarantees calibrated prediction learning theory

2019-12-31abs ↗pdf ↗

Binary Classification from Positive Data with Skewed Confidencestat.ML

Improves binary classification from positive data with skewed confidence.

problem Skewed confidence in positive data affects the performance of Pconf classifiers.

method Parameterized model of skewed confidence and hyperparameter selection.

result Proposed method effectively cancels out the negative impact of skewed confidence.

binary classification weakly supervised learning skewed confidence drowsiness prediction

2020-01-29abs ↗pdf ↗

CoinDICE: Off-Policy Confidence Interval Estimationcs.LG

CoinDICE estimates confidence intervals for unknown behavior policies in reinforcement learning.

problem Estimating value of a target policy using only behavior policy data.

method Function space embedding, generalized empirical likelihood method, Lagrangian optimization.

result Valid confidence intervals with tighter and more accurate estimates than existing methods.

reinforcement learning off-policy evaluation confidence intervals generalized empirical likelihood behavior policies

2020-10-22abs ↗pdf ↗

Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learningstat.ME

Responds to critiques on tests for causal parameter confidence intervals.

problem Testing nominal confidence interval coverage for causal parameters estimated by machine learning.

method Rejoinder to critiques on nearly assumption-free tests.

result Clarifies and supports the original research's approach.

rejoinder statistical science confidence intervals machine learning causal parameters

2020-08-07abs ↗pdf ↗

Open Problem: Tight Online Confidence Intervals for RKHS Elementsstat.ML

Research aims to improve confidence intervals for RKHS elements in online learning.

problem Improper confidence intervals lead to suboptimal regret bounds in kernel-based bandit and reinforcement learning.

method Formalizes the open problem of online confidence intervals in RKHS and reviews existing results.

result Identifies the online nature of observation points as the main challenge for tight confidence intervals.

confidence intervals reproducing kernel Hilbert space online learning kernel bandit reinforcement learning

2021-10-28abs ↗pdf ↗

Tighter Confidence Bounds for Sequential Kernel Regressionstat.ML

New tighter confidence bounds for sequential kernel regression.

problem Quantifying uncertainty in sequential learning algorithms.

method Martingale tail inequalities and conic programming.

result New confidence bounds are tighter than existing ones.

confidence bounds sequential learning kernel regression martingale inequalities conic programming

2024-03-19abs ↗pdf ↗

Geometry of the Minimum Volume Confidence Setsstat.ML

This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.

problem Determining if minimum-volume confidence sets for multinomial outcomes are disjoint.

method Enumerating and covering the continuous regions of the exact p-value function to study the geometry of minimum-volume confidence sets.

result The geometry of minimum-volume confidence sets for multinomial parameters is studied, providing insights into their structure and properties.

minimum-volume confidence sets multinomial parameters p-value function disjoint sets A/B testing

2022-02-16abs ↗pdf ↗

Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classificationcs.LG

The paper shows over-confidence in models isn't just due to over-parametrization.

problem Over-confidence in machine learning models, especially in binary classification.

method Theoretical analysis of logistic regression and other binary classification problems.

result Logistic regression is inherently over-confident in certain settings, but over-confidence is not always the case.

machine learning over-confidence over-parametrization logistic regression binary classification

2021-02-15abs ↗pdf ↗

Calibrated neighborhood aware confidence measure for deep metric learningcs.LG

This paper improves confidence measurement in deep metric learning models.

problem Measuring confidence in deep metric learning models is challenging.

method Approximates class distributions using Gaussian kernel smoothing and calibrates the confidence metric.

result Improves generalization and robustness of deep metric learning models.

deep metric learning confidence measurement calibration Gaussian kernel latent space

2020-06-08abs ↗pdf ↗

Meta-Learned Confidence for Few-shot Learningcs.LG

Meta-learned confidence improves few-shot learning accuracy.

problem Improving accuracy in few-shot learning with unreliable model confidence.

method Meta-learning confidence weights for query samples to improve transductive inference performance.

result Meta-learned confidence leads to new state-of-the-art results on benchmark datasets.

