Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.32889

Measurement-Gated Provenance Attenuation for Frozen EEG Representations

Anuar Aimoldin·Yankai Chen·Ayana Mussabayeva·Nurdaulet Akhanov·Xue Liu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Frozen EEG representations retain acquisition signatures as well as neural activity. Source predictability alone does not identify what should be removed: it can reflect measurement effects or genuine biological and population differences, which should not be erased. We propose Measurement-Gated Provenance Attenuation (MGPA), built on one principle: measurement evidence determines where correction may act, and preserved information determines what it should aim for. Paired measurement contrasts define a gate outside which nothing changes; inside it, the source score is moved to the value the preserved coordinates already predict: for a fixed affine score, this keeps the same information as any target set by those coordinates and needs the least expected squared movement. Closed-form and critic-guided iterative constructions apply it without source identity or encoder retraining. Three studies test the principle at increasing distance from its assumptions. Under controlled reference changes, where the source-task association is known, MGPA brings source to near chance with task performance unchanged, whereas erasing what predicts source (LEACE) lowers frozen-task AUROC from .753 to .656 while barely touching source; ablations attribute the attenuation to the gate's directions and 2.7x less movement to the conditional target. Across recordings from different devices and electrodes, iterative correction lowers source accessibility while preserving or improving task performance. Finally, one iterative map selected on one task and reused unchanged on existing heads for two others raises their worst-association AUROC (lowest over device-label shifts) by .057 and .019 over LEACE, at a cost to those heads while the training association holds. A reusable correction shows its value in how an existing predictor behaves once acquisition cues stop being reliable, not only in what a probe can read.

Explore related subjects

cs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anuar Aimoldin, Yankai Chen, Ayana Mussabayeva, Nurdaulet Akhanov, Xue Liu. 2026-09-26. Measurement-Gated Provenance Attenuation for Frozen EEG Representations. https://arxiv.org/abs/2609.32889

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design

Online Monte Carlo evaluation is a fundamental tool for assessing policy performance in reinforcement learning and sequential decision-making problems arising in operations research. However, achieving accurate estimates often requires extensive online interaction with the environment, which can be costly or impractical in many real-world settings. In this paper, we develop a framework that improves the sample efficiency of online Monte Carlo estimators while preserving unbiasedness. We first derive a closed-form optimal behavior policy that minimizes estimator variance under unbiasedness constraints. We then propose practical algorithms for learning the proposed behavior policy from previously collected offline data, enabling improved online evaluation without requiring estimation of the environment transition model. We provide theoretical analysis that quantifies the resulting variance reduction and analyzes the impact of approximation errors. Empirical studies across diverse environments demonstrate substantial improvements in online sample efficiency compared with standard on-policy Monte Carlo evaluation and existing baseline methods. Our results provide a unified framework for optimal behavior policy design in off-policy evaluation, with applications to reinforcement learning and operations research.

cs.LG↗
arXiv2026

MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner. Several distinct approaches such as the extraction of positive and negative sentiments as well as multiclass emotions, have been implemented in this field of study. Nevertheless, the extraction of multiple sentiments is an almost untouched area in this language. Which involves identifying several feelings based on a single piece of text. Therefore, this study demonstrates a thorough method for constructing an annotated corpus based on scrapped data from Facebook to bridge the gaps in this subject area to overcome the challenges. To make this annotation more fruitful, the context-based approach has been used. Bidirectional Encoder Representations from Transformers (BERT), a well-known methodology of transformers, have been shown the best results of all methods implemented. Finally, a web application has been developed to demonstrate the performance of the pre-trained top-performer model (BERT) for multi-label ER in Bangla.

cs.LG↗
arXiv2026

Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data

We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of a random Tikhonov regularization path and decompose the tracking error of the algorithm's output for the regularization path into random difference equations in RKHS. We show that the tracking error vanishes in mean square and almost surely if the regularization path is slowly time-varying. Then, leveraging the monotonicity of inverse operators and the spectral decomposition of compact operators, and introducing the RKHS persistence of excitation condition, we develop a dominated convergence method to prove the mean square and almost sure consistency between the regularization path and the unknown function to be learned. Especially, for independent and non-identically distributed data streams, the mean square and almost sure consistency between the algorithm's output and the unknown function is achieved if the input data's marginal probability measures are slowly time-varying and the average measure over each fixed-length time period is uniformly above a strictly positive finite Borel measure.

cs.LG↗