106 citations · 297 across the 21 of their papers we have counts for
21 papers
Mathematical Challenges in Deep Learning
Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev +8
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challe…
Conformalized Fairness via Quantile Regression
Meichen Liu, Lei Ding, Dengdeng Yu +3
Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness rema…
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Bowen Wang, Guibao Shen, Dong Li +7
Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation f…
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois, Xuening Feng, Zhaohui Jiang +4
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in…
New physics in semileptonic transitions: rare hyperon vs. kaon decays
Li-Sheng Geng, Jorge Martin Camalich, Rui-Xiang Shi
We investigate the potential of rare hyperon decays to probe the short distance structure in the and transitions. Hyperon decays into neutrinos…
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
Amur Ghose, Vincent Zhang, Yingxue Zhang +3
Presently with technology node scaling, an accurate prediction model at early design stages can significantly reduce the design cycle. Especially during logic synthesis, predicting…
Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles
Zhitao Wang, Yuzheng Zhuang, Qiang Gu +3
For autonomous vehicles integrating onto roadways with human traffic participants, it requires understanding and adapting to the participants' intention and driving styles by respo…
: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Xinlin Li, Bang Liu, Yaoliang Yu +3
Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast a…
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Tianze Zhou, Fubiao Zhang, Kun Shao +10
Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Zhihao Ma, Yuzheng Zhuang, Paul Weng +4
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks. However, this black-box approach fails to explain the learned policy i…
Addressing Action Oscillations through Learning Policy Inertia
Chen Chen, Hongyao Tang, Jianye Hao +2
Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective in a wide range of challenging decision making and control tasks. However, these methods typical…
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +6
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially with function approximation, can be challenging since it involves the stochasticit…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao +4
Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Me…
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Cong Fei, Bin Wang, Yuzheng Zhuang +5
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However,…
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang, Weixun Wang, Hongyao Tang +9
Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can sh…
Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Ben Liao +4
In many real-world tasks, multiple agents must learn to coordinate with each other given their private observations and limited communication ability. Deep multiagent reinforcement…
Multi-Agent Interactions Modeling with Correlated Policies
Minghuan Liu, Ming Zhou, Weinan Zhang +4
In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstratio…
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
Hangyu Mao, Wulong Liu, Jianye Hao +5
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about thei…
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Haotian Fu, Hongyao Tang, Jianye Hao +2
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work w…