arXiv:2511. 06454v3 Announce Type: replace-cross Abstract: We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis.
arXiv:2406. 06629v2 Announce Type: replace Abstract: This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization.
arXiv:2607. 09566v1 Announce Type: cross Abstract: Decision-making is posing an increasingly formidable challenge to investors because of the growing number of alternatives available in financial markets.
arXiv:2606. 03904v1 Announce Type: new Abstract: Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{kingma2015adam}.
arXiv:2608. 06808v1 Announce Type: new Abstract: The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems.
arXiv:2605. 04954v2 Announce Type: replace-cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance.
arXiv:2608. 03129v1 Announce Type: new Abstract: Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design.
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In M...
arXiv:2607. 23009v1 Announce Type: new Abstract: Reusing previously computed results is a long-standing principle for reducing computational cost, but such reuse has largely been confined to a single problem's computation.
arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.
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.
arXiv:2606. 17523v1 Announce Type: cross Abstract: Information-Geometric Optimization (IGO) provides a unified framework for black-box optimization by interpreting the adaptation of a search distribution as a natural gradient update.
arXiv:2606. 26728v1 Announce Type: new Abstract: Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity.