Search Results for author:
Found 7 papers, 3 papers with code
ACPO: AI-Enabled Compiler-Driven Program Optimization
The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler.
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning
Transferring representation for multitask imitation learning has the potential to provide improved sample efficiency on learning new tasks, when compared to learning from scratch.
Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task.
MLGOPerf: An ML Guided Inliner to Optimize Performance
This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner.
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks.
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control.
Satellite Navigation for the Age of Autonomy
Global Navigation Satellite Systems (GNSS) brought navigation to the masses.