few-shot learning transductive inference confidence weighting meta-learning distance metric

2020-02-27abs ↗pdf ↗

Optimal Confidence Regions for the Multinomial Parameterstat.ML

The paper develops optimal confidence regions for categorical data.

problem Constructing tight confidence regions for categorical data.

method Develops new theory for minimum average volume confidence regions.

result Shows optimality of the regions for categorical data and its implications for machine learning.

confidence regions multinomial parameter categorical data statistical inference machine learning

2020-02-03abs ↗pdf ↗

Learning Fair Representations with High-Confidence Guaranteescs.LG

Paper defines and solves a problem in representation learning to ensure fairness with high confidence.

problem Learning fair representations with high confidence guarantees for all downstream tasks.

method Formally defines the problem, introduces FRG framework, proves high probability fairness, and demonstrates effectiveness empirically.

result FRG framework provides high-confidence guarantees for limiting unfairness across all downstream models and tasks.

representation learning fairness high-confidence guarantees downstream tasks downstream models

2023-10-23abs ↗pdf ↗

Distance-Based Learning from Errors for Confidence Calibrationcs.LG

DBLE improves confidence calibration of DNNs by learning distances in representation space.

problem Poor confidence calibration of deep neural networks (DNNs).

method DBLE trains a confidence model jointly with the classification model, using distances in the representation space.

result DBLE outperforms alternative single-model confidence calibration approaches and ensemble methods.

confidence calibration deep neural networks distance-based learning representation space mis-classified samples

2019-12-03abs ↗pdf ↗

Differentiable Linear Bandit Algorithmcs.LG

A new differentiable UCB algorithm for linear bandits learns adaptive confidence bounds.

problem Inability of UCB to strike optimal exploration-exploitation due to confidence bounds.

method Proposes a differentiable linear bandit algorithm and a gradient estimator for learning adaptive confidence bounds.

result Achieves a

ilde{\mathcal{O}}(\hatβ\sqrt{dT})

upper bound of

T

-round regret.

bandit algorithms confidence bounds differentiability gradient ascent regret minimization

2020-06-04abs ↗pdf ↗

Imitation Learning from Imperfect Demonstrationcs.LG

Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel approach that utilizes confidence scores, which describe the quality of demonstrat…

2019-01-27abs ↗pdf ↗

Pessimism for Offline Linear Contextual Bandits using

\ell_p

Confidence Setscs.LG

New method optimizes offline linear bandits using different confidence sets.

problem Optimizing offline learning for linear contextual bandits.

method Introduces a family of pessimistic learning rules based on

\ell_p

confidence sets.

result The

\hatπ_\infty

rule achieves minimax performance and strictly dominates other predictors.

offline learning linear contextual bandits pessimism confidence sets minimax

2022-05-21abs ↗pdf ↗

Principled Confidence Estimation for Deep Computed Tomographycs.LG

Framework for confidence estimation in deep CT reconstructions.

problem Uncertainty in deep learning-based CT reconstructions.

method Sequential likelihood mixing framework with log-linear forward model.

result Deep models yield tighter confidence regions than classical methods.

Computed Tomography Confidence Estimation Deep Learning Uncertainty Medical Imaging

2026-02-05abs ↗pdf ↗

On the Role of Dataset Quality and Heterogeneity in Model Confidencecs.LG

The paper investigates how dataset quality and heterogeneity affect model confidence in machine learning.

problem Understanding how dataset quality and heterogeneity impact model confidence in machine learning.

method The study uses theoretical explanations and experimental demonstrations to investigate the effects of dataset size, label noise, and class heterogeneity on model confidence.

result Label noise reduces model confidence, while reduced dataset size increases it, and class heterogeneity leads to inconsistent confidence across classes.

dataset quality model confidence label noise dataset size class heterogeneity

2020-02-23abs ↗pdf ↗

Learning from Similarity-Confidence Datastat.ML

Paper tackles weakly supervised learning from similarity-confidence data.

problem Learning binary classifier from unlabeled data pairs with confidence of similarity.

