On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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29 results for “UAV-assisted MEC”

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingeess.SP

Paper optimizes UAV-assisted mobile edge computing for energy efficiency.

problem Minimizing energy consumption in UAV-assisted mobile edge computing.

method Proposes CAT and RAT algorithms combining convex optimization and deep reinforcement learning.

result RAT achieves similar performance and outperforms traditional algorithms.

UAV-assisted MEC Trajectory control Deep Reinforcement Learning Convex Optimization Energy Efficiency

2019-11-10abs ↗pdf ↗

MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programmingcs.LG

MEC-IP uses IP to efficiently find MECs in BNs from observational data.

problem Discovering Markov Equivalent Classes (MECs) in Bayesian Networks (BNs) efficiently.

method Clique-focusing strategy and EMSG for MEC discovery via Integer Programming.

result Significant reduction in computational time and improved accuracy.

Integer Programming Markov Equivalent Classes Bayesian Networks Causal Discovery Observational Data

2024-10-22abs ↗pdf ↗

FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcs.LG

Paper proposes FMore to incentivize edge nodes in federated learning with MEC.

problem Incentivizing edge nodes in federated learning with MEC resources.

method Multi-dimensional procurement auction with K winners.

result FMore improves model accuracy and reduces training rounds for AI tasks.

federated learning MEC incentive mechanism auction Nash equilibrium

2020-02-22abs ↗pdf ↗

MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimationstat.ML

MEC improves efficiency and robustness in semi-supervised inference.

problem Efficient inference with limited labeled data and robust uncertainty quantification.

method Machine-Learning-Assisted Generalized Entropy Calibration (MEC) using cross-fitted, calibration-weighted PPI.

result MEC achieves semiparametric efficiency bounds under weaker assumptions and provides near-nominal coverage.

Machine Learning Semi-Supervised Learning Entropy Calibration Prediction Error Cross-Fitting

2026-04-07abs ↗pdf ↗

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experimentsstat.ML

A fast algorithm for counting Markov equivalent DAGs and designing experiments.

problem Counting Markov equivalent DAGs and designing experiments efficiently.

method LazyIter algorithm for efficient iteration over MECs, utilizing intervention results.

result Significant reduction in time complexity for sparse graphs (O(n)).

Markov Equivalence Class DAG Experiments Intervention Efficiency

2020-06-17abs ↗pdf ↗

MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimationstat.ML

MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio

problem Estimating ATT marginal hazard-ratio in externally controlled survival trials

method Machine-learning-assisted generalized entropy calibration for IPW Cox regression

result Reduces bias, increases efficiency, and improves coverage

ATT marginal hazard-ratio survival trials machine learning generalized entropy calibration

2026-06-06abs ↗pdf ↗

A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problemsstat.ME

The application of standard sufficient dimension reduction methods for reducing the dimension space of predictors without losing regression information requires inverting the covariance matrix of the predictors. This has posed a number of challenges especially when analyzing high-dimensional data sets in which the numb…

2019-09-28abs ↗pdf ↗

Size of Interventional Markov Equivalence Classes in Random DAG Modelsstat.ML

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and o…

2019-03-05abs ↗pdf ↗

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approachcs.LG

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. Nevertheless, by considering a MEC system consisting of mu…

2018-12-16abs ↗pdf ↗

Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learningcs.LG

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computat…

2018-05-16abs ↗pdf ↗

Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approachphysics.soc-ph

This paper tackles risk-aware energy scheduling for MEC networks with microgrids.

problem Risk in energy demand and supply for MEC networks powered by microgrids.

method Formulated an optimization problem with CVaR for energy consumption and generation, analyzed using a multi-agent stochastic game, derived solution with MADRL-based A3C algorithm.

result Significant performance gain by considering CVaR for high accuracy energy scheduling.

Edge Computing Microgrids Deep Reinforcement Learning Risk Management Energy Scheduling

2020-02-21abs ↗pdf ↗

A Transformational Characterization of Unconditionally Equivalent Bayesian Networksstat.ML

Characterizes Bayesian networks up to unconditional equivalence.

problem Characterizing Bayesian networks up to unconditional equivalence.

method Transformational characterization via undirected graphs and specified moves.

result Two DAGs are in the same UEC if and only if one can be transformed into the other via a finite sequence of moves.

Bayesian networks unconditional equivalence d-separation undirected graphs Markov equivalence

2022-03-01abs ↗pdf ↗

Membership Testing in Markov Equivalence Classes via Independence Query Oraclescs.LG

New method tests causal relationships from data without needing to learn the entire graph.

problem Testing if a causal graph belongs to a specific Markov equivalence class from observational data.

method Established bounds on the number of independence tests required and provided an algorithm that matches these bounds.

result Testing requires exponentially less independence tests compared to learning, especially in graphs with high in-degrees and small clique sizes.

causal inference Markov equivalence independence testing constraint-based methods DAG associahedron

2024-03-09abs ↗pdf ↗

Universal Lower Bound for Learning Causal DAGs with Atomic Interventionscs.LG

This work sets a universal lower bound for learning causal DAGs with atomic interventions.

problem Learning causal DAGs using only observational data results in a Markov equivalence class, requiring interventions to fully orient.

method Developed CBSP orderings and used them to prove a universal lower bound on the number of single-node interventions needed.

result The universal lower bound is within a factor of two of the minimum number of single-node interventions required to fully orient a given Markov equivalence class.

causal DAGs interventions Markov equivalence class clique-block shared-parents (CBSP) orderings

2021-11-09abs ↗pdf ↗

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networkscs.LG

Deep learning approach for efficient IoT task scheduling in MEC networks.

problem Minimizing task latency in IoT users with large-scale MEC systems.

method Stacked auto-encoder for data compression, adaptive simulated annealing, experience replay.

result Near-optimal performance with significantly reduced computational time.

Deep Reinforcement Learning Resource Scheduling Mobile Edge Computing IoT Task Latency

2020-01-24abs ↗pdf ↗

Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systemscs.LG

Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.

problem High energy consumption in self-powered edge computing systems.

method Developed a semi-distributed data-driven MAMRL framework to solve a two-stage linear stochastic programming problem.

result The proposed MAMRL framework reduces up to 11% non-renewable energy usage and 22.4% energy cost.

edge computing meta-reinforcement learning energy dispatch self-powered wireless networks

2020-02-20abs ↗pdf ↗

Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscs.DC

CodedFedL speeds up federated learning in MEC networks by 15x.

problem Slow convergence in federated learning due to heterogeneity and stochastic fluctuations.

method Injects structured coding redundancy into federated learning to mitigate stragglers and speed up training.

result CodedFedL speeds up the training procedure by up to 15x compared to benchmark schemes.

Federated Learning Coded Computing Edge Computing Stragglers Convergence Rate

2020-11-12abs ↗pdf ↗

Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scalestat.ML

A new method for efficient causal structure learning at scale.

problem Causal structure learning is computationally challenging at scale.

method Relaxed sparsest-permutation formulation with support-level relaxation and masked zero-fill incomplete Cholesky factorization.

result The method enables scalable comparison of candidate orderings and matches the accuracy of slower baselines.

causal discovery sparsest-permutation linear structural equation models scalable computation Cholesky factorization

2026-05-07abs ↗pdf ↗

Coded Computing for Federated Learning at the Edgecs.DC

Coded Federated Learning speeds up training in edge computing networks.

problem Slow convergence in Federated Learning due to heterogeneity and stochastic fluctuations.

method Exploiting statistical properties of compute and communication delays, distributed kernel embedding, and random Fourier features.

result Significant performance gains for CodedFedL in distributed non-linear regression and classification problems.

