Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings
where experimentation is limited, such as healthcare. But, in these very same settings …
where experimentation is limited, such as healthcare. But, in these very same settings …
Predictive state recurrent neural networks
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering
and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural …
and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural …
Anchored discrete factor analysis
We present a semi-supervised learning algorithm for learning discrete factor analysis
models with arbitrary structure on the latent variables. Our algorithm assumes that every …
models with arbitrary structure on the latent variables. Our algorithm assumes that every …
Improving predictive state representations via gradient descent
Predictive state representations (PSRs) model dynamical systems using appropriately
chosen predictions about future observations as a representation of the current state. In …
chosen predictions about future observations as a representation of the current state. In …
Practical learning of predictive state representations
Over the past decade there has been considerable interest in spectral algorithms for
learning Predictive State Representations (PSRs). Spectral algorithms have appealing …
learning Predictive State Representations (PSRs). Spectral algorithms have appealing …
Fast and consistent learning of hidden Markov models by incorporating non-consecutive correlations
Can the parameters of a hidden Markov model (HMM) be estimated from a single sweep
through the observations–and additionally, without being trapped at a local optimum in the …
through the observations–and additionally, without being trapped at a local optimum in the …
Latent feature lasso
The latent feature model (LFM), proposed in (Griffiths\& Ghahramani, 2005), but possibly
with earlier origins, is a generalization of a mixture model, where each instance is generated …
with earlier origins, is a generalization of a mixture model, where each instance is generated …
Feature-weighted survival learning machine for COPD failure prediction
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as
hospital readmission and death in the United States, Canada and worldwide. COPD failure …
hospital readmission and death in the United States, Canada and worldwide. COPD failure …
Completing state representations using spectral learning
A central problem in dynamical system modeling is state discovery—that is, finding a
compact summary of the past that captures the information needed to predict the future …
compact summary of the past that captures the information needed to predict the future …
Identification of hidden Markov models using spectral learning with likelihood maximization
In this paper, we consider identifying a hidden Markov model (HMM) with the purpose of
computing estimates of joint and conditional (posterior) probabilities over observation …
computing estimates of joint and conditional (posterior) probabilities over observation …