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.
Paper optimizes change-point detection using learned distributions from training sequences.
problem Optimal change-point detection with unknown pre- and post-change distributions.
method Designs a change-point estimator using training sequences and test sequences.
result Optimal confidence width characterized as a function of undetected error.
A new technique called the Simulator analyzes adaptive sampling, improving on existing methods.
problem Analyzing the difficulty of distinguishing good from bad sampling strategies with limited data.
method A novel approach considering the difficulty of distinguishing strategies, matching strengths of Fano and change-of-measure techniques.
result First instance-based lower bounds for top-k problem, revealing new phenomena.
Q-learning with cSMART data assesses cAI tailoring variables.
problem Evaluating moderators in cAI construction.
method Clustered Q-learning with M-out-of-N Cluster Bootstrap.
result Constructs confidence intervals for causal effect moderation.
Improves accuracy and fairness in prediction systems with multiple domain experts.
problem Designing unbiased and accurate deferral systems with multiple experts.
method Proposes a framework for learning a classifier and deferral system that chooses to defer to multiple human experts.
result Significantly improves accuracy and fairness of final predictions compared to baselines.
Paper optimizes hypothesis verification in sequential experiments.
problem Maximizing confidence in a verified hypothesis after exploration.
method Formulated as a confidence maximization problem in a POMDP, characterized optimal solutions, and proposed a heuristic.
result Heuristic performs better than existing methods in some scenarios.
This study examines representation bias in open-source Qwen models for investment decisions.
problem Representation bias in financial applications of large language models.
method Balanced round-robin prompting over 150 U.S. equities, constrained decoding, token-logit aggregation.
result Firm size and valuation increase model confidence, while risk factors decrease it.
Confident learning estimates label quality by identifying and correcting noisy data.
problem Label quality in datasets, especially in noisy environments.
method Combines principles of pruning, probabilistic thresholds, and ranking to estimate noise and uncorrupted labels.
result Generalized CL framework that provably finds label errors and improves model accuracy.
Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
New algorithms identify best policies in discounted linear MDPs efficiently.
problem Identifying the best policy in discounted linear MDPs with limited samples.
method Derive lower bounds and devise simple yet near-optimal algorithms.
result Upper bound on sample complexity matches existing bounds.
Extends moderate deviations for a randomised Heston model.
problem Analyzing deviations in the Heston model with randomisation.
method Used Gärtner-Ellis theorem and sharp large deviations tools.
result Extended moderate deviations results for the randomised Heston model.
Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.
problem Interpreting causal effect moderation in high-dimensional data with interpretability and avoiding false positives.
method Two-step method: 1) Selects a smaller model for linear causal effect moderation using Gaussian randomization, 2) Conditions on selection to construct a pivot for uniformly asymptotic semi-parametric inference.
result Consistently achieves valid coverage rates and shorter, bounded intervals in time-varying causal effect moderation.
A new method estimates population mean using labeled and unlabeled data.
problem Estimating population mean with limited labeled data.
method Semi-supervised inference framework, least squares method.
result Proposed estimators outperform ordinary sample mean.
Paper develops robust methods for large-scale testing without tuning parameters.
problem Heavy-tailed data in high-dimensional settings.
method Revisits Hodges-Lehmann estimator for robust inference without tuning parameters.
result Develops confidence intervals and controls false discovery proportion.
The paper identifies all ε-optimal arms in a bandit problem with Gaussian rewards.
problem Identifying all ε-optimal arms in a finite stochastic multi-armed bandit with Gaussian rewards.
method The paper provides two lower bounds and a Track-and-Stop strategy to solve the problem, with an efficient numerical method to solve the convex max-min program.
result The Track-and-Stop strategy has asymptotically optimal average sample complexity in the regime of low risk.
Study option pricing near expiry for moderately out-of-the-money calls.
problem Estimating call option prices in the moderate deviations regime.
method Small-time moderate deviation estimates for call prices and implied volatility.
result Simple expressions of model parameters for generic models.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
New lower bounds show challenges in clustering in moderate dimensions.
problem Clustering points from mixtures of isotropic Gaussians in moderate dimensions.
method Established low-degree polynomial lower bounds and developed a novel non-spectral algorithm.
result New lower bounds reveal a 'non-parametric rate' in moderate dimensions.
Optimizes variance reduction in Heston model using large and moderate deviations.
problem Improving variance reduction in stochastic volatility models.
method Large and moderate deviations theory applied to Heston model.
result Derives closed-form solutions for optimal change of measure.
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…
Unified treatment of option pricing deviations scaled for financial models.
problem Modeling and scaling for option pricing deviations.
method Pathwise moderate deviations for financial models and related functionals.
result Unified approach to small-time, large-time, and tail asymptotics for diffusions and option prices.
Computes invariants distinguishing between immersions and embeddings of doodles and blobs on surfaces.
problem Distinguishing between immersions and embeddings of doodles and blobs on surfaces.
method Regular embeddings, bordisms, and exact sequences of abelian groups.
result Exact sequence describing bordisms of immersions and embeddings of doodles on A=RimesI. New method interprets deep learning for causal effects, separating prognostic and moderating covariates.
problem Estimating individual causal/treatment effects under confounders.
method Deep counterfactual learning architecture for estimating CATE with interpretable score functions.
result Demonstrated improved interpretability and quantification of uncertainty in CATE estimation.
Unified approach to stochastic Volterra systems' deviations.
problem Large and moderate deviations for stochastic Volterra systems.
method Weak convergence approach by Budhijara, Dupuis and Ellis.
result Unified treatment of deviations for a broad class of stochastic Volterra equations.
