Telecomunicaciones, información y comunicación

Successor-Generator Planning with LLM-generated Heuristics

- Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning. This work revisits that...

Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph

- As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation. Network traffic,...

From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision

- Vision Language Models (VLMs) have achieved strong performance across a wide range of video understanding tasks. However, their viewpoint-invariant training limits their ability to infer egocentric...

Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators

- Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential...

Minimum Monotone Spanning Trees

- Given a finite set $S$ of points in the plane and a finite set $\mathcal{D}$ of directions, a geometric spanning tree~$T$ of~$S$ is $\mathcal{D}$-monotone if every path in $T$ is monotone with...

ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving

- Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene...

Policies for Fair Exchanges of Resources

- People increasingly use digital platforms to exchange resources in accordance with some policies stating what resources users offer and what they require in return. In this paper, we propose a formal...

Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

- Subspace identification methods (SIMs) have proven to be very useful and numerically robust for building state-space models. While most SIMs are consistent, few if any can achieve the efficiency of...

How Learning Dynamics Drive Adversarially Robust Generalization?

- Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing empirical and theoretical explorations fail to provide...

Faster and Simpler Greedy Algorithm for $k$-Median and $k$-Means

- Clustering problems such as $k$-means and $k$-median are staples of unsupervised learning, and many algorithmic techniques have been developed to tackle their numerous aspects.In this paper, we...

Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees

- Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current policy during reward...

MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization

- Discrete optimization is ubiquitous in science and engineering. The vast array of existing discrete optimization problems, coupled with the continuous emergence of new ones, necessitates...

Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty

- Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardware. Many robust RL...

A Distributionally Robust Optimisation Approach to Fair Credit Scoring

- Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval...

MINDFul.jl: A Framework for Intent-driven Multi-Domain Network coordination

- Network coordination across multiple domains is a complex task that requires seamless communication among network entities. Network operators aim to minimize costs while ensuring the requirements of...

Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

- Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning...

InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference

- Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition. However,...

Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness

- We present a new algorithm to train a robust malware detector. Modern malware detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware...

Adversarial Rademacher Complexity of Deep Neural Networks

- Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data. While DNNs can...

Frameworks to Design Approximation Algorithms for Finding Diverse Solutions in Combinatorial Problems

- Finding a \emph{single} best solution is the most common objective in combinatorial optimization problems. However, such a single solution may not be applicable to real-world problems as objective...

Scaling WaterLily.jl with MPI and an improved geometric multigrid solver

- We present recent performance-oriented developments in this http URL, a scale-resolving incompressible flow solver written in pure Julia that runs...