Proof connects Gittins indices to Bayesian upper confidence bounds for patient agents.
problem Connecting Gittins indices to Bayesian upper confidence bounds for patient agents.
method Proof using Gaussian multi-armed bandit problem with discount factor γ.
result Gittins index equals γ-quantile of posterior mean plus vanishing error term.
Study detects Chinese stock market bubbles using LPPLS confidence indicator.
problem Early detection of stock market bubbles in China.
method LPPLS confidence indicator applied to CSI 300 index data.
result LPPLS detects positive and negative bubbles with high accuracy.
Develops a model to predict BitCoin prices influenced by confidence.
problem Predicting BitCoin prices influenced by confidence and sentiment.
method Continuous-time bivariate model with a delay between confidence indicator and BitCoin price.
result Arbitrage-free model and quasi-closed formula for European style derivatives.
Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.
problem Challenges in predicting stock market indices due to isolated time series treatment and simple regression.
method Fusion of stock latent embeddings, binary encoding classification, and confidence-guided prediction.
result Cubic outperforms state-of-the-art baselines in stock index prediction tasks.
Study detects Bitcoin bubbles and predicts crashes using adaptive multilevel time series detection.
problem Detecting and predicting Bitcoin price bubbles and crashes.
method Adaptive multilevel time series detection based on LPPLS model.
result LPPLS confidence indicator provides effective warnings for bubble detection and crash prediction.
Proposes sparsified intervals for high-dimensional regression coefficients.
problem Challenges of high-dimensional regression coefficient inference.
method Sparsified simultaneous confidence intervals.
result Intervals can shrink some coefficients to zero, indicating unimportance.
In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created…
Survey of methods to calibrate neural network predictions.
problem Ensuring neural networks provide accurate confidence levels.
method Empirical comparison of calibration methods.
result Various techniques for calibrating neural networks.
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.
Develops a novel fast bootstrap for dependent data with higher-order accuracy.
problem Estimation of parametric and semi-parametric models for dependent data.
method i.i.d. resampling of smoothed moment indicators, asymptotic refinements under mild assumptions.
result Higher-order correct asymptotic confidence distributions and confidence intervals.
New algorithm optimizes best arm identification in linear bandits.
problem Best arm identification in linear bandits with fixed confidence.
method Algorithm minimizes geometric overlap of confidence sets, is adaptive and efficient.
result Theoretical analysis shows optimality in many cases.
GPT-4 assesses its confidence in answering USMLE questions with and without feedback.
problem Understanding AI's performance in healthcare applications, especially in sensitive areas like medical education.
method Used a prompting technique to evaluate GPT-4's confidence scores before and after answering USMLE questions, categorized into with and without feedback.
result Feedback influences relative confidence but doesn't consistently increase or decrease it.
Improved AI lung ultrasound segmentation using expert confidence values.
problem Label uncertainty in lung ultrasound due to subjective interpretation by radiologists.
method Designing a data annotation protocol capturing expert confidence, training AI on binarized labels with confidence thresholds.
result Improved AI segmentation and better clinical outcomes (e.g., S/F oxygenation ratio estimation, patient readmission prediction).
Novel framework for contextual anomaly detection models uncertainty.
problem Identifying anomalies in target variables influenced by contextual variables.
method Normalcy score (NS) framework using heteroscedastic Gaussian process regression.
result NS outperforms state-of-the-art methods in detection accuracy and interpretability.
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
problem Uncertainty quantification in automated valuation models with spatial dependencies.
method Survey and demonstration of various spatially weighted approaches to adjust conformal prediction confidence sets.
result Spatially weighted CP makes confidence sets more consistently calibrated across geographical regions.
Study detects endogenous bubbles in meme stocks using CI.
problem Detecting endogenous bubbles in meme stocks.
method Used Log-Periodic Power Law (LPPL) Confidence Indicator (CI).
result CI detected numerous bubbles in meme stocks but struggled with predicting exogenous rallies.
New method improves model calibration by adjusting confidence based on prediction correctness.
problem Improving model confidence alignment with true class probabilities.
method Post-hoc calibration objective using transformed samples for training.
result Competitive calibration performance on in-distribution and out-of-distribution test sets.
Simulation studies show resampling methods can be reliable for causal graph confidence.
problem Determining when causal discovery results can be trusted in real-world settings.
method Evaluation of subsampling and sampling with replacement methods.
result Subsampling and sampling with replacement performed well in indicating graph feature confidence.
BoC probe assesses neural network confidence coherence, revealing architecture-specific uncertainty.
problem Poor calibration and OOD detection in neural networks.
method Bag-of-Coins (BoC) probe compares softmax confidence to pairwise dominance probabilities.
result BoC reveals clear ID/OOD separation for some architectures but not others.
Data augmented bootstrap unifies various confidence interval construction methods.
problem Constructing confidence intervals from data transformations.
method Data augmented bootstrap (DAB) framework.
result Establishes theoretical coverage results for DAB methods.
RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.
problem Mixup degrades SSL performance by compromising artificial labels purity.
method RegMixMatch integrates high- and low-confidence samples, uses class-aware Mixup, and mitigates confirmation bias.
result RegMixMatch achieves state-of-the-art performance in SSL benchmarks.
This study examines representation bias in open-source Qwen models for investment decisions.
problem Representation bias in financial applications of large language models.
method Balanced round-robin prompting over 150 U.S. equities, constrained decoding, token-logit aggregation.
result Firm size and valuation increase model confidence, while risk factors decrease it.
Efficiently computes indices for UCB and DMED algorithms in reinforcement learning.
problem Efficiently compute indices for UCB and DMED algorithms in reinforcement learning.
method Developed efficient methods to compute indices for UCB and DMED algorithms by solving systems of equations.
result Significant computational time savings and improved regret performance demonstrated.
New method reduces bias in deep neural classifier uncertainty estimates.
problem Bias in uncertainty estimates for confident predictions.
method Selective estimation using earlier model snapshots.
result Consistently better uncertainty estimates than existing methods.
Adapts model-based advice to stabilize black-box policies for nonlinear control.
problem Stabilizing machine-learned policies for nonlinear control with limited model information.
method Proposes an adaptive λ-confident policy to combine black-box and model-based advice. result Proves the stability of the adaptive λ-confident policy and its competitive ratio. δ-CLUE generates diverse explanations for model uncertainty.
problem Lack of constraints in generating explanations for uncertainty estimates.
method Augmenting CLUE approach to provide a set of plausible explanations.
result Returns a set of diverse inputs that yield confident predictions.
New method improves neural network classification accuracy and confidence.
problem Improving neural network classification accuracy and confidence.
method Pairwise coupling of convolutional neural networks.
result Bayes covariant method provides higher accuracy and better sureness predictions.
Paper bounds prediction error for misspecified Gaussian process models.
problem Guaranteeing model confidence for nonparametric Gaussian process regression.
method Derives an upper bound for mean square prediction error using pseudo-concave optimization.
result Upper bound for mean square prediction error of misspecified models.
Statistical test rejects market efficiency using entropy from price returns.
problem Determining market efficiency using information theory.
method Symbolic representation of price returns, Shannon entropy, and statistical test.
result Rejects market efficiency hypothesis for various datasets.
Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfide…
A new OOD detector using an overlap index improves accuracy without high computational costs.
problem Effective OOD detection for machine learning models in open-world scenarios.
method Proposes an overlap index-based confidence score function for OOD detection.
result The proposed method achieves competitive accuracy with lower computational costs compared to state-of-the-art detectors.
The paper proposes a method to identify model uncertainty in mechanical presses using optimal design of experiments.
problem Model uncertainties in forming machines impair controller performance.
method Parameter identification, optimal design of experiments, and hypothesis testing.
result Identifies inconsistencies in parameter estimates as indicators of model uncertainty.
CLUE method interprets uncertainty from BNNs by showing how inputs change to increase confidence.
problem Lack of work on interpreting uncertainty estimates from probabilistic models.
method CLUE method uses counterfactual explanations to interpret uncertainty from BNNs.
result CLUE outperforms baselines and helps practitioners understand predictive uncertainty.
Study of 13,456 hot stellar systems reveals multi-layered grouping.
problem Understanding physical and evolutionary properties of Hot Stellar Systems.
method Used stellar mass, effective radius, and mass-to-luminosity ratio to group HSS into eight homogeneous ellipsoidal groups, then merged them through a multi-phased syncytial algorithm.
result Identified two complex-structured groups of HSS, one older and smaller, the other brighter and younger.
Study combines VaR and ES forecasts using MCS to improve risk predictions.
problem Combining VaR and ES forecasts to improve risk predictions under uncertainty.
method Employed Model Confidence Set (MCS) methodology to identify best-performing models and combine their forecasts.
result Proposed combined predictors are robust and pass standard backtests.
Improves statistical inference using machine learning predictions with imputed data.
problem Invalid statistical inference due to machine learning prediction errors.
method Bootstrap confidence intervals for nonuniform samples and arbitrary imputed features.
result Valid confidence intervals without assumptions on machine learning model quality.
Focal loss reduces model curvature for better calibration.
problem Improving model confidence in classification problems.
method Geometric interpretation of focal loss to reduce curvature.
result Focal loss reduces the curvature of the loss surface, enhancing model calibration.
The paper analyzes LETF option markets using moneyness scaling to find statistical arbitrage opportunities.
problem Statistical discrepancies between levered and unlevered ETF option implied volatility smiles.
method Bootstrap uniform confidence bands, dynamic semiparametric factor model, moneyness scaling, Heston stochastic volatility.
result Trading opportunities exist on LETF market, and a statistical arbitrage strategy generates positive returns.
New method improves AI system's uncertainty estimation.
problem Precise uncertainty estimation in AI predictions.
method Information Aware Max-Norm Dirichlet Networks.
result Outperforms state-of-the-art neural networks for uncertainty estimation.
EBUCB framework achieves optimal regret with bounded approximate inference error.
problem Theoretical gap between practical performance and theoretical justification of Bayesian bandit algorithms with approximate inference.
method Enhanced Bayesian Upper Confidence Bound (EBUCB) framework that accommodates bandit problems with approximate inference.
result EBUCB achieves optimal regret order O(logT) under certain conditions on inference error. This paper introduces online algorithms to estimate robust geometric median in large data streams.
problem Detecting outliers in large data sets using robust statistical measures.
method Online stochastic Newton methods for estimating the geometric median.
result Rates of convergence for online estimation of the geometric median.
New methods reduce bias in estimating calibration error.
problem Reducing bias in estimating calibration error.
method Synthesizing model outputs and using equal-mass bins.
result Two reliable calibration-error estimators found: debiased estimator and ECE_sweep.
Deep neural network improves cancer mutation calls with confidence.
problem Improving accuracy and confidence in somatic variant calls from cancer sequencing.
method Deep Bayesian Recurrent Neural Network (RNN) with flexible priors.
result Enhanced confidence in mutation calls without performance degradation.
ARTEO algorithm optimizes safety-critical systems with uncertainty.
problem Decision-making under uncertainty with safety constraints in real-time optimization.
method ARTEO algorithm uses multi-armed bandits as a mathematical programming problem subject to safety constraints, learning unknown characteristics through exploration and incorporating uncertainty quantification.
result ARTEO achieves less cumulative regret with accurate and safe decisions.
The paper proposes a method for constructing confidence sets that adapt to the cardinality of the smallest component of a mean vector.
problem Forming confidence sets for the smallest component of an unknown mean vector.
method Sample splitting and self-normalization approach to test each component for being the smallest, maintaining validity regardless of d and n. result The proposed tests achieve the local minimax separation rate and robust to heavy-tailed distributions.
New metric space for ReLU codes connects to network safety and robustness.
problem Lack of metrics capturing network safety and robustness beyond accuracy.
method Introduces a metric space of ReLU activation codes with a truncated Hamming distance.
result Establishes an isometry between ReLU codes and polyhedral bodies related to safety and robustness.
Large learning rates enhance model robustness and compressibility.
problem Achieving robustness and resource-efficiency in machine learning models.
method Identifying and utilizing large learning rates as a facilitator for robustness and compressibility.
result Large learning rates produce desirable representation properties and compare favorably to other methods.
Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.
problem Twin difficulties of modeling and searching complex functions in high dimensions.
method Exploiting conditional independence, Thompson Sampling respecting bilateral uncertainty (BU).
result Thompson Sampling with BU is more effective than the additive approximation in small budgets.