Copertina

Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Jiang, Chao, Huang, Jingyu, Li, Miqing
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
di: Ahmadianshalchi, Alaleh, et al.
Pubblicazione: (2024)

Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
di: Bian, Chao, et al.
Pubblicazione: (2023)

Do We Really Need a Large Number of Visual Prompts?
di: Kim, Youngeun, et al.
Pubblicazione: (2023)

MambaOut: Do We Really Need Mamba for Vision?
di: Yu, Weihao, et al.
Pubblicazione: (2024)

QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?
di: Li, Yuanjun, et al.
Pubblicazione: (2025)

Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?
di: Zhao, Zijian, et al.
Pubblicazione: (2025)

MMO: Meta Multi-Objectivization for Software Configuration Tuning
di: Chen, Pengzhou, et al.
Pubblicazione: (2021)

POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
di: Lomurno, Eugenio, et al.
Pubblicazione: (2024)

Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
di: Xu, Peipei, et al.
Pubblicazione: (2026)

Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
di: Wilm, Timo, et al.
Pubblicazione: (2024)

Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
di: Vhaduri, Sudip, et al.
Pubblicazione: (2026)

Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
di: Zhao, Zhonghan, et al.
Pubblicazione: (2024)

Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute
di: Chen, Jianhao, et al.
Pubblicazione: (2025)

Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution
di: Qin, Tian, et al.
Pubblicazione: (2026)

Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
di: Ye, Yulong, et al.
Pubblicazione: (2026)

Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability
di: Jabs, Christoph, et al.
Pubblicazione: (2025)

Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
di: Wang, Yanbo, et al.
Pubblicazione: (2025)

Adapting Multi-objectivized Software Configuration Tuning
di: Chen, Tao, et al.
Pubblicazione: (2024)

Approximating Pareto Frontiers in Stochastic Multi-Objective Optimization via Hashing and Randomization
di: Li, Jinzhao, et al.
Pubblicazione: (2026)

Should We Really Edit Language Models? On the Evaluation of Edited Language Models
di: Li, Qi, et al.
Pubblicazione: (2024)

Distilled Lifelong Self-Adaptation for Configurable Systems
di: Ye, Yulong, et al.
Pubblicazione: (2025)

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
di: Berdica, Uljad, et al.
Pubblicazione: (2026)

Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
di: Speziali, Paolo, et al.
Pubblicazione: (2026)

Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
di: Liu, Yu, et al.
Pubblicazione: (2025)

Pareto Multi-Objective Alignment for Language Models
di: He, Qiang, et al.
Pubblicazione: (2025)

Optimisation Is Not What You Need
di: Ibias, Alfredo
Pubblicazione: (2025)

How Far Are We From AGI: Are LLMs All We Need?
di: Feng, Tao, et al.
Pubblicazione: (2024)

Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
di: Liu, Xiaoze, et al.
Pubblicazione: (2026)

Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization
di: Shrestha, Jatan, et al.
Pubblicazione: (2026)

Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
di: Rosero, Juan C., et al.
Pubblicazione: (2024)

Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
di: Lu, Shuo, et al.
Pubblicazione: (2026)

How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve
di: Reda, Waleed, et al.
Pubblicazione: (2025)

Do We Need Frontier Models to Verify Mathematical Proofs?
di: Naik, Aaditya, et al.
Pubblicazione: (2026)

On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
di: Tang, Menghao, et al.
Pubblicazione: (2026)

ParetoFlow: Guided Flows in Multi-Objective Optimization
di: Yuan, Ye, et al.
Pubblicazione: (2024)

Learning Pareto Set for Multi-Objective Continuous Robot Control
di: Shu, Tianye, et al.
Pubblicazione: (2024)

Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
di: Rachman, Rifny, et al.
Pubblicazione: (2025)

Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities?
di: Jin, Jiahe, et al.
Pubblicazione: (2025)

Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
di: Zhang, Kaituo, et al.
Pubblicazione: (2026)

AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbots
di: Rath, Emma, et al.
Pubblicazione: (2025)