Study optimality conditions for interval-valued optimization problems on Riemannian manifolds.
problem Optimizing interval-valued functions on Riemannian manifolds under a total order relation.
method Generalized Hukuhara directional differentiability to derive KKT-type optimality conditions.
result Derives optimality conditions for interval-valued optimization problems on Riemannian manifolds.
New method estimates optimal dose intervals for personalized treatment.
problem Learning optimal dose intervals from observational data.
method Probability dose interval (PDI) method using DC algorithm.
result Consistent policy with risk converging to best-in-class at root-n rate.
Study KKT conditions for multi-objective optimization on Hadamard manifolds.
problem Optimizing multi-objective interval-valued functions on Hadamard manifolds.
method Developed KKT conditions for Pareto optimal solutions under different ordering and convexity notions.
result Results are more general than on Euclidean spaces.
Develops methods for predicting and tolerating intervals for DTRs.
problem Constructing detailed prognostic information for patients following an estimated optimal treatment regime.
method Adapting existing interval estimation and prediction methods to the DTR setting.
result Extensive empirical evaluation of methods and discussion of practical aspects.
The paper improves methods for generating prediction intervals in regression.
problem Uncertainty quantification in regression models.
method Formalizes prediction interval generation as an optimization problem, studying generalization and calibration.
result Empirical demonstration of improved testing performances compared to existing methods.
Maximizes anomaly detection intervals for multivariate time series.
problem Detect anomalies in multivariate time series data.
method Maximizes Kullback-Leibler divergence between data within and outside intervals.
result Improves anomaly detection compared to independent time step methods.
EIM improves prediction interval tightness without distributional assumptions.
problem Generating tight prediction intervals for asymmetric distributions.
method Expanded Interval Minimization (EIM) using minibatch statistics for coverage and width optimization.
result EIM produces on average 1.37x tighter prediction intervals than prior techniques.
Paper tackles online learning with interval regret, achieving adaptive bounds.
problem Non-stationary online learning over time intervals.
method Two-layer online ensemble structure with gradient variation.
result Achieves strong theoretical guarantees with adaptive bounds.
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.
Algorithm finds significant sub-interval relationships in time series data.
problem Finding meaningful interactions in small sub-intervals of time series data.
method Fast-optimal guaranteed algorithm for sub-interval relationships (SIR).
result Algorithm identifies SIR relationships that are prominent in specific sub-intervals.
BCI provides calibrated prediction intervals for time series forecasts.
problem Calibration of prediction intervals for time series forecasts.
method BCI wraps around any time series forecasting models and optimizes interval lengths using dynamic programming.
result BCI achieves long-term coverage under arbitrary distribution shifts and temporal dependence.
Estimates policy value from off-policy data in contextual bandits.
problem Estimating policy value from limited off-policy data in contextual bandits.
method Empirical likelihood techniques for optimization.
result Improves over previous methods in finite sample regimes.
Optimizes data splitting for shorter conformal prediction intervals.
problem Minimizing prediction interval length while maintaining coverage.
method Theoretical framework for optimal data splitting in split conformal prediction.
result Analytical characterizations of length-optimal split ratios in various settings.
Develops a method for robust optimization with exact coverage confidence intervals.
problem Statistical inference and distributionally robust solutions for stochastic optimization problems.
method Generalized empirical likelihood framework based on f-divergence balls. result Provides a principled method for choosing distributional uncertainty regions for exact coverage.
Unified minimax value interval for off-policy evaluation and optimization.
problem Overcoming the exponential variance in off-policy evaluation and policy optimization.
method Unified minimax value interval using marginalized importance weights.
result Unified value interval with double robustness, valid when either value-function or importance-weight class is well specified.
In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
problem Inefficient conformal prediction intervals under heteroscedasticity and skewness.
method Co-optimization framework that learns center and radius through alternating optimization steps.
result CoCP yields consistently shorter intervals and state-of-the-art conditional coverage diagnostics.
