CONTINA provides adaptive confidence intervals for traffic demand prediction.
problem Uncertainty in future traffic demand predictions and the need for valid confidence intervals.
method Adaptive confidence interval method that adjusts based on deployment errors.
result Valid confidence intervals with shorter lengths and theoretical coverage guarantee.
New confidence intervals improve treatment effect estimation in randomized experiments.
problem Improving confidence intervals for treatment effects in randomized experiments.
method Systematic exploitation of negative dependence or variance adaptivity.
result Achieved nonasymptotic confidence intervals with the same effective sample size as asymptotic ones.
A new algorithm reduces inference error in adaptive contextual bandits.
problem Challenges in statistical inference for adaptive contextual bandits.
method Proposes a regularized EXP4 algorithm that satisfies the Lai-Wei stability condition.
result Valid Wald-type confidence intervals for linear functionals can be achieved without the price of adaptivity.
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.
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.
Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the sho…
Develops a simple method for creating private confidence intervals.
problem Creating private confidence intervals for parametric estimation.
method Parametric bootstrap approach to construct confidence intervals.
result The parametric bootstrap provides consistent and effective confidence intervals.
Motivated by the growing popularity of variants of the Wasserstein distance in statistics and machine learning, we study statistical inference for the Sliced Wasserstein distance--an easily computable variant of the Wasserstein distance. Specifically, we construct confidence intervals for the Sliced Wasserstein distanc…
We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates. Prior work in this area has either focused on providing tight confidence intervals for specific analyses, or providing general worst-case bounds for point estimate…
A new method for adaptive experiments improves inference.
problem Current inference methods for adaptive experiments are weak and asymptotic.
method Simulation-based inference using optimistic simulations.
result Our method achieves better coverage and narrower intervals.
Data-driven method for error estimation without needing class complexity.
problem Constructing confidence intervals for a class of estimates.
method Data-driven approach to derive high-probability upper bounds on maximum error.
result Method naturally adapts to unknown correlation structures and works for finite and infinite classes.
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.
Proposes online debiasing estimators for adaptive linear regression.
problem Adaptive data collection leads to non-normal asymptotic behavior in simple methods.
method Online debiasing estimators that correct distributional anomalies.
result Asymptotic normality and minimax lower bound for proposed estimators.
CADR estimator improves inference for contextual bandit data.
problem Valid inference on contextual bandit data.
method CADR estimator for policy value, addressing adaptive data collection challenges.
result CADR provides correct coverage of confidence intervals.
We consider the problem of undirected graphical model inference. In many applications, instead of perfectly recovering the unknown graph structure, a more realistic goal is to infer some graph invariants (e.g., the maximum degree, the number of connected subgraphs, the number of isolated nodes). In this paper, we propo…
PPI++ uses machine learning predictions to improve inference from small datasets.
problem Efficient inference from small labeled datasets with high-quality predictions.
method Adapts prediction-powered inference (PPI) to compute confidence sets for any parameter dimensionality.
result Improves classical intervals using only labeled data, always yielding better results.
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
problem Creating interpretable local smoothers from complex random forest outputs.
method Uses random forest outputs to define spatially adaptive bandwidth matrices for a linear smoother.
result Improves interpretability and applicability of random forest outputs for various analyses.
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. 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.
Proposes BSI for valid statistical inference on bandit algorithms.
problem Valid statistical inference on bandit algorithms' performance.
method Fits a simulator of the bandit environment from observed data and uses it to estimate mean reward under any policy.
result Proves asymptotically valid confidence intervals and maintains nominal coverage.
Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.
problem Bias in LLM evaluations due to imperfect sensitivity and specificity.
method Plug-in framework with confidence intervals accounting for test and calibration dataset uncertainties.
result LML-based evaluation yields more reliable estimates than human-only evaluation.
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.
We address challenges in estimating parameters from adaptively collected data.
problem Estimating parameters from data collected adaptively leads to non-normal asymptotic distributions.
method We develop semi-parametric estimators that account for adaptivity in data collection.
result Our estimators are asymptotically normal under certain conditions.
SPACR trains uncertainty-aware regressors directly within a single pass, improving efficiency and validity.
problem Training uncertainty-aware regressors while maintaining efficiency and validity.
method Joint optimization of efficiency and validity during training.
result SPACR consistently provides tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR.
CASCADE improves uncertainty communication in Parkinson's disease medication management.
problem Uncertainty in clinical decision-making for Parkinson's disease patients.
method CASCADE uses a novel conformal prediction framework to adaptively scale prediction intervals based on classification uncertainty.
result CASCADE produces more efficient and robust prediction intervals for Parkinson's disease patients.
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.
New method predicts sets under unknown covariate shift with high confidence.
problem Adapting to unknown covariate shift in prediction sets.
method PredSet-1Step, a flexible distribution-free method.
result Achieves asymptotic probably approximately correct coverage.
Improved statistical inference for adaptive Thompson Sampling.
problem Statistical inference challenges in Thompson Sampling.
method Inflating posterior variance in Thompson Sampling.
result Asymptotically normal estimates of arm means with logarithmic regret increase.
Confidence intervals are a popular way to visualize and analyze data distributions. Unlike p-values, they can convey information both about statistical significance as well as effect size. However, very little work exists on applying confidence intervals to multivariate data. In this paper we define confidence interval…
The paper proposes an efficient method for estimating ATEs using adaptive experiments.
problem Estimating average treatment effects (ATEs) with minimal sample size and high accuracy.
method The paper defines and uses the efficient treatment-assignment probability to sequentially assign treatments, estimating ATEs using an Adaptive Augmented Inverse Probability Weighting (A2IPW) estimator.
result The proposed experimental design and A2IPW estimator achieve the minimized semiparametric efficiency bound and provide anytime valid confidence intervals for early stopping.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
problem Identifying outliers in a set of rewards where the threshold is a function of all rewards.
method Adaptively updated confidence interval for the threshold based on previous rounds' estimates, balancing exploration of individual arms and the outlier threshold.
result Efficient algorithm with reduced sample complexity for outlier detection.
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods are known to achieve complementary bias-variance trade-off properties, w…
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.
Paper proposes a new UCB approach for estimating maximum mean.
problem Estimating the maximum mean in various applications.
method Upper Confidence Bound (UCB) approach with adaptive sampling.
result LSA estimator shows faster bias decay compared to GA.
Clarifies the confidence interval approach for bioequivalence testing.
problem Ensuring the reliability of bioequivalence testing methods.
method Clarifies the conditions under which a 100(1-2α)% confidence interval yields a size-α test.
result A 100(1-2α)% confidence interval approach for bioequivalence testing yields a size-α test only when the two one-sided tests are 'equal-tailed'.
Paper proposes new method for time series confidence intervals using LSTM.
problem Constructing accurate confidence intervals for multivariate time series.
method Uses Long Short Term Memory Network (LSTM) and novel block bootstrap techniques.
result Demonstrates improved accuracy in constructing confidence intervals.
This paper presents new deviation inequalities that are valid uniformly in time under adaptive sampling in a multi-armed bandit model. The deviations are measured using the Kullback-Leibler divergence in a given one-dimensional exponential family, and may take into account several arms at a time. They are obtained by c…
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…
Random forest training yields confidence intervals for generalization error.
problem Computing accurate confidence intervals for random forest generalization error.
method Directly computes confidence intervals from training data without data splitting.
result Confidence intervals have good coverage and appropriate width.
Active inference uses machine learning to prioritize data labeling for more efficient statistical inference.
problem Efficiently collecting data points for statistical inference with limited labels.
method A machine learning-assisted approach that identifies uncertain data points for labeling.
result Achieves the same level of accuracy with fewer samples, resulting in smaller confidence intervals and more powerful p-values.
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.
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.
The paper proposes a method to infer Q-values online with Q-Learning.
problem High variance and instability in reinforcement learning algorithms.
method Adapting FCLT for a modified Q-learning approach and constructing confidence intervals.
result The proposed method provides more stable and reliable inference of Q-values.
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 ℓ2 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.
Improved statistical inference for expensive data using machine learning predictions.
problem Statistical inference under adaptive two-phase multiwave sampling with expensive measurements.
method Multiwave Predict-Then-Debias estimator combining proxy information and expensive measurements.
result Valid estimators and confidence intervals for M-estimation under adaptive sampling.
The age of big data has produced data sets that are computationally expensive to analyze and store. Algorithmic leveraging proposes that we sample observations from the original data set to generate a representative data set and then perform analysis on the representative data set. In this paper, we present efficient a…
Unified framework for fair decision-making across diverse groups.
problem Statistical brittleness in fairness testing for small subgroups.
method Size-adaptive hypothesis testing framework.
result Validated approach for interpretable, statistically rigorous decisions.
We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors k may depend on and diverge with sample size n. In addition to the case of known error variance, we define and study versions of the estimators when…