5 papers
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
David Wu, Sanjiban Choudhury
Existing inverse reinforcement learning methods (e.g. MaxEntIRL, -IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop. This…
Testing side-channel security of cryptographic implementations against future microarchitectures
Gilles Barthe, Marcel Böhme, Sunjay Cauligi +7
How will future microarchitectures impact the security of existing cryptographic implementations? As we cannot keep reducing the size of transistors, chip vendors have started deve…
CryptOpt: Automatic Optimization of Straightline Code
Joel Kuepper, Andres Erbsen, Jason Gross +9
Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware. Compilers try to address these problems; h…
Self-Explaining Deviations for Coordination
Hengyuan Hu, Samuel Sokota, David Wu +4
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which huma…