Telecomunicaciones, información y comunicación

Simple grammar bisimilarity, with an application to session type equivalence

- We provide an algorithm for deciding simple grammar bisimilarity whose complexity is polynomial in the valuation of the grammar (maximum seminorm among production rules). Since the valuation is at...

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

- Rapid advances in the field of Large Language Models (LLMs) have made LLM-based code generation an important area for investigation. An LLM-based code generator takes a prompt as input and produces...

Active teacher selection for reward learning

- Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human...

Characterizing and Correcting Effective Target Shift in Online Learning

- Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, especially under distributional shift. To understand...

Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity

- Sampling from a high-dimensional probability distribution is a fundamental algorithmic task arising in wide-ranging applications across multiple disciplines, including scientific computing,...

Robust Capacity Expansion under Wildfire Ignition Risk and High Renewable Penetration

- In power systems, the risk of wildfire ignition has increased significantly in recent years. The impact and severity of these events on energy dispatch, as well as their societal ramifications, make...

Pre-training Enables Extraordinary All-optical Image Denoising

- Optical neural networks are emerging as powerful machine learning and information processing tools because of their potential advantages in speed and energy efficiency. The training methods of these...

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling

- We consider a first order stochastic optimization framework where, at each iteration, $K$ independent identically distributed (i.i.d.) data point samples are drawn, based on which stochastic...

A Combinatorial Framework for the Pons-Batle Identity: Young Tableaux, Lattice Paths, and Limit Laws

- Tree-child networks are an important class of phylogenetic network used to model reticulate evolutionary processes. These networks have attracted increasing attention from researchers with interests...

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning

- Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample complexity remains poorly understood. Existing...

Loop Composition in Quantum Algorithms

- The quantum circuit model essentially treats every quantum algorithm as a straight-line program. While this view is universal, recent work has shown that it is inconvenient for using different-length...

Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization

- To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends the conventional circuit of the quantum approximate...

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

- The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditional approaches require massive video data to be...

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

- Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition, but conventional analog aggregation schemes typically require instantaneous channel state...

Spectrum-Adaptive Generalization Bounds for Trained Deep Transformers

- Understanding why trained Transformers generalize well is a fundamental problem in modern machine learning theory, and complexity-based generalization bounds provide a principled way to study this...

Symplectic H2 Model Reduction for High-Dimensional Linear Quantum Systems

- The $\mathcal{H}_2$ model reduction problem for high-dimensional linear quantum systems is studied under the constraint of physical realizability (PR). This constraint requires preservation of the...

Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning

- Functional connectivity (FC) derived from resting-state fMRI is widely used to characterize large-scale brain network alterations in neurological and psychiatric disorders. However, FC construction...

Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions

- This paper presents a parametric solution to piecewise linear regression through the Adaptive Block Gradient Descent (ABGD) algorithm. The heart of the method is the parametrization of piecewise...

Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices

- Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple device geometries. We present PIC-Flow, a generative...

Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests

- The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed kernel inherently...

One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators

- Probabilistic conditioning is concerned with the identification of a distribution of a random variable $X$ given a random variable $Y$. It is a cornerstone of scientific and engineering applications...