LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.
problem Optimizing sequential decisions with a linear threshold under resource limitations.
method LinearAPT, an adaptive algorithm for fixed-budget TLB problem.
result LinearAPT achieves theoretical upper bounds and robust performance on various datasets.
Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.
problem Machine learning models can be unfair to certain groups based on sensitive attributes.
method Proposes threshold invariant fairness and uses two approximation methods to equalize risk distributions.
result Demonstrates effectiveness in alleviating threshold sensitivity in fairness models.
Comparison of decision curve analysis and cost curves for model evaluation.
problem Evaluating classification performance across different operating contexts.
method Comparison of Decision Curve Analysis (DCA) and Cost Curves.
result DCA and Cost Curves are closely related, with Brier curves being more generally applicable.
We define the information threshold in Bayesian decision-making.
problem Understanding the optimal amount of information for reliable classification.
method Defining the information threshold as the point of maximum curvature in the prior vs. posterior curve.
result At the information threshold, additional evidence does not significantly improve posterior probability.
GrAPL identifies arms above a threshold using graph similarity.
problem Efficiently identifying arms with means above a threshold in a graph-structured bandit problem.
method Thresholding Graph Bandits with GrAPL algorithm exploiting graph structure and reward homophily.
result GrAPL effectively identifies arms above a threshold using graph structure and reward homophily.
The study analyzes when Bayesian averaging over decision trees is reliable.
problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.
Heuristic tool estimates lactate threshold for easier training decisions.
problem Improving lactate threshold estimation for recreational runners.
method Formalized lactate threshold principles, iterative methodology, heuristic approach.
result Heuristic %60 of 'endurance running speed reserve' is reliable and accessible.
A RL algorithm learns optimal multi-threshold policies for MDPs.
problem Overcoming the curse of dimensionality in MDPs.
method Structure-aware RL algorithm exploiting multi-threshold optimal policies.
result The algorithm converges to the optimal policy asymptotically.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
problem Safety in reinforcement learning in unknown and uncertain environments.
method Growing-Window estimator sampling and Stochastic Pessimistic-Optimistic Thresholding (SPOT) algorithm.
result Achieves sublinear regret and constraint violation of i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) . SpaRCe optimizes reservoir computing by learning neuron thresholds to improve performance and prevent forgetting.
problem Improving performance and preventing forgetting in reservoir computing networks.
method Integrates neuron-specific learnable thresholds to optimize sparsity without altering dynamics, learning read-out weights and thresholds via gradient rule.
result Threshold learning improves performance and alleviates catastrophic forgetting.
Paper proposes a new decision strategy for open set recognition.
problem Existing OSR methods are limited in recognizing unknown classes and setting decision thresholds.
method Introduces a collective decision-based OSR framework (CD-OSR) using Hierarchical Dirichlet process (HDP).
result CD-OSR can simultaneously implement open set recognition and new class discovery.
Decision trees and shallow neural networks have different geometric complexities, impacting their interpretability and accuracy.
problem The geometric simplicity of decision boundaries in decision trees conflicts with the approximation capabilities of shallow neural networks.
method Analysis of the Radon total variation (RTV) seminorm to compare geometric complexity of decision regions and neural network approximations.
result Smooth barrier scores can approximate decision regions with finite RTV, but their performance depends on the tube-mass condition near the decision boundary.
Investing in declining tech boosts profits, study finds.
problem Optimal decision-making in declining profit streams.
method Modeling profit stream as Brownian motion with negative drift, analyzing thresholds for investment and exit.
result Investment threshold decreases in volatility when profit boost is large.
Faster method detects bias in lending, hiring, and policing decisions.
problem Detecting bias in lending, hiring, and policing decisions.
method Developed a faster method for fitting threshold tests using discriminant distributions.
result Reduced computation time from hours to minutes.
Critiques binary classification evaluation methods, advocating for proper scoring rules.
problem The dominance of top-K metrics and fixed-threshold evaluations in machine learning.
method Introduces a decision-theoretic framework mapping evaluation metrics to their use cases, and implements a clipped Brier score variant.
result Demonstrates the clinical utility of proper scoring rules through a Python package, exttt{briertools}.
New RL algorithm learns optimal policies for MDPs using known structure.
problem Overcoming curse of dimensionality and modeling in MDPs.
method Structure-aware online learning algorithm exploiting known threshold policy.
result Proposed algorithm converges to optimal policy with significant speed improvements.
This paper improves thresholding bandit problem algorithms.
problem Identifying arms with rewards above a threshold.
method Asynchronous parallel empirical variance guided algorithms.
result Optimal round complexity improvement for all arms with bounded high order moments.
The paper establishes a nearly-sharp statistical threshold for efficient learning in Latent MDPs with separated components.
problem Learning Latent Markov Decision Processes (LMDPs) with separated components.
method The paper considers various notions of separation and establishes a nearly-sharp statistical threshold for efficient learning. It also presents a quasi-polynomial algorithm with time complexity scaling in terms of the statistical threshold under a weaker assumption of separability under the optimal policy, and a near-matching time complexity lower bound under the exponential time hypothesis.
result Establishes a nearly-sharp statistical threshold for efficient learning in Latent MDPs with separated components.
MLShrink integrates machine learning with wavelet shrinkage for denoising.
problem Denoising signals with uncertain magnitudes
method Combines wavelet shrinkage with machine learning
result Preserves simplicity for signal coefficients while allowing data-adaptive decisions for ambiguous coefficients
Develops RES metrics for stable rare-event forecasting evaluation.
problem Challenges in evaluating forecasts of rare events.
method Rare-event-stable (RES) metrics designed to maintain stable thresholds under extreme rarity.
result RES metrics maintain stable thresholds, consistent model rankings, and near-complete prevalence invariance.
New method controls false discoveries in online testing with deadlines.
problem Controlling false discoveries in online hypothesis testing with decision deadlines.
method Benjamini-Hochberg-type procedure over a moving window of hypotheses with adaptive threshold parameters.
result Controls false discovery rate at every stage and adaptively chosen stopping times.
We derive expressions for the first three moments of the decision time (DT) distribution produced via first threshold crossings by sample paths of a drift-diffusion equation. The "pure" and "extended" diffusion processes are widely used to model two-alternative forced choice decisions, and, while simple formulae for ac…
Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique…
The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.
problem Limitations of conventional stopping criteria in recursive Bayesian classification.
method Geometric interpretation of state posterior progression and analysis of conventional criteria.
result Proposes a new stopping criterion to overcome limitations of conventional methods.
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.
The paper tackles reward-relevance in offline RL with sparse decision dynamics.
problem Offline reinforcement learning with sparse decision dynamics and estimation sparsity.
method Reward-filtered least-squares policy evaluation using thresholded lasso.
result The method provides theoretical guarantees with sample complexity dependent on sparse component size.
Modeling firm default with a variable threshold based on management decisions.
problem Estimating default probability with asymmetric information.
method Generalized structural model with a variable default threshold.
result The information level significantly impacts default probability and credit yield spread.
Paper introduces WWAggr for ensemble CPD, improving accuracy and decision threshold selection.
problem Challenges in detecting abrupt distribution shifts in high-dimensional data streams.
method Introduces WWAggr, a novel task-specific ensemble aggregation method based on Wasserstein distance.
result Demonstrates WWAggr outperforms standard aggregation techniques and decision threshold selection.
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
New method controls false discoveries in real-time data streams.
problem Online testing of hypotheses with strict error constraints and no future data.
method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.
result Substantial power gain over existing online testing rules.
The Thresholding Method calibrates black box models to control loss function.
problem Balancing high power and loss function control in high-dimensional classification.
method Parameterizing strong signal points through thresholding.
result Empirical performance shows loss function control and reduced overfitting.
We explain why numbers occurring in the classification of polygon spaces coincide with numbers of self-dual equivalence classes of threshold functions, or of regular Boolean functions, or of decisive weighted majority games.
The paper examines fairness issues in decision-making systems when protected class labels are unobserved.
problem Fairness assessment challenges when protected class labels are unavailable.
method Decomposes biases in estimating outcome disparity via threshold-based imputation and proposes a weighted estimator.
result Threshold-based imputation generally overestimates disparities, while the weighted estimator has a simpler negative bias.
Deep learning uses ROC cost functions to improve virtual screening accuracy.
problem Challenges in training deep learning models for virtual screening, especially class imbalance and lack of ground truth labels.
method Proposes using ROC cost functions to optimize deep learning models for virtual screening, introduces new training schemes and cost functions.
result Demonstrates improved performance of ROC-based approaches on PubChem datasets.
Optimal policy found for observing noisy time series.
problem Minimizing posterior variance plus observation costs in discrete-time Gaussian random walks.
method Developed a simple threshold-based policy and proved its optimality.
result Simple threshold policy is optimal for observing noisy time series.
Decision forests learn to model text by evaluating categorical-set conditions.
problem Decision forests cannot directly model text features.
method Defined and learned conditions for categorical-set features, enabling efficient text modeling.
result Decision forests can now directly model text features.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
Proposes a simple neural network model similar to gradient boosted decision trees.
problem Building a neural network equivalent to gradient boosted decision trees.
method Converts an ensemble of decision trees to a neural network, relaxes properties, and trains a simple neural network model.
result The proposed Hammock model achieves similar performance to gradient boosted decision trees.
LLMs fail to match statistical ground truth despite stable run-to-run performance.
problem LLMs lack validation against statistical ground truth in automated scientific workflows.
method Introduced a behavioral evaluation framework for LLMs, separating four decision-making dimensions.
result LLMs can exhibit near-perfect stability but diverge from statistical ground truth.
Paper improves risk estimation for rare events in sequential decisions.
problem Estimating tail risk in high confidence levels with limited data.
method Combines extreme value theory and automated threshold selection.
result Improves performance in estimating tail risk for some distributions.
Study uses SGD to learn optimal thresholds for buying and selling stocks.
problem Optimizing investment strategies in fluctuating stock prices.
method Applying Kiefer--Wolfowitz Stochastic Gradient method to threshold strategies.
result Algorithm converges to log-optimal solution for threshold strategies.
Optimal purchasing policy for mean-reverting items with a finite deadline.
problem Minimizing cost of purchasing and holding mean-reverting items within a fixed time.
method Proved optimal policy as a time-variant threshold function, constructed with dynamic programming.
result Explicit equations for crossing time probability and overshoot expectation.
Risk-controlled post-processing optimizes decision policies under risk constraints.
problem Optimizing decision policies with risk constraints for better outcomes.
method Developed a post-processing algorithm that selects a threshold based on fitted fallback policy and score, leveraging tools from algorithmic stability and stochastic processes.
result The post-processed policy achieves precise expected risk control under exchangeability and meets or nearly meets risk budgets while preserving more agreement with the baseline.
Paper formalizes Simon's satisficing through FFSD, proving its equivalence to expected utility theory.
problem Formalizing Herbert Simon's bounded rationality concept in economic decision-making.
method Developed FFSD framework using Lean 4 theorem prover, proving equivalence to expected utility theory.
result Equivalence theorem linking FFSD to expected utility maximization for approximate indicator functions.
Consider a two-class classification problem where the number of features is much larger than the sample size. The features are masked by Gaussian noise with mean zero and covariance matrix Σ Σ Σ , where the precision matrix Ω = Σ − 1 Ω=Σ^{-1} Ω = Σ − 1 is unknown but is presumably sparse. The useful features, also unknown, are sparse and e…
Optimal decision trees are constructed via integer programming for better accuracy and interpretability.
problem Overfitting and loss of interpretability in decision trees.
method Mixed integer programming formulation to construct optimal decision trees of a prespecified size, considering categorical and numerical features.
result Very good accuracy can be achieved with small trees using moderately-sized training sets.
The study explores fairness in classifier post-processing methods.
problem Achieving fairness in binary decision-making classifiers with imperfect information.
method Examines fairness properties of post-processing calibrated scores and deferring decisions.
result Deferring decisions can help achieve fairness in PPV, NPV, FPR, and FNR across protected groups.
This paper calibrates Gaussian process predictive distributions for Bayesian optimization to improve sampling decisions.
problem Lower-tail miscalibration in GP predictive distributions affects BO sampling decisions.
method Introduces goal-oriented calibration for GP predictive distributions below a threshold t t t . result Post-hoc method tcGP improves lower-tail calibration and BO performance.