Improves conditional coverage of regression models using conformal prediction.
problem Lack of conditional coverage guarantees in conformal prediction methods.
method Proposes a novel algorithm to train a regression function to improve conditional coverage after split conformal prediction.
result Establishes an upper bound for miscoverage gap and proposes an end-to-end algorithm to control it.
ECI improves time series prediction uncertainty quantification by smoothing miscoverage error.
problem Challenges in uncertainty quantification for time series prediction due to temporal dependence and distribution shift.
method Error-quantified Conformal Inference (ECI) by smoothing quantile loss function and introducing adaptive feedback scale.
result ECI achieves valid miscoverage control and tighter prediction sets than existing methods.
Develops methods to adjust prediction set coverage based on post-selection analysis.
problem Adjusting prediction set coverage after initial analysis to better fit specific needs.
method Post-selection conformal inference to adjust miscoverage levels.
result Allows for trade-off between coverage and prediction set quality.
SConU improves uncertainty prediction for large models by testing for outliers and reducing miscoverage.
problem Real-world deployment of large language models requires reliable guarantees of task-specific metrics.
method SConU implements significance tests to identify and exclude outliers that violate exchangeability assumptions.
result SConU reduces miscoverage rates and enhances prediction efficiency in high-stakes tasks.
Exact distribution of split conformal prediction coverage found.
problem Determining the reliability of prediction sets in batch mode.
method Analysis of exchangeable data to find universal distribution of empirical coverage.
result Exact distribution of empirical coverage is universal and determined by nominal miscoverage level and calibration sample size.
Improved conformal prediction for better conditional coverage of classifier predictions.
problem Achieving exact conditional coverage in finite samples for prediction sets.
method Developed a variant of conformal prediction targeting coverage conditional on confidence and trust score.
result Empirically improved conditional coverage properties compared to standard conformal prediction.
DRO-NPE improves neural posterior estimation by reducing overconfidence and overfitting.
problem Overconfident and unreliable posteriors in simulation-based inference with limited simulation budgets.
method Distributionally robust approach using Wasserstein ambiguity set and KL-based metrics.
result Consistently improves coverage and calibration across benchmark tasks.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
TA-CQR predicts regression intervals with exact coverage, splitting miscoverage between endpoints.
problem Predicting regression intervals with exact coverage under reporting constraints.
method TA-CQR uses tail allocation to parameterize the oracle, estimating the allocation by searching quantile cores and applying nonnegative additive split-conformal calibration.
result TA-CQR achieves exact finite-sample marginal coverage under exchangeability, with theoretical guarantees on calibration and length.
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.
New bounds on efficiency for conformalized regression methods.
problem Efficiency of conformal prediction in regression models.
method Non-asymptotic bounds on prediction set length for conformalized quantile and median regression.
result Identifies phase transitions in convergence rates across different regimes of miscoverage level.
New framework calibrates decision robustness using inverse conformal risk control.
problem Inadequate robustness levels in decision-making due to ad hoc choices.
method Constructs valid estimators to trace miscoverage-regret Pareto frontier.
result Provides distribution-free, finite-sample guarantees on robustness levels.
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.
Proposes a new method to unlearn from specific data points in conformal predictors.
problem Challenges of existing unlearning methods in conformal predictors.
method Formalizes conformal unlearning, introduces practical metrics, and presents an optimization algorithm.
result Demonstrates effective removal of targeted information while preserving utility.
Backward Conformal Prediction offers flexible control over prediction set sizes while ensuring coverage guarantees.
problem Providing reliable prediction sets with controlled sizes in applications like medical diagnosis.
method Defines a rule that constrains prediction set sizes based on observed data, adapting coverage levels.
result Maintains computable coverage guarantees while ensuring interpretable, well-controlled prediction set sizes.
New risk control method for non-monotonic losses in complex parameters.
problem Controlling risk for non-monotonic losses with multidimensional parameters.
method Stability-based guarantees for generic algorithms applied to non-monotonic losses.
result Guarantees depend on algorithm stability, with looser guarantees for unstable algorithms.
C-SymmPI provides near-conditional coverage for structured data with group symmetries.
problem Establishing near-conditional coverage guarantees for structured data with group symmetries.
method Developed a framework C-SymmPI that achieves near-conditional coverage under general data structures with group symmetries.
result Near-conditional coverage guarantees for structured data with group symmetries.
