Building predictive models of healthcare costs with open healthcare data
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
Comments 2020 IEEE International Conference on Healthcare Informatics (ICHI)
视觉与机器人
面向环境建模、时序预测、仿真规划、具身智能和自动驾驶的世界模型方法与应用。
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
Comments 2020 IEEE International Conference on Healthcare Informatics (ICHI)
专题命中 通用世界模型 :environment model(abstract);model-based reinforcement learning(abstract);分类 cs.LG
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
Comments Classifier; Demographic Parity; Discrimination; Equal Opportunity; Fairness; Penalized regression; Proxy; Statistical Discrimination
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments Accepted in Journal of Neural Engineering
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments 13 pages, 4 figures, 6 tables
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments Submitted to SETN2022
专题命中 通用世界模型 :environment model(abstract);分类 cs.AI、cs.LG、cs.MA;dynamics model(abstract)
Comments Paper accepted at IJCAI 2021
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
Comments 10 pages
Journal ref In Proceedings of ICAIL 2021, pp. 129-138. 2021
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.LG、cs.CV
Comments 25 Pages, 12 Figures
专题命中 通用世界模型 :environment model(abstract);model-based reinforcement learning(abstract);分类 cs.LG
Comments Appears in NeurIPS 2020
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
专题命中 通用世界模型 :environment model(abstract);model-based reinforcement learning(abstract);分类 cs.LG
Journal ref 1st International Workshop on Industrial Recommendation Systems (IRS), KDD 2020
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
Comments 12 pages, 13 figures
Journal ref New J. Phys. 22 045003 (2020)
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.AI
专题命中 通用世界模型 :environment model(abstract);分类 cs.AI、cs.LG、cs.CV;dynamics model(abstract)
Comments Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 2020
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. Previously presented at the NeurIPS 2018 Causal Learning Workshop
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title);分类 cs.LG
专题命中 通用世界模型 :environment model(abstract);model-based reinforcement learning(abstract);分类 cs.LG
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments To appear:Twenty-third Americas Conference on Information Systems, Boston, 2017
专题命中 通用世界模型 :predictive model(title,abstract);predictive models(title,abstract)
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
专题命中 通用世界模型 :model-based reinforcement learning(abstract);分类 cs.AI、cs.LG、cs.CV;predictive model(abstract)
专题命中 通用世界模型 :predictive model(title);predictive models(title);分类 cs.AI、cs.LG
Comments The paper has been withdrawn by the authors. The current version is incomplete and the work is still on going. The algorithm gives poor results for a particular setting and we are working on it. However, we are not planning to submit a revision of the paper. This work is going to take some time and we want to withdraw the current version since it is not in a good shape and needs a lot more work to be in publishable condition