19 citations · 150 across the 48 of their papers we have counts for
49 papers
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Taichi Liu, Chen Gao, Zhenyu Wang +4
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem…
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Junyi Wang, Yuanyang Zhu, Zhi Wang +3
Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone…
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Yao Lai, Jinxin Liu, Zhentao Tang +3
Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL…
Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang, Yunjie Ye, Lei Feng +6
This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle…
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder
Yueen Ma, Dafeng Chi, Jingjing Li +3
The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art generative quality, they often neglect…
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Fei Ni, Jianye Hao, Yao Mu +4
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL). However, these works mostly lack the generalizati…
GFlowNets with Human Feedback
Yinchuan Li, Shuang Luo, Yunfeng Shao +1
We propose the GFlowNets with Human Feedback (GFlowHF) framework to improve the exploration ability when training AI models. For tasks where the reward is unknown, we fit the rewar…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu +5
The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which c…
Generalized Universal Domain Adaptation with Generative Flow Networks
Didi Zhu, Yinchuan Li, Yunfeng Shao +5
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target label…
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Yinchuan Li, Zhigang Li, Wenqian Li +3
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neur…
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Hao Chen, Jiaze Wang, Kun Shao +5
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory…
Out-of-distribution Detection with Implicit Outlier Transformation
Qizhou Wang, Junjie Ye, Feng Liu +5
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typi…
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Bowen Wang, Chen Liang, Jiaze Wang +7
Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with…
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks
Wenqian Li, Yinchuan Li, Zhigang Li +2
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph,…
CFlowNets: Continuous Control with Generative Flow Networks
Yinchuan Li, Shuang Luo, Haozhi Wang +1
Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate dis…
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting
Hongyao Tang, Min Zhang, Jianye Hao
Knowing the learning dynamics of policy is significant to unveiling the mysteries of Reinforcement Learning (RL). It is especially crucial yet challenging to Deep RL, from which th…
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang +5
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massiv…
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao +1
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learnin…
Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization
Junlong Lyu, Zhitang Chen, Wenlong Lyu +1
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems. Our method investigates the intrinsic connection between posterior distribut…
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Hao Chen +5
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network…
Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents
Minghuan Liu, Zhengbang Zhu, Menghui Zhu +3
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their deve…
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
Junjie Wang, Yao Mu, Dong Li +6
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse…