Sampling-based Algorithms for Efficient and Scalable AI
, MIT CSAIL
Date: Monday, August 02, 2021
Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone
Event Type: Thesis Defense
Host: Daniela Rus , MIT CSAIL
Contact: Cenk Baykal, email@example.com
Speaker URL: None
TALK: Thesis Defense (Cenk Baykal): Sampling-based Algorithms for Efficient and Scalable AI
Thesis supervisor: Daniela Rus
Thesis committee: Piotr Indyk, Aleksander Mądry
We present sampling-based algorithms with provable guarantees to alleviate the increasingly prohibitive costs of training and deploying modern AI systems. At the core of this thesis lies importance sampling, which we use to construct representative subsets of inputs and compress machine learning models to enable fast and deployable systems. We provide theoretical guarantees on the representativeness of the generated subsamples for a variety of objectives, ranging from eliminating data redundancy for efficient training of ML models to compressing large neural networks for real-time inference. In contrast to prior work that has predominantly focused on heuristics, the algorithms presented in this thesis can be widely applied to varying scenarios to obtain provably competitive results. We present empirical evaluations on real-world scenarios and data sets that demonstrate the practicality and effectiveness of the presented work.
Created by Cenk Baykal at Monday, July 26, 2021 at 4:37 PM.