Other Computer Science

19 papers

Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.

Latest in Other Computer Science

Showing 1–19 of 19

Apr 22, 2026Formal Sciences260423.0006

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories

Ali Rayat, Yaohang Li, Gia-Wei Chern

This paper introduces gauge-equivariant graph neural networks that embed local gauge symmetries directly into message passing for learning on lattice gauge theories. The approach enables principled machine learning for systems with site-dependent symmetries and nonlocal observables.

Apr 22, 2026Formal Sciences260423.0010

Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data

Sreechakra Vasudeva Raju Rachavelpula, Sangwhan Cha

This paper presents a personalized BEV energy consumption framework that combines driver behavior prediction using Bidirectional LSTM with physics-based energy modeling and map contextual features. The system accurately estimates State-of-Charge depletion by capturing individual driving patterns across diverse road conditions.

Apr 22, 2026Formal Sciences260423.0027

A weighted angle distance on strings

Grant Molnar

The paper introduces a multi-scale metric for strings that combines angle distances between n-gram count vectors at different scales using exponential weights. It provides algorithms, theoretical guarantees, and empirical validation for clustering applications.

Apr 22, 2026Formal Sciences260423.0036

On Bayesian Softmax-Gated Mixture-of-Experts Models

Nicola Bariletto, Huy Nguyen, Nhat Ho, Alessandro Rinaldo

This paper provides the first systematic theoretical analysis of Bayesian mixture-of-experts models with softmax gating, establishing posterior contraction rates for density estimation, convergence guarantees for parameter estimation, and strategies for selecting the number of experts.

Apr 22, 2026Formal Sciences260423.0037

Efficient Symbolic Computations for Identifying Causal Effects

Benjamin Hollering, Pratik Misra, Nils Sturma

This paper presents an efficient symbolic computation algorithm for determining whether causal effects can be identified from observational data with latent confounders in linear structural causal models. The algorithm finds the lowest-degree identifying formulas in quasi-polynomial time, making causal identifiability analysis practical for larger problems.

Apr 22, 2026Formal Sciences260423.0042

Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms

Yaiza Bermudez, Samir Perlaza, Iñaki Esnaola

This paper shows that decentralized machine learning can achieve the same performance as centralized learning by having clients share Gibbs measures (probability distributions over models) instead of raw data. The key innovation is using each client's Gibbs measure as a reference measure for the next client, effectively encoding prior information while preserving privacy.

Apr 20, 2026Formal Sciences260421.0043

Revisiting Active Sequential Prediction-Powered Mean Estimation

Maria-Eleni Sfyraki, Jun-Kun Wang

This paper analyzes active sequential prediction-powered mean estimation, where labels are selectively queried and ML predictions fill in the gaps. The authors find that contrary to intuition, using a nearly constant query probability (ignoring uncertainty) often produces tighter confidence intervals than adaptive uncertainty-based querying.

Apr 20, 2026Formal Sciences260421.0049

FUSE: Ensembling Verifiers with Zero Labeled Data

Joonhyuk Lee, Virginia Ma, Sarah Zhao, Yash Nair +3 more

FUSE is a method that combines multiple imperfect AI verifiers to better judge model outputs without needing any labeled training data. It matches or beats semi-supervised methods across diverse benchmarks by controlling how verifiers depend on each other using spectral algorithms.

Apr 20, 2026Formal Sciences260421.0051

Duality for the Adversarial Total Variation

Leon Bungert, Lucas Schmitt

This paper establishes a mathematical duality framework for adversarial total variation, showing that adversarial training of binary classifiers can be understood through nonlocal calculus of variations. The work provides rigorous characterizations of subdifferentials using dual representations and integration by parts formulas in both metric and Euclidean spaces.

Apr 20, 2026Formal Sciences260421.0053

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

Jun Chen, Umberto Biccari, Junmin Wang

This paper uses Deep Operator Networks (DeepONets) to learn a surrogate for the Riccati differential equation solution operator in finite-horizon LQR problems, enabling fast approximate optimal control without repeated numerical integration. The approach includes theoretical guarantees on stability and performance, and demonstrates significant computational speedups while maintaining high accuracy.

