{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-and-efficient-transfer-learning-with-1","title":"Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes","arxiv_id":null,"date":"2017-12-01","proceeding":"NeurIPS 2017 12","authors":["Taylor W. Killian","Samuel Daulton","George Konidaris","Finale Doshi-Velez"],"abstract":"We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.  Our new framework correctly models the joint uncertainty in the latent parameters and the state space.  We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference.  Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.","url_abs":"http://papers.nips.cc/paper/7205-robust-and-efficient-transfer-learning-with-hidden-parameter-markov-decision-processes","url_pdf":"http://papers.nips.cc/paper/7205-robust-and-efficient-transfer-learning-with-hidden-parameter-markov-decision-processes.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-and-efficient-transfer-learning-with-1","repo_url":"https://github.com/dtak/hip-mdp-public","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}