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Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d.
Is imitation learning the route to humanoid robots?
S. Schaal · 1999
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Approximately optimal approximate reinforcement learning
S. Kakade and J. Langford · 2002
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Max margin markov networks
B. Taskar, C. Guestrin, and D. Koller · 2003
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Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
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On the generalization ability of on-line learning algorithms
N. Cesa-Bianchi, A. Conconi, and C. Gentile · 2004
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Error limiting reductions between classification tasks
A. Beygelzimer, V. Dani, T. Hayes, J. Langford, and B. Zadrozny · 2005
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Logarithmic regret algorithms for online convex optimization
E. Hazan, A. Kalai, S. Kale, and A. Agarwal · 2006
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Lower bounds for reductions, 2006
M. Kääriäinen · 2006
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Boosting structured prediction for imitation learning
N. Ratliff, D. Bradley, J. A. Bagnell, and J. Chestnutt · 2006
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(Online) subgradient methods for structured prediction
N. Ratliff, J. A. Bagnell, and M. Zinkevich · 2007
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Mind the duality gap: Logarithmic regret algorithms for online optimization
S. Kakade and S. Shalev-Shwartz · 2008
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High performance outdoor navigation from overhead data using imitation learning
D. Silver, J. A. Bagnell, and A. Stentz · 2008
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A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
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sgd code, 2009
L. Bottou · 2009
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Interactive policy learning through confidence-based autonomy
S. Chernova and M. Veloso · 2009
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Search-based structured prediction
H. Daumé III, J. Langford, and D. Marcu · 2009
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On the generalization ability of online strongly convex programming algorithms
S. Kakade and A. Tewari · 2009
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Mario AI Competition, 2009
J. Togelius and S. Karakovskiy · 2009
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Efficient reductions for imitation learning
S. Ross and J. A. Bagnell · 2010
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