method Proposes an unbiased estimator of classification risk from Sconf data and risk correction scheme.

result Demonstrates effectiveness of proposed methods through experiments.

weakly supervised learning similarity-confidence data binary classifier unbiased estimator risk correction

2021-02-13abs ↗pdf ↗

Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networkscs.LG

Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…

2018-09-24abs ↗pdf ↗

Confidence Intervals for Testing Disparate Impact in Fair Learningstat.ML

We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence int…

2018-07-17abs ↗pdf ↗

Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systemscs.LG

The paper presents a method to compute trusted confidence bounds for LECs in CPS.

problem Non-transparent predictions of LECs make CPS safety challenging.

method Inductive Conformal Prediction (ICP) and Triplet Network architecture.

result Efficient real-time computation of trusted confidence bounds.

Cyber-Physical Systems Learning Enabled Components Deep Neural Networks Confidence Bounds Inductive Conformal Prediction

2020-03-11abs ↗pdf ↗

Valid Inference for Machine Learning Model Parametersstat.ML

The paper provides a method to find optimal machine learning model parameters with confidence.

problem Finding optimal machine learning model parameters that generalize well to the entire population.

method Constructs valid confidence sets for the optimal parameter using only training data.

result Valid confidence sets for optimal machine learning model parameters can be generated using bootstrapping techniques.

machine learning confidence sets overtraining generalization bootstrapping

2023-02-21abs ↗pdf ↗

Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learningstat.ML

New method for accurate uncertainty estimation in deep learning predictions.

problem Insufficient methods for assessing prediction uncertainty in deep learning.

method Valid non-parametric bootstrap method for deep neural networks.

result Accurate confidence intervals and simultaneous confidence bands for survival data.

deep learning uncertainty quantification confidence intervals bootstrap method survival analysis

2024-06-20abs ↗pdf ↗

Learning across label confidence distributions using Filtered Transfer Learningcs.LG

Improved drug-protein interaction prediction using FTL method.

problem Predicting drug-protein interactions from noisy data with uncertain labels.

method Filtered Transfer Learning (FTL) method that fine-tunes a deep neural network across multiple tiers of data confidence.

result FTL method outperforms deep neural networks trained on single confidence ranges.

Transfer Learning Filtered Transfer Learning Deep Neural Networks Drug-Protein Interactions Uncertain Labels

2020-06-03abs ↗pdf ↗

Cross-validation Confidence Intervals for Test Errorstat.ML

The paper improves confidence intervals for test error using cross-validation.

problem Improving confidence intervals for test error in machine learning.

method Develops central limit theorems and consistent estimators for cross-validation.

result Provides asymptotically-exact confidence intervals and hypothesis tests.

cross-validation confidence intervals test error machine learning

2020-07-24abs ↗pdf ↗

A Confidence Interval for the

\ell_2

Expected Calibration Errorstat.ML

Develops confidence intervals for ECE, a measure of model calibration.

problem Ensuring the calibration of probabilistic predictions in machine learning models.

method Develops confidence intervals for the

\ell_2

Expected Calibration Error (ECE), considering top-1-to-

k

calibration.

result Shows asymptotic normality and different convergence rates for calibrated and miscalibrated models, developing methods to construct valid confidence intervals.

machine learning model calibration Expected Calibration Error (ECE)confidence intervals asymptotic normality

2024-08-16abs ↗pdf ↗

Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networksstat.ML

Paper improves confidence intervals and variance estimation for deep learning models.

problem Improving confidence intervals and variance estimation in deep learning models.

method Residual-based framework for conditional variance estimation; robust bootstrap procedure for confidence intervals.

result First non-asymptotic bounds for variance estimation using ReLU networks.

