Search Results for author: Arun Venkatraman

Found 5 papers, 0 papers with code

Feedback in Imitation Learning: The Three Regimes of Covariate Shift

no code implementations4 Feb 2021 Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian Ziebart, J. Andrew Bagnell

The learner often comes to rely on features that are strongly predictive of decisions, but are subject to strong covariate shift.

Causal Inference Decision Making +1

Predictive-State Decoders: Encoding the Future into Recurrent Networks

no code implementations NeurIPS 2017 Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell

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.

Imitation Learning

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

no code implementations ICML 2017 Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

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.

Decision Making Dependency Parsing +1

Gradient Boosting on Stochastic Data Streams

no code implementations1 Mar 2017 Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

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.

Learning to Filter with Predictive State Inference Machines

no code implementations30 Dec 2015 Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell

Latent state space models are a fundamental and widely used tool for modeling dynamical systems.

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