AskewSGD optimizes quantized neural networks with interval-constrained optimization.
problem Training deep neural networks with quantized weights.
method Formulates QNN training as smoothed interval-constrained optimization, proposes AskewSGD for solving each subproblem.
result AskewSGD avoids projections and allows infeasible iterates, performs better than state-of-the-art methods.
Improved confidence interval estimation with control variates.
problem Estimating confidence intervals with minimal samples.
method Designing an estimation algorithm using control variates and leveraging order statistics.
result Improved asymptotic efficiency compared to existing algorithms.
Exact PA algorithms learn to rank with interval labels.
problem Learning to rank with interval labels.
method Exact passive-aggressive algorithms solving convex optimization problems.
result Maintains threshold ordering and achieves accurate classifiers.
Develops a method to estimate optimal policy value in online learning.
problem Challenges in evaluating ongoing policies in online learning environments.
method Doubly Robust Interval Estimation (DREAM) method.
result Valid inference on online conditional mean estimator with asymptotically normal distribution.
Proposes a new decision rule for continuous treatments.
problem Developing personalized treatment recommendations for continuous treatments.
method Jump interval-learning method to estimate conditional mean of outcomes.
result Optimal interval-valued decision rule (I2DR) for continuous treatments.
Efficient method for high confidence level inference using parallel stochastic optimization.
problem Uncertainty quantification for online estimation.
method Small number of independent multi-runs to construct t-based confidence intervals.
result Rigorous theoretical guarantee for exact coverage of confidence intervals.
New method calibrates deep models' uncertainty predictions.
problem Quantifying deep models' uncertainties for trustworthiness.
method Separate prediction and interval estimation with uncertainty matching.
result Significant improvements in model fidelity and calibration error.
An online framework optimizes efficiency in conformal prediction with a target miscoverage rate.
problem Achieving coverage and minimizing interval length in a sequential, online setting.
method Optimizes efficiency by directly optimizing the average length of intervals while maintaining coverage.
result Shows a gap between optimal performance for exchangeable and arbitrary sequences, and provides a matching algorithm for the Pareto-optimal settings.
Study optimizes prediction intervals in conformal regression.
problem Optimizing the length of prediction intervals in conformal regression.
method Introduces EffOrt and Ad-EffOrt methodologies to minimize interval length.
result Demonstrates theoretical and empirical improvements over classical methods.
Study on confidence intervals for Sliced Wasserstein distance, with optimal risk bounds.
problem Statistical inference for the Sliced Wasserstein distance.
method Construct minimax confidence intervals with adaptive lengths.
result Minimax optimal confidence intervals for the Sliced Wasserstein distance.
We investigate the growth optimal strategy over a finite time horizon for a stock and bond portfolio in an analytically solvable multiplicative Markovian market model. We show that the optimal strategy consists in holding the amount of capital invested in stocks within an interval around an ideal optimal investment. Th…
Study compares Interval Regression models and their performance.
problem Regression models struggle with interval target values.
method Comprehensive review and comparative analysis of existing Interval Regression models.
result No single model is universally optimal for all scenarios.
This paper improves offline contextual bandits using distributional robustness.
problem Improving offline contextual bandits with robustness.
method Extends Distributionally Robust Optimization (DRO) for offline contextual bandits, introducing a convex reformulation of Counterfactual Risk Minimization.
result Automatic calibration of asymptotic confidence intervals for policy optimization.
The recurrence interval of extreme returns can be predicted with high accuracy.
problem Predicting the occurrence of extreme financial returns.
method Recurrence interval analysis of extreme returns, using q-exponential distribution. result The recurrence interval of extreme returns follows a q-exponential distribution, leading to more accurate forecasts. UTOPIA aggregates multiple prediction intervals efficiently.
problem Constructing optimal prediction intervals for various real-world data problems.
method UTOPIA is a universally trainable strategy using linear or convex programming.
result UTOPIA constructs prediction intervals with small average width and high coverage probability.
DeepAries optimizes rebalancing intervals and asset allocations for better portfolio performance.
problem Fixed rebalancing intervals lead to unnecessary transactions and poor risk-adjusted returns.
method Adaptive deep reinforcement learning with Transformer state encoder and PPO.
result DeepAries outperforms traditional strategies in risk-adjusted returns, transaction costs, and drawdowns.