New methods improve cross-conformal prediction's prediction sets without sacrificing coverage guarantees.
problem Improving the width of prediction sets in cross-conformal prediction.
method Proposed new variants of existing methods based on recent results on more efficient combination of p-values.
result Smaller prediction sets achieved without compromising theoretical guarantees.
Adaptive conformal inference without data exchangeability assumptions.
problem Real-world scenarios often violate the data exchangeability assumption for conformal prediction.
method Parameter-free online convex optimization for adaptive conformal inference.
result Controls long-term miscoverage frequency at a nominal level empirically.
Paper proposes a hybrid RL algorithm that combines offline and online data without needing reward info.
problem How to efficiently use online data to improve RL policies using only offline data.
method A three-stage hybrid RL algorithm that uses reward-agnostic exploration and model-based offline RL.
result The hybrid RL algorithm outperforms both pure offline and pure online RL in sample complexity.
Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
problem Improving prediction intervals for non-exchangeable time-indexed datasets
method Spectral adaptive conformal prediction
result Improves on fixed spectral weighting while monitoring uncertainty changes
The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.
problem Constructing well-calibrated prediction sets for correlated target variables.
method The method uses vine copulas to estimate the joint cumulative distribution function of non-conformity scores and improves the asymptotic efficiency of the quantile estimate.
result The method guarantees asymptotically exact coverage and competitive efficiency on real-world regression problems.
Develops a new method for online conformal prediction without manual tuning.
problem Achieving long-run 1−α coverage for arbitrary data streams in an informative manner. method Linearized regret theory and universal portfolio algorithms.
result Strong finite-time bounds on miscoverage for UP-OCP, outperforming prior methods.
A new method controls risk for set predictors using cross-validation.
problem Inefficient set predictors when data limited.
method Cross-validation conformal risk control (CV-CRC).
result CV-CRC offers theoretical guarantees and reduces set size.
New method for valid prediction intervals in counterfactual outcomes with runtime confounding.
problem Valid prediction intervals for counterfactual outcomes under runtime confounding.
method Debiased machine learning framework grounded in semiparametric efficiency theory.
result Prediction intervals achieve desired coverage rates with faster convergence compared to standard methods.
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.
ACP-UCB1 ranks arms based on upper-tail performance, improving stochastic bandit algorithms.
problem Stochastic bandit algorithms often favor arms with strong upper-tail performance, which is not well-addressed by classical mean-reward criteria.
method ACP-UCB1 combines an adaptive conformal estimate of the upper endpoint with a UCB-type optimism bonus.
result ACP-UCB1 achieves logarithmic upper-quantile regret with per-arm contribution \(O(
icefrac{\log n}{Δ_j^{\mathrm{ACP}}})\).
New metrics improve understanding of predictive system reliability.
problem Evaluating conditional coverage of predictive systems.
method Casting conditional coverage estimation as a classification problem, using excess risk of the target coverage (ERT) metrics.
result Modern classifiers provide higher statistical power for estimating conditional coverage.
The paper explores gaps in curvature-related metrics and rigidity.
problem Understanding gaps in curvature-related metrics and rigidity.
method Analyzes three types of gaps: spectral, metric-rigidity, and topological-rigidity.
result Proposes open problems in the field.
Study gap-dependent regret bounds for risk-sensitive RL.
problem Risk-sensitive reinforcement learning with entropic risk measure.
method Propose cascaded gaps to adapt to problem structures, derive regret bounds.
result Exponential improvement over existing bounds in appropriate settings.
A new method for online prediction uncertainty quantification in non-exchangeable panel data.
problem Challenges in quantifying predictive uncertainty for non-exchangeable panel data.
method Online conformal prediction framework for non-exchangeable panel data, using similarity weights and adaptive miscoverage levels.
result Improves coverage on worst-covered target units through adaptive interval-width allocation.
We tackle permutation in linear regression with a new inference framework.
problem Statistical investigation of permutation in linear regression models.
method Localization step followed by conditional Monte Carlo test and coefficient inference.
result Valid statistical inference procedures for permutation and regression coefficients.
Proposes online conformal prediction method with adversarial semi-bandit feedback.
problem Online uncertainty quantification with adversarial semi-bandit feedback.
method Formulates online conformal prediction as an adversarial bandit problem and uses regret minimization.
result Achieves long-run coverage guarantee with adversarial semi-bandit feedback.
Paper analyzes origami slope gaps and their distribution, finding a unique pattern.
problem Analyzing slope gaps in origami surfaces.
method Derived slope gap distribution of a specific origami by considering return times under the horocycle flow.
result Found a unique distribution of origami slope gaps, not a sum of scaled Hall distributions.
CAP algorithm controls FCR in online selective prediction.
problem Online predictive tasks with temporal multiplicity and FCR control.
method CAP framework with adaptive pick rule and calibration set construction.
result CAP achieves exact selection-conditional coverage guarantee and FCR control.
Random hyperbolic surfaces have nearly optimal spectral gaps.
problem Proving the nearly optimal spectral gap conjecture for random Belyi surfaces.
method Using the Brooks-Makover model, the authors show a spectral gap greater than 1/4 - c/log(n).
result A random hyperbolic surface in the Brooks-Makover model has a spectral gap greater than 1/4 - c/log(n).
Paper improves volume gap between minimal submanifolds and unit spheres.
problem Volume gap between minimal submanifolds and unit spheres.
method Modified Cheng-Li-Yau coefficients and applied Cheng-Yang eigenvalue estimate for Laplacian.
result Enhanced volume gap between minimal submanifolds and unit spheres.
The article proves a conjecture about the fundamental gap for horoconvex domains in hyperbolic space.
problem Proving a conjecture about the fundamental gap for horoconvex domains in hyperbolic space.
method Establishing conformal log-concavity estimates for the first eigenfunction.
result Proves a conjecture about the fundamental gap for horoconvex domains in hyperbolic space.
Local gaps in Ricci shrinkers depend only on dimension.
problem Understanding local properties of Ricci shrinkers.
method Proved local versions of Ricci curvature and entropy gap theorems.
result Local gaps depend only on dimension, not global entropy.
Study shows gaps in Bitcoin order book are linked to returns but only in the short term.
problem Understanding the relationship between gaps and returns in Bitcoin order books.
method Examined the dynamics of gaps and returns in a Bitcoin order book without considering long-term causation.
result The causal relationship between gaps and returns is limited to instantaneous causation.
Researchers compute gap distributions for saddle connection directions on specific translation surfaces.
problem Computing gap distributions for saddle connection directions on translation surfaces.
method Translation to dynamical question of return times to a transversal under the horocycle flow.
result Gap distributions have support at 0 and quadratic tail decay.
Proves gap rigidity theorem for Hermitian symmetric spaces.
problem Gap rigidity problems in compact Hermitian symmetric spaces.
method Dual analogy to Mok's noncompact case theorem, theorem on higher dimensional submanifolds.
result Proves gap rigidity theorem for diagonal curves in tube type spaces.
The paper introduces gapped scale-sensitive dimensions to improve learning rate bounds.
problem Improving lower bounds on rates of convergence in statistical and online learning.
method Introducing and analyzing gapped scale-sensitive dimensions for function classes.
result Gapped dimensions lead to stronger lower bounds on offset Rademacher averages.
The article explores the fundamental gap in Bakry-Emery geometry.
problem The fundamental gap in Bakry-Emery geometry.
method Recalled Bakry-Emery geometry and connected eigenvalues with boundary conditions. Showed a connection between fundamental gap and Bakry-Emery geometry.
result Presented key ideas in Andrews's and Clutterbuck's proof of the fundamental gap conjecture.
The paper calculates gap distributions for translation surfaces, focusing on the double heptagon.
problem Calculating gap distributions for translation surfaces.
method Describes a procedure to find winning holonomy vectors and applies it to the double heptagon.
result Explicitly computed gap distribution for the regular double heptagon translation surface.
Improved gap-dependent bounds for reinforcement learning with linear approximations.
problem Achieving nearly minimax-optimal performance with linear function approximation.
method Developed and analyzed the LSVI-UCB++ algorithm and its concurrent variant.
result First gap-dependent regret bound for nearly minimax-optimal algorithm LSVI-UCB++.
We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrain…
Computing unlinking number is usually very difficult and complex problem, therefore we define BJ-unlinking number and recall Bernhard-Jablan conjecture stating that the classical unknotting/unlinking number is equal to the BJ-unlinking number. We compute BJ-unlinking number for various families of knots and links for w…