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Found 6 papers, 2 papers with code
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
Multi-Objective Online Learning
This paper presents a systematic study of multi-objective online learning.
Asynchronous Decentralized Online Learning
To overcome this problem, we study decentralized online learning in the asynchronous setting, which allows different learners to work at their own pace.
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards.
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation.
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Aligning language models to human expectations, e. g., being helpful and harmless, has become a pressing challenge for large language models.