Optimizes liquidity provision intervals for profitable AMM participation.
problem Financial losses from poor liquidity provision intervals and reallocation costs.
method Developed a tractable stochastic optimization problem.
result Computes optimal liquidity provision intervals for profitable liquidity concentration.
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.
Paper presents methods to create stock price confidence intervals using LSTM models.
problem Creating accurate confidence intervals for LSTM-estimated stock prices.
method Three bootstrap methods for dependent data, optimal block length selection, and benchmark comparison.
result Illustrated through stock price data, different bootstrap strategies provide varying confidence intervals.
A framework for uncertainty-aware multimodal learning using conformal Shapley intervals.
problem Uncertainty and modality level importance in multimodal learning.
method Introduces conformal Shapley intervals to quantify modality level importance and uncertainty.
result Demonstrates meaningful uncertainty quantification and strong predictive performance.
CIR method constructs efficient prediction intervals with guaranteed coverage.
problem Efficiently constructing near-minimal prediction intervals with guaranteed coverage.
method Conditional Interquantile Regression (CIR) and CIR+ (enhanced version).
result Optimal balance between predictive accuracy and computational efficiency.
New method optimizes prediction set volume in conformal prediction.
problem Achieving volume optimality in conformal prediction without sacrificing coverage guarantees.
method Dynamic programming algorithm for finding near-optimal volume unions of k-intervals.
result Efficient algorithm finds unions of k-intervals with near-optimal volume for any distribution.
AutoCP automates the construction of accurate prediction intervals.
problem Creating valid and accurate prediction intervals for machine learning models.
method AutoML framework that optimizes prediction interval length for better accuracy and less conservatism.
result AutoCP significantly outperforms benchmark algorithms in constructing accurate prediction intervals.
The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.
problem Developing robust uncertainty quantification for Efron's Gaussian two-groups model with unknown contamination fraction.
method The approach involves Fourier-based certification procedures to find minimax-optimal adaptive confidence intervals.
result The minimax-optimal length of adaptive confidence intervals is polynomially worse than when contamination fraction is known.
Novel method for time-series prediction with tighter confidence intervals.
problem Improving prediction intervals for time-series data.
method Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI) using adaptive weights.
result KOWCPI achieves narrower confidence intervals with guaranteed coverage.
The paper improves prediction intervals for non-parametric regression using histograms.
problem Computing accurate prediction intervals for non-parametric regression models.
method Uses conditional histograms to estimate conditional distributions and compute shortest prediction intervals.
result The method provides prediction intervals with provable marginal coverage and asymptotic conditional coverage.
RQR improves prediction intervals for skewed data.
problem Invalid prediction intervals for skewed noise.
method Relaxed Quantile Regression (RQR) for asymmetric noise.
result Improved prediction intervals with desirable qualities.
This paper optimizes prediction intervals by tuning Random Forest using meta-validation.
problem Optimizing prediction intervals constructed by Random Forest.
method Combines exhaustive search with meta-validation techniques to tune Random Forest parameters.
result The 75/25 holdout meta-validation technique is always beneficial for tuning Random Forest.
Near-optimal confidence intervals for bounded data.
problem Online inference for sequential decision problems like A/B testing.
method Utilizing Bentkus' concentration results to improve on existing methods.
result Near-optimal confidence intervals confirmed favorable in synthetic and practical applications.
Study uncovers statistical optimality of nonconvex tensor completion methods.
problem Estimating a low-rank tensor from incomplete and corrupted observations.
method Two-stage estimation algorithm for nonconvex optimization.
result Nonconvex tensor completion achieves optimal ℓ2 accuracy. The paper proposes a tree model for interval-valued regression.
problem Learning a real-valued function from interval-valued data.
method Minimizing a margin-based discriminative objective function using a tree structure and dynamic programming.
result The proposed algorithm achieves state-of-the-art speed and accuracy.