2 citations · 2 across the 17 of their papers we have counts for
21 papers
An AI Assistant for Automating Anti-Corruption Expertise of Draft Legislation Based on the TRACE Framework
Serhii Zabolotnii, Alina Tarasova
We present an AI assistant for automating anti-corruption expertise of draft legislation based on the TRACE architecture, designed to screen bills for corruption risks: pinpoint pr…
Moment-Orthogonal Quadratic-Envelope E-Processes for Anytime-Valid Location Inference
Serhii Zabolotnii
E-process confidence sequences for unbounded means typically use a variance bound and a first-order score, leaving skewness and kurtosis unused. We construct a quadratic-envelope e…
Rank-one variance inflation in polynomial estimation of signal parameters with a sign-preserving fractional-power basis
Serhii Zabolotnii
The polynomial maximization method estimates signal parameters in non-Gaussian noise from a finite description of the noise by its moments and cumulants, without a density. Known a…
Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents
Serhii Zabolotnii
Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's authority during a consequential task.…
Exact Computation of Non-Gaussian Mismatch Penalties in Wiener-Hermite Cross-Correlation Identification
Serhii Zabolotnii
Wiener-Hermite cross-correlation identification represents a polynomial response in the Hermite basis. Under Gaussian excitation the basis is orthogonal and a diagonal rule recover…
A Closed-Form Skew Penalty for Volterra Cross-Correlation Identification under Non-Gaussian Input
Serhii Zabolotnii
The monomial parameterization of finite-memory Volterra identification is ill-conditioned under non-Gaussian input, and the Wiener--Hermite expansion removes this ill-conditioning…
Closed-form fractional radial links for elliptical Mahalanobis discriminant analysis
Serhii Zabolotnii
We study binary classification when the two classes are elliptical with a shared radial generator . The log-likelihood ratio is then an additive function of the two squared Maha…
Parametrically Adaptive Transition Polynomial: a Signed-Parity Continuous-alpha Extension of Kunchenko Stochastic Polynomials
Serhii Zabolotnii
Kunchenko's method of polynomial maximization provides a semiparametric apparatus for parameter estimation under non-Gaussian errors, but its classical power basis relies on finite…
Moment-Free Kunchenko Stochastic Polynomials via Empirical Characteristic Function
Serhii Zabolotnii
We give a characteristic-function formulation of Kunchenko's stochastic-polynomial construction for settings in which raw moments may fail to exist. In the finite-variance trigonom…
Second-Order Least Squares as a Special Case of the Polynomial Maximization Method
Serhii Zabolotnii
For linear regression with i.i.d. homoskedastic errors, optimally weighted second-order least squares (SLS) is the degree-two polynomial maximization method (PMM) estimating equati…
A Transferability Criterion for Null-Optimized Variance Reduction in Cumulant-Based Error-Independence Testing
Serhii Zabolotnii
Control-variate and polynomial-maximization (PMM) estimators are optimized at a single fixed distribution, yet they are increasingly proposed to strengthen hypothesis tests, which…
Efficient frequentist fractional polynomials for skewed dose-response and survival data: a variance-reducing alternative to OLS-FP
Serhii Zabolotnii
Fractional polynomials (FP) are a standard tool for modelling nonlinear dose-response and covariate effects, implemented in the widely used mfp package. The conventional FP fit est…
Matched generating elements in maximum entropy density reconstruction
Serhii Zabolotnii
Moment-constrained maximum entropy (MaxEnt) reconstructs a density as an exponential family whose sufficient statistics are chosen constraint functions. We study this choice separa…
Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
Serhii Zabolotnii
Uniform sampling on implicitly defined manifolds is a core primitive in motion planning, constrained simulation, and probabilistic machine learning. MASEM addresses this problem by…
From Volterra Series to Kunchenko Stochastic Polynomials: Half a Century of Non-Gaussian Estimation Methodology
Serhii Zabolotnii
This paper reconstructs the half-century evolution of the scientific school founded by Yuriy P. Kunchenko (1939--2006) as the development of a semiparametric methodology for non-Ga…
TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains
Serhii Zabolotnii
We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer reference architecture with an ex…
EstemPMM: Polynomial Maximization Method for Non-Gaussian Regression and Time Series in R
Serhii Zabolotnii
We describe the R package EstemPMM, which implements the Polynomial Maximization Method (PMM) for parameter estimation under non-Gaussian errors. PMM exploits higher-order cumulant…
Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection
Serhii Zabolotnii
Generalized stochastic approximation replaces the log-likelihood ratio of a sequential change-point problem by an affine function of a fixed feature dictionary, whose coefficients…
From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy
Serhii Zabolotnii, Viktoriia Holinko, Olha Antonenko
Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineere…
LLM StructCore: Schema-Guided Reasoning Condensation and Deterministic Compilation
Serhii Zabolotnii
Automatically filling Case Report Forms (CRFs) from clinical notes is challenging due to noisy language, strict output contracts, and the high cost of false positives. We describe…
Applying the Polynomial Maximization Method to Estimate ARIMA Models with Asymmetric Non-Gaussian Innovations
Serhii Zabolotnii
Classical estimators for ARIMA parameters (MLE, CSS, OLS) assume Gaussian innovations, an assumption frequently violated in financial and economic data exhibiting asymmetric distri…