Federated Learning Edge Computing Coded Computing Random Fourier Features Performance Gains

2020-07-07abs ↗pdf ↗

Learning in the Sky: An Efficient 3D Placement of UAVseess.SP

Paper proposes efficient UAV placement for aerial base stations.

problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.

method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.

result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.

UAVs Aerial Base Stations Learning-based Placement Non-Cooperative Game Throughput

2020-03-02abs ↗pdf ↗

Reframed GES with a Neural Conditional Dependence Measurestat.ML

Reframed GES uses a neural conditional dependence measure for consistent causal structure learning.

problem Identifying causal structure in nonparametric settings.

method Reframed GES algorithm with a neural conditional dependence measure.

result Optimality and consistency of the reframed GES algorithm under standard assumptions.

GES neural conditional dependence causal inference nonparametric Markov equivalence

2022-06-17abs ↗pdf ↗

Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computingeess.SP

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wir…

2019-06-19abs ↗pdf ↗

Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networkscs.LG

Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight l…

2019-06-12abs ↗pdf ↗

Separation-based distance measures for causal graphsstat.ME

New measures assess differences in causal graphs' separations.

problem Evaluating causal discovery algorithms' output.

method Proposes new distance measures capturing causal graphs' separations.

result Proposed distances assess differences in causal graphs' separations.

causal graphs separation distance measures causal discovery Markov equivalence classes

2024-02-07abs ↗pdf ↗

Decoupled Entropy Minimizationcs.LG

AdaDEM decouples EM into two parts to improve class overlap and uncertainty.

problem Improper EM limits its effectiveness in various machine learning tasks.

method Decouple EM into CADF and GMC, and AdaDEM normalizes CADF reward and uses MEC.

result AdaDEM outperforms classical EM and improves performance in noisy and dynamic environments.

Entropy Minimization Machine Learning Class Overlap Uncertainty Adaptive Decoupling

2025-11-05abs ↗pdf ↗

Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcs.LG

Scheduling and power allocation improve federated learning efficiency in NOMA networks.

problem Efficiently scheduling and allocating power for federated learning in bandwidth-limited wireless networks.

method Proposed a scheduling policy and power allocation scheme using NOMA to maximize data rate and convergence speed.

result Simulation results show improved federated learning accuracy in NOMA networks.

Federated Learning NOMA Wireless Networks Scheduling Policy Power Allocation

2020-06-21abs ↗pdf ↗

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cellsq-bio.NC

Hexagon grid patterns emerge from conformal isometry in grid cell neural networks.

problem Understanding the algebraic, geometric, and topological properties of grid cells.

method Investigating recurrent neural network models of grid cells, focusing on Lie group and Lie algebra representations, conformal isometry, and hexagon periodic patterns.

result Conformal isometry leads to hexagon periodic patterns in grid cell responses and accurate path integration.

grid cells recurrent networks Lie group conformal isometry hexagon patterns

2022-10-06abs ↗pdf ↗

On sub-Riemannian geodesics in

SE(3)

whose spatial projections do not have cuspsmath.OC

We consider the problem $\mathbf{P_{curve}}$ of minimizing $\int \limits_0^L \sqrt{ξ^2 + κ^2(s)} \, {\rm d}s$ for a curve $\mathbf{x}$ on $\mathbb R$ with fixed boundary points and directions. Here the total length $L\geq 0$ is free, $s$ denotes the arclength parameter, $κ$ denotes the absolute curvature of $\mathbf{x}…

2013-05-26abs ↗pdf ↗

Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networkseess.SP

Federated learning optimizes task and resource allocation in balloon networks.

problem Minimizing energy and time consumption in task computation and transmission.

method SVM-based federated learning algorithm to dynamically adjust user associations, service sequences, and task allocations.

result Reduces the weighted sum of energy and time consumption by up to 16.1%.

Federated Learning Wireless Balloon Networks Task Allocation Energy Consumption Time Consumption

2020-03-19abs ↗pdf ↗