30 citations · 54 across the 21 of their papers we have counts for
6 papers · 1 filter
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes
Taylor Killian, Samuel Daulton, George Konidaris +1
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…
Supervised topic models for clinical interpretability
Michael C. Hughes, Huseyin Melih Elibol, Thomas McCoy +2
Supervised topic models can help clinical researchers find interpretable cooccurence patterns in count data that are relevant for diagnostics. However, standard formulations of sup…
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes
Taylor Killian, George Konidaris, Finale Doshi-Velez
Due to physiological variation, patients diagnosed with the same condition may exhibit divergent, but related, responses to the same treatments. Hidden Parameter Markov Decision Pr…
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Viktoriya Krakovna, Finale Doshi-Velez
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, a…
Rapid Posterior Exploration in Bayesian Non-negative Matrix Factorization
M. Arjumand Masood, Finale Doshi-Velez
Non-negative Matrix Factorization (NMF) is a popular tool for data exploration. Bayesian NMF promises to also characterize uncertainty in the factorization. Unfortunately, current…
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Finale Doshi-Velez, Byron Wallace, Ryan Adams
Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the m…