Search Results for author:
Found 36 papers, 11 papers with code
Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables
In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold.
Self-Improved Learning for Scalable Neural Combinatorial Optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks.
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
The core philosophy of CMiMC is to preserve discriminative information of individual views in the collaborative view by maximizing mutual information between pre- and post-collaboration features while enhancing the efficacy of collaborative views by minimizing the loss function of downstream tasks.
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint.
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
Escaping Local Optima in Global Placement
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences.
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker.
Multi-Objective Reinforcement Learning
Multi-Task Learning
+1
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security.
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks
Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning
Researchers have struggled to design fair FL systems that ensure fairness of results.
Evolutionary Pareto Set Learning with Structure Constraints
In our approach, the Pareto optimality can be traded off with a preferred structure among the whole solution set, which could be crucial for many real-world problems.
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach
This study proposes an innovative model-based modular approach (MMA) to dynamically optimize order matching and vehicle relocation in a ride-hailing platform.
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption
We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information.
Continuation Path Learning for Homotopy Optimization
Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems.
Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach
The integrated development of city clusters has given rise to an increasing demand for intercity travel.
Exploring the Cognitive Dynamics of Artificial Intelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study of ChatGPT
The ultimate objective of this study is to instigate a scholarly discourse on the interplay between technological advancements in education and the evolution of human learning patterns, raising the question of whether technology is driving human evolution or vice versa.
Pareto Set Learning for Expensive Multi-Objective Optimization
This work represents the first attempt to model the Pareto set for expensive multi-objective optimization.
Pareto Set Learning for Neural Multi-objective Combinatorial Optimization
In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure.
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images
By representing object instances within the same category as shape and appearance variation of a shared NeRF template, our proposed method can achieve dense shape correspondences reasoning on images for a wide range of object classes.
Preference Conditioned Neural Multi-objective Combinatorial Optimization
In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure.
Aggregate Modeling and Equilibrium Analysis of the Crowdsourcing Market for Autonomous Vehicles
We call such a mobility market with AV renting options the "AV crowdsourcing market".
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC).
Superconductor-metal quantum transition at the EuO-KTaO3 interface
Superconductivity has been one of the most fascinating quantum states of matter for over several decades.
Superconductivity Mesoscale and Nanoscale Physics Materials Science
Controllable Pareto Multi-Task Learning
With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together.
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach
Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable constituent in urban logistics distribution systems.
Computational Efficiency
Multi-agent Reinforcement Learning
+2
Pareto Multi-Task Learning
Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
A Keyframe-based Continuous Visual SLAM for RGB-D Cameras via Nonparametric Joint Geometric and Appearance Representation
The experimental evaluations using publicly available RGB-D benchmarks show that the developed keyframe selection technique using continuous visual odometry outperforms its robust dense (and direct) visual odometry equivalent.
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
The proposed algorithm uses the Bayesian neural network as the scalable surrogate model.
Nonlinear Collaborative Scheme for Deep Neural Networks
Different from them, in this paper, we aim to link the generalization ability of a deep network to optimizing a new objective function.
Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications.
