Proposes DR-ACI for causal effect intervals with temporal dependence.
problem Causal effect intervals under temporal dependence.
method Doubly robust adaptive conformal inference (DR-ACI).
result Constructs prediction intervals for causal effects.
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48 results for “robustness interval”
This paper improves conformal prediction for robust interval estimation under distribution shifts.
problem Robustness of conformal prediction under distribution shifts.
method Modeling distribution shifts using Levy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations.
result Constructs robust conformal prediction intervals that remain valid under distribution shifts.
New framework tightens certified robustness gaps in machine learning models.
problem Persistent gap between theoretical certified robustness and empirical accuracy.
method Leverages Lipschitz continuity and novel confidence intervals.
result Improves robust accuracy, compressing the gap between theory and practice.
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.
Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gradient-based attacks). However, it is an open question whether the adversarially trained networks are truly robust under unknown attacks. In…
This work bridges theory and practice in spiking reservoirs, identifying robust parameter ranges.
problem Challenging tuning of spiking reservoirs at the edge-of-chaos.
method Introducing robustness interval, systematic evaluations, and control experiments.
result Consistent monotonic trends in robustness interval width across network configurations.
Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contaminationmath.OC
Study enhances robustness of In-CVaR based regression models under perturbation and contamination.
problem Enhancing robustness of nonlinear regression models under perturbation and contamination.
method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.
result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.
New method reduces confidence interval sizes for causal inference.
problem Inaccurate propensity scores and extreme scores cause large confidence intervals.
method Data-dependent Coarse IPW (CIPW) estimators.
result Robust CIPW estimators reduce confidence interval sizes to ε+1/√n.
Paper proves neural networks can be approximated using interval bounds.
problem Verifying safety and robustness of neural networks.
method Introduces interval universal approximation (IUA) theorem for neural networks.
result Neural networks can be approximated using interval bounds for any continuous function and squashable activation functions.
Walley's Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or i…
Bayesian method for estimating ATE with robustness to model misspecification.
problem Estimating average treatment effects under unconfoundedness.
method Double robust Bayesian inference using adjusted prior and posterior distributions.
result Bayesian credible sets form asymptotically exact confidence intervals.
Improves deep learning robustness by considering task and model.
problem Adversarial attacks on deep learning systems.
method Binary and interval label encoding strategy to redefine classification tasks and design corresponding loss functions.
result Our method enhances robustness without sacrificing accuracy.
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.
Boosting methods for interval-censored data improve predictive accuracy in survival analysis.
problem Handling interval-censored data in survival analysis and time-to-event studies.
method Nonparametric boosting methods using censoring unbiased transformations and functional gradient descent.
result Effective boosting methods for regression and classification with interval-censored data, offering robust performance.
TDistNNs improve prediction intervals for neural networks by using t-distributions.
problem Traditional neural networks provide only point estimates, lacking predictive uncertainty.
method TDistNNs generate t-distributed outputs with adjustable degrees of freedom, enhancing robustness to non-Gaussian data.
result TDistNNs produce narrower prediction intervals with proper coverage compared to Gaussian-based PNNs.
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.
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingstat.ML
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
problem Generating reliable multi-step ahead prediction intervals for time series data.
method Adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) based on conformal prediction principles.
result AEnbMIMOCQR provides close to exact coverage and robustness to distribution shifts.
Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks. However, it is currently very difficult to train a neural network that is both accurate and certifiably robust. In this work we take a step towards addressing this challenge. We prove that for every continu…
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.
Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong, but merely an axiom t…
IBP-R improves verified adversarial robustness with simple, effective interval bound propagation.
problem Improving verifiability of adversarially trained networks.
method Coupling adversarial attacks with interval bound propagation for minimized verification gap.
result State-of-the-art verified robustness-accuracy trade-offs for small perturbations on CIFAR-10.
PIVEN predicts both specific values and prediction intervals.
problem Improving robustness of neural nets in regression tasks.
method PIVEN is a deep neural network that produces both a prediction interval and a specific value prediction.
result PIVEN produces tighter uncertainty bounds than state-of-the-art approaches for prediction intervals.
Random Forests provide interpretable prediction intervals with theoretical guarantees.
problem Lack of uncertainty estimates in machine learning point predictions.
method Out-of-Bag procedure for generating parametric and non-parametric prediction intervals.
result Proposed prediction intervals deliver correct coverage rates and narrow lengths.
This paper analyzes statistical properties of the Robust Satisficing model.
problem Lack of statistical theory for the Robust Satisficing model.
method Comprehensive analysis of statistical properties, including confidence intervals and generalization error bounds.
result Established two-sided confidence intervals and finite-sample generalization error bounds for the RS optimizer.
Paper presents robust confidence sequences for means with known moment bounds and arbitrary corruption.
problem Tackles robustness to outliers and adversarial corruptions in mean estimation.
method Designs new robust exponential supermartingales to create confidence sequences.
result Achieves optimal width and shows smaller margin of error compared to fixed-time robust methods.
