3.2k citations · 5.1k across the 137 of their papers we have counts for
9 papers · 1 filter
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…
An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization
Arjumand Masood, Weiwei Pan, Finale Doshi-Velez
In this work, we empirically explore the question: how can we assess the quality of samples from some target distribution? We assume that the samples are provided by some valid Mon…
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…
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning.…
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations
W. Pan, F. Doshi-Velez
Nonnegative matrix factorization (NMF) is a popular dimension reduction technique that produces interpretable decomposition of the data into parts. However, this decompostion is no…
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Method of moment estimators exhibit appealing statistical properties, such as asymptotic unbiasedness, for nonconvex problems. However, they typically require a large number of sam…