84 citations · 129 across the 5 of their papers we have counts for
6 papers
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman +2
Imitation learning practitioners have often noted that conditioning policies on previous actions leads to a dramatic divergence between "held out" error and performance of the lear…
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun +5
Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement trai…
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon +2
Researchers have demonstrated state-of-the-art performance in sequential decision making problems (e.g., robotics control, sequential prediction) with deep neural network models. O…
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman +2
Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the p…
Learning to Filter with Predictive State Inference Machines
Wen Sun, Arun Venkatraman, Byron Boots +1
Latent state space models are a fundamental and widely used tool for modeling dynamical systems. However, they are difficult to learn from data and learned models often lack perfor…
Autonomy Infused Teleoperation with Application to BCI Manipulation
Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois +7
Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interf…