Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study. - Daniel Graves; Witold Pedrycz (Analysis and Design of Intelligent Systems using Soft Computing Techniques, 2007)

Multivariate Segmentation of Time Series with Differential Evolution. - Daniel Graves; Witold Pedrycz (IFSA/EUSFLAT Conf., 2009)

Fuzzy prediction architecture using recurrent neural networks. - Daniel Graves; Witold Pedrycz (Neurocomputing, 2009)

Proximity fuzzy clustering and its application to time series clustering and prediction. - Daniel Graves; Witold Pedrycz (ISDA, 2010)

Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. - Daniel Graves; Witold Pedrycz (Fuzzy Sets Syst., 2010)

Clustering with proximity knowledge and relational knowledge. - Daniel Graves; Joost Noppen; Witold Pedrycz (Pattern Recognit., 2012)

A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning. - Xin Xu 001; Zhenhua Huang 004; Daniel Graves; Witold Pedrycz (IEEE Trans. Cybern., 2014)

A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. - Yingxu Wang 001; Henry Leung; Marina L. Gavrilova; Omar A. Zatarain; Daniel Graves; Jianhua Lu; Newton Howard; Sam Kwong; Phillip C.-Y. Sheu; Shushma Patel (ICCI*CC, 2018)

Sequence Learning for Images Recognition in Videos with Differential Neural Networks. - Yingxu Wang 001; Omar A. Zatarain; Tony Tsai; Daniel Graves (ICCI*CC, 2019)

Perception as prediction using general value functions in autonomous driving applications. - Daniel Graves; Kasra Rezaee; Sean Scheideman (IROS, 2019)

Importance Resampling for Off-policy Prediction. - Matthew Schlegel; Wesley Chung; Daniel Graves; Jian Qian; Martha White (NeurIPS, 2019)

Performance analysis and optimization for scalable deployment of deep learning models for country-scale settlement mapping on Titan supercomputer. - Kuldeep R. Kurte; Jibonananda Sanyal; Anne Berres; Dalton D. Lunga; Mark Coletti; Hsiuhan Lexie Yang; Daniel Graves; Benjamin Liebersohn; Amy N. Rose (Concurr. Comput. Pract. Exp., 2019)

Efficient decorrelation of features using Gramian in Reinforcement Learning. - Borislav Mavrin; Daniel Graves; Alan Chan 001 (CoRR, 2019)

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. - Kristopher De Asis; Alan Chan 001; Silviu Pitis; Richard S. Sutton; Daniel Graves (AAAI, 2020)

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Chongxi Huang; Ying Wen 001; et al. (CoRL, 2020)

Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. - Jun Jin 001; Nhat M. Nguyen; Nazmus Sakib; Daniel Graves; Hengshuai Yao; Martin Jägersand (ICRA, 2020)

Learning predictive representations in autonomous driving to improve deep reinforcement learning. - Daniel Graves; Nhat M. Nguyen; Kimia Hassanzadeh; Jun Jin 001 (CoRR, 2020)

What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator. - Hongyao Tang; Zhaopeng Meng; Jianye Hao; Chen Chen 077; Daniel Graves; Dong Li 016; Wulong Liu; Yaodong Yang 002 (CoRR, 2020)

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Aurora Chongxi Huang; Ying Wen 001; et al. (CoRR, 2020)

Offline Learning of Counterfactual Perception as Prediction for Real-World Robotic Reinforcement Learning. - Jun Jin 001; Daniel Graves; Cameron Haigh; Jun Luo 009; Martin Jägersand (CoRR, 2020)

LISPR: An Options Framework for Policy Reuse with Reinforcement Learning. - Daniel Graves; Jun Jin 001; Jun Luo 009 (CoRR, 2020)

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. - Yaodong Yang 002; Jun Luo 009; Ying Wen 001; Oliver Slumbers; Daniel Graves; Haitham Bou-Ammar; Jun Wang 012; Matthew E. Taylor (AAMAS, 2021)

Learning robust driving policies without online exploration. - Daniel Graves; Nhat M. Nguyen; Kimia Hassanzadeh; Jun Jin 001; Jun Luo 009 (ICRA, 2021)

What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator. - Hongyao Tang; Zhaopeng Meng; Jianye Hao; Chen Chen 077; Daniel Graves; Dong Li 016; Changmin Yu; Hangyu Mao; Wulong Liu; Yaodong Yang 002; Wenyuan Tao; Li Wang (AAAI, 2022)

Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning. - Jun Jin 001; Daniel Graves; Cameron Haigh; Jun Luo 009; Martin Jägersand (ICRA, 2022)

Affordance as general value function: a computational model. - Daniel Graves; Johannes Günther 002; Jun Luo 009 (Adapt. Behav., 2022)