multi-objective

This paper introduces Lexi-LowGLM, an efficient algorithm for generalized low-rank matrix bandits with multiple prioritized objectives, using online Newton updates to reduce computational complexity and achieve regret bounds depending on effective low-rank dimensions.

This paper introduces THV-UCB, an algorithm for multi-objective bandit problems with slate selection, and establishes gap-free and gap-dependent regret bounds for hypervolume regret.

This paper proposes a framework to automatically optimize prompts for LLM-based user simulators in conversational recommender systems, addressing issues like positive bias and limited behavioral diversity.

This paper introduces a novel preference-conditioned Bellman operator based on Chebyshev scalarization to compute deterministic Pareto-optimal policies for Multi-Objective Markov Decision Processes, proving its convergence and effectiveness in capturing the entire Pareto frontier.

Proposes a multi-objective reinforcement learning framework combining semantic embeddings with Pareto-DQN to balance engagement, diversity, and fairness in recommendations, mitigating filter bubbles.

Introduces Holistic Data Scheduler (HDS), a reinforcement learning-based framework that dynamically adjusts data mixtures during LLM pre-training using a multi-objective reward function, achieving 44% fewer iterations to reach target perplexity and a 7.2% improvement on MMLU.

This paper proposes a POMDP framework for multi-objective decision making in lithium production, addressing geological, demand, and pricing uncertainties to optimize mine opening and extraction method selection. The approach outperforms human-inspired heuristics by dynamically adapting to shifting price regimes through belief state planning.

CRAFT is a Pareto-front prompt optimizer that jointly optimizes for accuracy and token cost, avoiding the 'scalarization collapse' of weighted-sum approaches by maintaining a diverse population of prompts across the accuracy-cost trade-off frontier using NSGA-II and budget-aware validation.

Presents WeCon, a weight-conditioned neural solver for multi-objective combinatorial optimization problems that achieves comparable hypervolume to the state-of-the-art while reducing inference time by 40%.

This paper introduces MOCI, a novel framework for inferring shared constraints and individual preferences from heterogeneous expert demonstrations in reinforcement learning, outperforming existing baselines in predictive performance and computational efficiency.