Stochastic Gradient Descent shows directional bias with moderate learning rates, impacting optimization outcomes.
problem Understanding the bias of SGD with moderate learning rates in practical scenarios.
method Analyzing SGD and GD on an overparameterized linear regression problem.
result SGD converges along large eigenvalue directions, GD along small ones, affecting early stopping outcomes.
A new graph-based clustering method for moderate-dimensional data.
problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.
New algorithm uses machine learning to predict rewards for decision-making problems.
problem Sequential decision-making under uncertainty with scarce online data.
method Machine Learning-Assisted Upper Confidence Bound (MLA-UCB) algorithm.
result Proves to improve cumulative regret even with biased surrogate rewards.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.
problem Understanding the evolution of tokens in transformer models at moderate interaction levels.
method Modeling transformer models as a system of particles interacting in a mean-field way and studying the corresponding dynamics.
result Characterization and convergence of the limiting dynamics in different phases of the system.
This paper tackles efficient clustering of moderate dimensional data from dual observation and attribute spaces.
problem Clustering high-dimensional data, especially when dimensionality is moderate to small.
method Develops an efficient clustering processing pipeline using dual spaces of observations and attributes.
result Established an effective method for clustering in moderate dimensional data.
Kernel-embedding tests can be suboptimal, but a simple modification improves their performance.
problem Optimizing goodness-of-fit tests using kernel embeddings.
method Analyzing and modifying kernel-embedding based goodness-of-fit tests within a minimax framework.
result A moderated kernel-embedding approach provides optimal tests for various deviations and is adaptive over a wide range of spaces.
We refine option pricing near-the-money skew in rough fractional volatility models.
problem Approximating near-the-money skew in rough fractional volatility models.
method Proved higher order moderate deviation estimates for rough fractional volatility models.
result Allowed application of skew approximation formulae to wider moderate deviations regime.
A new method improves AI fairness assessment by estimating performance across intersectional subgroups.
problem Limited evaluation of AI systems across intersectional subgroups due to small sample sizes.
method Structured regression approach to disaggregated evaluation.
result Our method yields more accurate performance estimates, especially for small subgroups.
A framework for optimizing prompt selection in generative language models.
problem Efficiently selecting prompts for generative language models.
method Two-stage framework using simulation optimization to maximize a pre-defined score.
result Consistency of the sequential evaluation procedure in the proposed framework.
Proposes a method for differentially private linear regression and synthetic data generation.
problem Lack of valid inference and synthetic data generation methods for small-scale datasets in privacy-aware settings.
method Gaussian differentially private linear regression with bias-corrected estimator and SDG procedure.
result Improves accuracy and provides valid confidence intervals for downstream tasks.
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
A k-means clustering-based SVM method classifies aggressive and moderate drivers.
problem Classifying drivers based on their curve-negotiating behaviors.
method k-means clustering for feature extraction, SVM for classification.
result kMC-SVM method reduces recognition time and improves classification accuracy.
New tighter confidence bounds for sequential kernel regression.
problem Quantifying uncertainty in sequential learning algorithms.
method Martingale tail inequalities and conic programming.
result New confidence bounds are tighter than existing ones.
New flexible confidence sequences for robust statistical inference.
problem Creating robust statistical inference methods that work under mild assumptions.
method Proposed a new class of asymptotic time-uniform confidence sequences.
result Sharp asymptotic time-uniform confidence sequences achieved under mild assumptions.
Houdini finds high-dimensional saddle points under few constraints.
problem Escaping from saddle points in high-dimensional spaces with constraints.
method Gradient descent methods under logarithmic inequality constraints.
result Polynomial time algorithms for escaping saddle points under constraints.
The paper extends confidence sequences for infinite variance data.
problem Addressing confidence sequences for distributions with infinite variance.
method Establishing lower bounds and deriving tight confidence sequences for relaxed bounded pth-moment distributions. result Derived confidence sequences are tighter than those using Dubins-Savage inequality.
The paper defines multivariate confidence intervals that are easy to interpret and retain qualities of one-dimensional counterparts.
problem Applying confidence intervals to multivariate data.
method Defining multivariate confidence intervals that extend one-dimensional definitions and providing efficient approximate algorithms.
result Multivariate confidence intervals retain qualities of one-dimensional counterparts and are easy to interpret.
Improves binary classification from positive data with skewed confidence.
problem Skewed confidence in positive data affects the performance of Pconf classifiers.
method Parameterized model of skewed confidence and hyperparameter selection.
result Proposed method effectively cancels out the negative impact of skewed confidence.
Improved algorithms for stochastic linear bandits using tighter confidence sequences.
problem Stochastic linear bandits with improved worst-case regret guarantees.
method Novel tail bound for adaptive martingale mixtures to construct tighter confidence sequences.
result Linear bandit algorithm achieves competitive worst-case regret.
Study describes frequencies of geodesics on hyperbolic surfaces as genus grows.
problem Large genus asymptotic behaviors of geodesic frequencies on hyperbolic surfaces.
method Proof of conjecture involving separating and nonseparating geodesics.
result Explicit function $f( rac{n}{g})$ for frequency ratio given.
The Lax-Hopf formula simplifies the value function of an intertemporal optimization (infinite dimensional) problem associated with a convex transaction-cost function which depends only on the transactions (velocities) of a commodity evolution: it states that the value function is equal to the marginal fonction of a fin…
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.
A tool predicts BN performance for real-world datasets.
problem Lack of validation for BN results in real-world applications.
method Synthetic datasets and structure learning algorithms to estimate BN performance.
result Automatic recommendations for BN performance based on synthetic data.