11 citations · 24 across the 8 of their papers we have counts for
9 papers
An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
Nian Ran, Tianhe Jia, Zhengyang Mu +6
Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery. Yet rapid initial gains often give…
Federated Loss Exploration for Improved Convergence on Non-IID Data
Christian Internò, Markus Olhofer, Yaochu Jin +1
Federated learning (FL) has emerged as a groundbreaking paradigm in machine learning (ML), offering privacy-preserving collaborative model training across diverse datasets. Despite…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
Coding by Design: GPT-4 empowers Agile Model Driven Development
Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer
Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has…
Bi-level Network Design for UAM Vertiport Allocation Using Activity-Based Transport Simulations
Sebastian Brulin, Markus Olhofer
The design or the optimization of transport systems is a difficult task. This is especially true in the case of the introduction of new transport modes in an existing system. The m…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
Yali Wang, Steffen Limmer, Markus Olhofer +2
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference base…