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Learning to Filter with Predictive State Inference Machines
Latent state space models are a fundamental and widely used tool for modeling dynamical systems.
Gradient Boosting on Stochastic Data Streams
To generalize from batch to online, we first introduce the definition of online weak learning edge with which for strongly convex and smooth loss functions, we present an algorithm, Streaming Gradient Boosting (SGB) with exponential shrinkage guarantees in the number of weak learners.
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
We demonstrate that AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL) approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve faster and better solutions with less training data than a less-informed Reinforcement Learning (RL) technique.
Predictive-State Decoders: Encoding the Future into Recurrent Networks
We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations.
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
The learner often comes to rely on features that are strongly predictive of decisions, but are subject to strong covariate shift.
