arXiv research

The study examines methods to correct measurement error in nutritional epidemiology studies.

A method compares AI corrections to a base model for explaining predictions.

Conditional forecasts improve performative prediction accuracy.

In distributed ML applications, shared parameters are usually replicated among computing nodes to minimize network overhead. Therefore, proper consistency model must be carefully chosen to ensure algorithm's correctness and provide high throughput. Existing consistency models used in general-purpose databases and moder…

This paper has been withdrawn by the authors pending corrections.

We use tools from geometric statistics to analyze the usual estimation procedure of a template shape. This applies to shapes from landmarks, curves, surfaces, images etc. We demonstrate the asymptotic bias of the template shape estimation using the stratified geometry of the shape space. We give a Taylor expansion of t…

Study shows AD for neural nets with machine-representable numbers can be incorrect.

A new method improves super learner validation efficiency.

New method corrects bias in datasets using cumulative distribution functions.

A new correction term improves sample efficiency in deep reinforcement learning.

Practical or scientific considerations often lead to selecting a subset of parameters as ``important.'' Inferences about those parameters often are based on the same data used to select them in the first place. That can make the reported uncertainties deceptively optimistic: confidence intervals that ignore selection g…

AI-generated variables bias regression estimates; methods correct for invalid inference.

New CRB derived for curved models using extrinsic geometry.

Using 4-dimensional arithmetic hyperbolic manifolds, we construct some new homological quantum error correcting codes. They are LDPC codes with linear rate and distance $n^ε$. Their rate is evaluated via Euler characteristic arguments and their distance using $\mathbb{Z}_2$-systolic geometry. This construction answers …

We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. We prove that such approximate corrections are sufficien…

We use Khovanov homology to define families of LDPC quantum error-correcting codes: unknot codes with asymptotical parameters [[3^(2l+1)/sqrt(8πl);1;2^l]]; unlink codes with asymptotical parameters [[sqrt(2/2πl)6^l;2^l;2^l]] and (2,l)-torus link codes with asymptotical parameters [[n;1;d_n]] where d_n>\sqrt(n)/1.62.

Local surrogate model improves time series forecasts and provides interpretable explanations.

Improved estimation for imbalanced data using log odds correction and optimal sampling.

UCPO improves diversity in reinforcement learning models, maintaining high accuracy.

Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCB…

Estimates proxy-based inference adjustments for distribution shifts.

Neural operators correct PDE residuals to improve BIP solutions.

SLOE speeds up logistic regression in high dimensions with accurate signal strength estimation.

Paper corrects bias in online learning algorithms with endogenous data.

In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statistically similar link probabilities within and between groups. A recent extension by Karrer and Newman incorporates a node degree correction to mod…

Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a $δ$-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified $δ$) that has minimum sample complexity. Lower bounds for …

Most distortion correction methods focus on simple forms of distortion, such as radial or linear distortions. These works undistort images either based on measurements in the presence of a calibration grid, or use multiple views to find point correspondences and predict distortion parameters. When possible distortions …

Training-free method improves text-to-image generation quality.

Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classification. But, efficient policy evaluation need not yield efficient estimation of policy parameters. We consider the estimation problem give…

New matching estimators correct bias in multivariate settings without smoothing parameters.

Efficient surrogate modeling for complex PDEs with physical laws.

We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random …

A new method for math reasoning that allows for iterative correction.

String geometry theory uniquely determines classical action with T-symmetry.

We consider community detection in Degree-Corrected Stochastic Block Models (DC-SBM). We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block-membership of all but a vanishing fraction of nodes, in the regime where the lowes…

Improved MALA method for neural networks uncertainty quantification.

New method corrects Laplace/BIC errors in singular models, revealing effective dimension.

WEINCE improves contrastive learning by correcting softmax biases.

Proposes new methods for inference in GLMs without assuming model correctness.

Random feature model shows slow self-correction of generalization gap.

New method corrects bias in CVaR estimation for extreme risks.

Two approaches improve conformal Bayes for label shift, one post-hoc and one in-training.

Study proposes a method to construct copulas using corrected Hermite polynomial expansion for estimating foreign exchange volatility.

LatentNN corrects neural network attenuation bias in astronomical data.

Develops a GMM method to estimate roughness in stochastic volatility models.

Study efficient pricing for barrier options in stochastic-volatility models with leverage correction.

Bayesian method corrects bias in treatment effect estimation.

The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks…