1 citations · 2 across the 30 of their papers we have counts for
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Distributional Multi-objective Black-box Optimization for Diffusion-model Inference-time Multi-Target Generation
Kim Yong Tan, Yueming Lyu, Ivor Tsang +1
Diffusion models have been successful in learning complex data distributions. This capability has driven their application to high-dimensional multi-objective black-box optimizatio…
MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming
Chengqi Zheng, Jianda Chen, Yueming Lyu +5
Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLM-driven constructio…
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
Kim Yong Tan, Yueming Lyu, Ivor Tsang +1
Guided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion model to address specific downstream tasks. Existing gu…
Mastering Continual Reinforcement Learning through Fine-Grained Sparse Network Allocation and Dormant Neuron Exploration
Chengqi Zheng, Haiyan Yin, Jianda Chen +3
Continual Reinforcement Learning (CRL) is essential for developing agents that can learn, adapt, and accumulate knowledge over time. However, a fundamental challenge persists as ag…
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration
Xingrui Yu, Zhenglin Wan, David Mark Bossens +3
Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because…
Sharpness-Aware Black-Box Optimization
Feiyang Ye, Yueming Lyu, Xuehao Wang +3
Black-box optimization algorithms have been widely used in various machine learning problems, including reinforcement learning and prompt fine-tuning. However, directly optimizing…
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
Zhenglin Wan, Xingrui Yu, David Mark Bossens +5
Imitation learning (IL) has shown promise in various applications (e.g. robot locomotion) but is often limited to learning a single expert policy, constraining behavior diversity a…
Cross-Context Backdoor Attacks against Graph Prompt Learning
Xiaoting Lyu, Yufei Han, Wei Wang +3
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learn…
Transductive Reward Inference on Graph
Bohao Qu, Xiaofeng Cao, Qing Guo +3
In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in o…
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
Feiyang Ye, Baijiong Lin, Xiaofeng Cao +2
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level su…
Nonparametric Teaching for Multiple Learners
Chen Zhang, Xiaofeng Cao, Weiyang Liu +2
We study the problem of teaching multiple learners simultaneously in the nonparametric iterative teaching setting, where the teacher iteratively provides examples to the learner fo…