327 citations · 783 across the 13 of their papers we have counts for
13 papers
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou, Han Liu, Farhad Pourpanah +2
Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient k…
EQL -- an extremely easy to learn knowledge graph query language, achieving highspeed and precise search
Han Liu, Shantao Liu
EQL, also named as Extremely Simple Query Language, can be widely used in the field of knowledge graph, precise search, strong artificial intelligence, database, smart speaker ,pat…
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
Boxin Wang, Hengzhi Pei, Boyuan Pan +3
Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Thou…
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu +2
Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challen…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
Fully Implicit Online Learning
Chaobing Song, Ji Liu, Han Liu +2
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial t…
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Peng Sun, Xinghai Sun, Lei Han +8
Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and i…
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Ming Zhou, Yong Chen, Ying Wen +5
Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to…
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Jianqing Fan, Han Liu, Zhaoran Wang +1
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data. As examples, we study sparse…
Feedback-Based Tree Search for Reinforcement Learning
Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL)…
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu +2
We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network.…
Continual Learning in Generative Adversarial Nets
Ari Seff, Alex Beatson, Daniel Suo +1
Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that trai…
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Xingguo Li, Haoming Jiang, Jarvis Haupt +4
Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility. However, by exploring the modeling structures…