Confidence intervals Conditional variance Deep learning ReLU networks Bootstrap

2024-12-29abs ↗pdf ↗

Confidence Scores Make Instance-dependent Label-noise Learning Possiblecs.LG

A method uses confidence scores to handle noisy labels for each instance.

problem Learning with noisy labels where each instance's label can randomly change.

method Introduces confidence-scored instance-dependent noise (CSIDN) to estimate transition distributions for each instance.

result Demonstrates the utility and effectiveness of CSIDN through experiments with synthetic and real-world noise.

confidence scores instance-dependent noise label-noise learning forward correction noisy labels

2020-01-11abs ↗pdf ↗

Confidence Estimation via Auxiliary Modelscs.CV

Paper introduces a novel method for estimating model confidence in deep neural classifiers.

problem Reliable confidence estimation for deep neural classifiers in safety-critical applications.

method Proposes a novel target criterion (true class probability) and learns it from data with an auxiliary model.

result The proposed method outperforms strong baselines in various tasks and network architectures.

confidence estimation deep neural classifiers auxiliary models true class probability maximum class probability

2020-12-11abs ↗pdf ↗

Confidence-Aware Learning for Deep Neural Networkscs.LG

Paper proposes a method to improve deep neural networks' confidence estimates.

problem Overconfident predictions limit practical use of deep neural networks in safety-critical applications.

method Proposes a novel loss function, Correctness Ranking Loss, to regularize class probabilities.

result The method produces well-ranked confidence estimates and is effective for out-of-distribution detection and active learning.

deep neural networks confidence estimation ordinal ranking correctness ranking loss overconfidence mitigation

2020-07-03abs ↗pdf ↗

Understanding Softmax Confidence and Uncertaintycs.LG

Softmax confidence misrepresents uncertainty in neural networks.

problem Neural networks fail to increase uncertainty on out-of-distribution data.

method Investigates two implicit biases in softmax confidence.

result Softmax confidence correlates with epistemic uncertainty due to decision boundary structure and deep network filtering.

softmax confidence uncertainty neural networks out-of-distribution bias

2021-06-09abs ↗pdf ↗

Imputation Uncertainty in Interpretable Machine Learning Methodsstat.ML

Study compares imputation methods' effects on IML confidence intervals.

problem Missing data impacts IML interpretation and confidence intervals.

method Compared single vs multiple imputation methods on IML confidence intervals.

result Multiple imputation provides closer coverage to nominal than single imputation.

missing data interpretable machine learning imputation methods confidence intervals feature importance

2025-12-19abs ↗pdf ↗

Optimal Learning via Moderate Deviations Theorystat.ML

Optimal learning via moderate deviations theory improves statistical accuracy.

problem Statistical estimation of expected loss in various models.

method Develops confidence intervals using moderate deviation principle.

result Proposed confidence intervals are statistically optimal.

moderate deviations statistical learning confidence intervals stochastic programming optimal estimation

2023-05-23abs ↗pdf ↗

TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASReess.AS

TeLeS improves ASR confidence estimation by considering temporal alignment and lexical errors.

problem Inaccurate confidence scores from E2E ASR models, especially for overconfident predictions.

method Proposes TeLeS, a novel confidence score that considers temporal alignment and lexical errors, and uses shrinkage loss to handle data imbalance.

result TeLeS generalizes well across different languages and ASR models, leading to significant WER reduction.

End-to-End ASR Confidence Estimation Temporal Lexeme Similarity Active Learning Word Error Rate

2024-01-06abs ↗pdf ↗

Confidence Calibration for Convolutional Neural Networks Using Structured Dropoutcs.LG

In classification applications, we often want probabilistic predictions to reflect confidence or uncertainty. Dropout, a commonly used training technique, has recently been linked to Bayesian inference, yielding an efficient way to quantify uncertainty in neural network models. However, as previously demonstrated, conf…

2019-06-23abs ↗pdf ↗

Estimating Classification Confidence Using Kernel Densitiesstat.ML

The paper tackles confidence calibration for exploratory machine learning problems.

problem Difficulty in curating datasets and confusion about category validity.

method Introduces four new algorithms for category-specific confidence estimation, including kernel density ratios.

result Kernel density ratios provide a novel approach to confidence calibration, especially for exploratory problems.

confidence calibration exploratory machine learning category-specific kernel density ratios post-hoc calibration

