Sampling-based Algorithms for Efficient and Scalable AI

Speaker: Cenk Baykal , MIT CSAIL

Date: Monday, August 02, 2021

Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: https://mit.zoom.us/j/99960735273?pwd=MGszaDVMSnp4WkI0WkYvYldSWitGdz09

Event Type: Thesis Defense

Room Description:

Host: Daniela Rus , MIT CSAIL

Contact: Cenk Baykal, baykal@csail.mit.edu

Relevant URL: https://mit.zoom.us/j/99960735273?pwd=MGszaDVMSnp4WkI0WkYvYldSWitGdz09

Speaker URL: None

Speaker Photo:
None

Reminders to: seminars@csail.mit.edu

Reminder Subject: TALK: Thesis Defense (Cenk Baykal): Sampling-based Algorithms for Efficient and Scalable AI

Thesis supervisor: Daniela Rus
Thesis committee: Piotr Indyk, Aleksander MÄ…dry

Abstract:

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.

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This event is not part of a series.

Created by Cenk Baykal Email at Monday, July 26, 2021 at 4:37 PM.