Apr 20, 2026Formal Sciences260421.0057

Safe Control using Learned Safety Filters and Adaptive Conformal Inference

Sacha Huriot, Ihab Tabbara, Hussein Sibai

This paper introduces Adaptive Conformal Filtering (ACoFi), which combines learned safety filters with adaptive conformal inference to provide soft safety guarantees for control systems. The method dynamically adjusts switching criteria between nominal and safe policies based on prediction uncertainty, achieving better safety performance than fixed-threshold approaches.

Apr 20, 2026Formal Sciences260421.0058

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle

Abdeladhim Tahimi

This paper provides a complete, step-by-step manual derivation of how Physics-Informed Neural Networks (PINNs) are trained, including forward propagation, loss computation, and backpropagation with explicit numerical examples. It bridges the gap between automatic differentiation libraries and the underlying mathematical operations, making PINN training transparent and verifiable.

Apr 20, 2026Formal Sciences260421.0066

Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario

Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud

This paper develops a random matrix theory model that explains why neural networks exhibit a transient learning window where signal is detectable before overfitting occurs. The key mechanism is that anisotropy in input data creates fast and slow learning directions, causing a learnable eigenvalue to temporarily separate from noise before being reabsorbed.

Apr 20, 2026Formal Sciences260421.0070

Spectral bandits for smooth graph functions

Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák

This paper introduces a bandit algorithm for learning smooth functions over graphs, where neighboring nodes have similar payoffs. The approach achieves regret bounds that scale with an effective dimension rather than the total number of nodes, enabling efficient online learning for applications like content recommendation with thousands of items from just tens of evaluations.

Apr 20, 2026Formal Sciences260421.0079

Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

Jonas Arruda, Sophie Chervet, Paula Staudt, Andreas Wieser +5 more

This paper develops a simulation-based Bayesian inference method that corrects for selection bias by embedding the selection mechanism directly into the generative model, enabling accurate parameter estimation in complex models where traditional likelihood-based approaches fail. The approach allows both debiased estimation and explicit testing for bias presence without requiring tractable likelihoods.

Apr 20, 2026Formal Sciences260421.0080

Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

Hafiza Syeda Yusra Tirmizi, Syed Ibad Hasnain, Muhammad Faris, Rabail Khowaja +1 more

This study develops a machine learning model using molecular descriptors to screen over 7,000 natural compounds for potential anti-Alzheimer activity, identifying 73 promising candidates. The cheminformatics approach demonstrates moderate predictive performance and highlights key molecular features associated with therapeutic potential.

Apr 20, 2026Formal Sciences260421.0082

Symmetry Guarantees Statistic Recovery in Variational Inference

Daniel Marks, Dario Paccagnan, Mark van der Wilk

This paper develops a general mathematical theory explaining how symmetries in target distributions and variational families guarantee that variational inference can accurately recover certain statistics, even when the approximation is imperfect. The framework unifies existing results and enables derivation of new recovery guarantees across diverse settings including directional statistics.

Apr 20, 2026Formal Sciences260421.0085

Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface

Matic Petrič, René Zander

This paper introduces the BlockEncoding interface in the Eclipse Qrisp framework, which makes block-encoding techniques accessible as high-level programming abstractions for implementing advanced quantum algorithms. The interface simplifies the practical implementation and resource estimation of algorithms like QSVT, QSP, and Hamiltonian simulation.

Apr 20, 2026Formal Sciences260421.0086

Incremental learning for audio classification with Hebbian Deep Neural Networks

Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros

This paper applies Hebbian learning principles to audio classification in a continual learning setting, introducing a kernel plasticity approach that selectively updates network weights to balance learning new sounds while retaining knowledge of previously learned ones. On the ESC-50 dataset, the method achieves 76.3% accuracy across five incremental learning steps, significantly outperforming a baseline approach.