2022-07-13abs ↗pdf ↗

Flowing with Confidencestat.ML

Generative models need per-sample confidence scores to improve quality and stability.

problem Generative models produce unreliable outputs and lack confidence measures.

method Flow Matching with Confidence (FMwC) injects noise and integrates it through the network, providing per-sample confidence scores.

result The confidence score correlates with the velocity field's divergence, offering insights into generative processes.

generative models confidence scores per-sample evaluation flow matching confidence integration

2026-05-18abs ↗pdf ↗

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learningcs.AI

In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward fun…

2017-07-03abs ↗pdf ↗

Spikes as regularizerscs.NE

We present a confidence-based single-layer feed-forward learning algorithm SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of activation spikes. We adaptively update a weight vector relying on confidence estimates and activation offsets relative to previous activity. We regularize updates proportion…

2016-11-18abs ↗pdf ↗

How to Shrink Confidence Sets for Many Equivalent Discrete Distributions?stat.ML

We shrink confidence sets for equivalent discrete distributions using permutation equivalence.

problem Building high-probability confidence sets for equivalent discrete distributions.

method Exploiting permutation-equivalence to refine confidence sets.

result Confidence sets shrink at asymptotic rates of

O(1/\sqrt{\sum_{k\in \mathcal K} n_k})

and

O(1/\max_{k\in K} n_{k})

Confidence Sets Permutation-Equivalence Machine Learning High-Probability Bounds Reinforcement Learning

2024-07-22abs ↗pdf ↗

Conformal confidence sets for biomedical image segmentationstat.ML

Develops spatial uncertainty guarantees for image segmentation models.

problem Ensuring reliable segmentation predictions for biomedical images.

method Adapting conformal inference to imaging, using transformed logit scores and calibration datasets.

result Confidence sets provide spatial uncertainty guarantees with desired probability.

image segmentation conformal inference machine learning biomedical tumor

2024-10-04abs ↗pdf ↗

Trust, or Don't Predict: Introducing the CWSA Family for Confidence-Aware Model Evaluationcs.LG

New metrics CWSA and CWSA+ improve model evaluation under confidence thresholds.

problem Lack of metrics capturing model reliability under confidence thresholds.

method Introducing CWSA and CWSA+ metrics that reward confident accuracy and penalize overconfident mistakes.

result CWSA and CWSA+ outperform classical metrics in trust-sensitive tests.

machine learning model evaluation confidence thresholds selective prediction reliability metrics

2025-05-24abs ↗pdf ↗

Label-Only Membership Inference Attackscs.CR

New attacks can infer model training membership using only label predictions, not confidence.

problem Inferring whether a data point was used to train a machine learning model.

method Evaluate model's predicted labels under perturbations to infer membership.

result Label-only attacks perform as well as confidence-based attacks and break defenses that rely on confidence masking.

membership inference machine learning privacy leakage label-only attacks confidence masking

2020-07-28abs ↗pdf ↗

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probescs.LG

We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probe…

2018-05-14abs ↗pdf ↗

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Settingstat.ME

ACORE improves hypothesis testing and confidence sets in likelihood-free inference.

problem Constructing hypothesis tests and confidence sets in likelihood-free inference settings.

method Formulates classical LRT as a classification problem, uses machine learning to improve estimates.

result Demonstrates improved accuracy in hypothesis testing and confidence sets.

likelihood-free inference hypothesis testing confidence sets machine learning parameter estimation

2020-02-24abs ↗pdf ↗

Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluationcs.LG

This work uses statistical bootstrapping to provide accurate confidence intervals for policy value in reinforcement learning.

problem Bias in estimating policy value using empirical transitions and rewards.

method Statistical bootstrapping to produce calibrated confidence intervals for the true policy value.

result Statistical bootstrapping can yield correct confidence intervals under certain conditions, and mechanisms are proposed to mitigate these conditions.

reinforcement learning bootstrapping uncertainty estimation policy evaluation confidence intervals

2020-07-27abs ↗pdf ↗