We study statistical inference and distributionally robust solution methods for stochastic optimization problems, focusing on confidence intervals for optimal values and solutions that achieve exact coverage asymptotically. We develop a generalized empirical likelihood framework---based on distributional uncertainty se…
Paper proposes a method to create more reliable confidence intervals for off-policy evaluations.
problem Creating reliable confidence intervals for off-policy evaluations.
method Proposes a deeply-debiasing procedure to construct efficient, robust, and flexible confidence intervals.
result Validated by theoretical results and numerical experiments, the method improves the reliability of off-policy evaluations.
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.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
problem Outlier detection in interval-valued data.
method Proposed a novel approach based on Shapley value for interval-valued data.
result Fine-grained interpretation of outliers with variable contributions.
We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks. This framework is built upon the work of Gowal et al., who applies the interval arithmetic to bound the activations at each layer and keeps the prediction invariant to t…
We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures. Unlike methods that target heterogeneous or conditional average treatment effects of an exposure, the proposed approach aims to take into account the irreducible dispersions …
This study uses ICL to efficiently generate robust confidence intervals for noisy regression tasks.
problem Uncertainty quantification for in-context learning in noisy regression tasks.
method Proposes a method based on conformal prediction to construct prediction intervals with guaranteed coverage.
result Conformal prediction with in-context learning (CP with ICL) achieves robust and scalable uncertainty estimates.
Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performancestat.ML
Proposes a method to create shorter, more accurate prediction intervals.
problem Challenges in achieving both conditional validity and interval efficiency in complex settings.
method Uses a conformal-style calibration method for neural network responses, adjusting to empirical PIT distribution.
result Demonstrates better conditional calibration and shorter intervals than existing methods.
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.
Perry uses auxiliary data to estimate RL policy values with confidence intervals.
problem Leveraging auxiliary datasets for off-policy evaluation with uncertainty quantification.
method Two methods for constructing valid confidence intervals for OPE with data augmentation.
result Methods consistently produce confidence intervals covering ground truth policy values.
New auction design uses statistical learning to reduce costs and improve fairness.
problem Designing efficient multi-item auctions with reduced implementation costs and fairness.
method Nonparametric density estimation for credible intervals, two new strategies.
result Strategies consistently outperform alternative methods in revenue maximization and cost reduction.
Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and other problem-specific tasks in applied Machine …
Corrects mismatch in consistency of nuisance estimators for doubly robust methods.
problem Mismatch in consistency of nuisance estimators in doubly robust methods.
method Calibrated debiased machine learning (calibrated DML) with isotonic regression adjustment.
result Calibrated DML yields doubly robust asymptotic normality with slower convergence of nuisance estimators.
Study improves traffic prediction intervals for minor roads.
problem Uncertainty in traffic data for underrepresented minor roads.
method Quantile Random Forest with PCA for interval prediction.
result Achieved 88.22% interval coverage and Winkler Score of 7,468.47.
Non-parametric bootstrap improves robust portfolio and trading strategy optimization.
problem Mitigating uncertainty in expected returns and covariances in financial decision-making.
method Non-parametric bootstrap framework for robust optimization without distributional assumptions.
result Improved out-of-sample performance with smoother, more stable results.
A theorem for debiasing machine learning with finite sample guarantees.
problem Calculating confidence intervals for machine learning functionals.
method Debiased machine learning based on bias correction and sample splitting.
result Nonasymptotic debiased machine learning theorem with finite sample guarantees.
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.
Conformal prediction improves prediction intervals for PCEs, especially in sparse cases.
problem Quantifying local model errors in PCEs for small datasets.
method Integration of conformal prediction methods (full and Jackknife+) into full and sparse PCEs.
result Better-calibrated prediction intervals for both full and sparse PCEs.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
problem Statistical inference challenges with missing at random labels and spatial dependence.
method Doubly robust estimator with cross-fit nuisances and jackknife spatial HAC variance correction.
result Asymptotically valid confidence intervals with improved finite-sample calibration.
Proposes two-stage robust and sparse distributed inference for large-scale data.
problem Statistical inference in large-scale, high-dimensional, and outlier-contaminated data.
method Two-stage approach: model selection with robust Lasso, fusion of local selections, and bootstrap methods for inference.
result Robust and computationally efficient inference procedures for variable selection, confidence intervals, and standard deviation approximations.
New method for robust learning from batches, even adversarial ones.
problem Learning from batches that may be corrupt or adversarial.
method General framework for robust learning, derived from optimal robust algorithms.
result First robust agnostic learning algorithms for various distributions.
Training Deep Neural Networks that are robust to norm bounded adversarial attacks remains an elusive problem. While exact and inexact verification-based methods are generally too expensive to train large networks, it was demonstrated that bounded input intervals can be inexpensively propagated from a layer to another t…
Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism in the face of sensible value functions (OFVF)- a novel data-driven Bayesian algorithm to constructing Plausibility sets for MDPs to explore…