The paper presents Iterative Sequential Transfer (IST), a method for few-shot multiobjective multitask optimization that addresses the bottleneck of aligning elite solution distributions across tasks. IST treats multitask optimization as a sequence of transfer problems, focusing evaluations on one target task per iteration and using a likelihood-informed prioritization to select the task most ready for knowledge integration. Experiments on benchmark and real-world problems demonstrate IST’s effectiveness under tight evaluation budgets.
arXiv:2605. 01712v2 Announce Type: replace Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions.
By Xinyue Chen, Yingxuan Liang, Yiqin Huang, Chikai Shang, Hai-Lin Liu, Fangqing Gu
arXiv:2609.25836v1 Announce Type: new
Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation...
By Tingyang Wei, Haofeng Wu, Jiao Liu, Zhao Wei, Puay Siew Tan, Yew-Soon Ong
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning proposes a new scalarization method for multi-task learning that is invariant to the relative scales of task losses. By using a logarithmic transformation, SIMS converts the multi-objective problem into a single objective that preserves weak Pareto optimality and allows a smooth surrogate with controllable approximation error. Experiments on standard multi-task benchmarks show that SIMS consistently outperforms existing scalarization methods and achieves state‑of‑the‑art performance.
By Zebin Chen, Fei Xing, Yang Chen, Hua Liu, Andy HF Chow, Yuhua Qian, Yu Zhang
arXiv:2606. 14900v1 Announce Type: new Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible.
By Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar, Salimeh Yasaei Sekeh
arXiv:2607. 09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job.
By Carl Hvarfner, Sam Daulton, Max Balandat, Eytan Bakshy
arXiv:2604. 21991v2 Announce Type: replace Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel.
By Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben, Anil Yaman
Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives. Diffusion models trained on such data have emer...
arXiv:2608. 20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost.
By Nayeon Kim, Hojin Lee, Yunju Bak, Jaesun Park, Boseop Kim
arXiv:2406. 09770v2 Announce Type: replace-cross Abstract: Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models.
By Anke Tang, Li Shen, Yong Luo, Shiwei Liu, Han Hu, Bo Du, Dacheng Tao
arXiv:2606. 02221v1 Announce Type: cross Abstract: Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains.
By Chengfeng Wu, Tao Zou, Yanru Wu, Jingge Wang
arXiv:2609.38920v1 Announce Type: new
Abstract: Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives...
By Yuan Lu, Esha Singh, Yi-An Ma, Yusu Wang