Timezone: »

 
Poster
Locality Sensitive Teaching
Zhaozhuo Xu · Beidi Chen · Chaojian Li · Weiyang Liu · Le Song · Yingyan Lin · Anshumali Shrivastava

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching (MT) algorithms for online personalized education on home devices. This direction becomes more promising during the COVID-19 pandemic when in-person education becomes infeasible. However, as one of the most influential and practical MT paradigms, iterative machine teaching (IMT) is prohibited on IoT devices due to its inefficient and unscalable algorithms. IMT is a paradigm where a teacher feeds examples iteratively and intelligently based on the learner's status. In each iteration, current IMT algorithms greedily traverse the whole training set to find an example for the learner, which is computationally expensive in practice. We propose a novel teaching framework, Locality Sensitive Teaching (LST), based on locality sensitive sampling, to overcome these challenges. LST has provable near-constant time complexity, which is exponentially better than the existing baseline. With at most 425.12x speedups and 99.76% energy savings over IMT, LST is the first algorithm that enables energy and time efficient machine teaching on IoT devices. Owing to LST's substantial efficiency and scalability, it is readily applicable in real-world education scenarios.

Keywords:

Author Information

Zhaozhuo Xu (Rice University)
Beidi Chen (Stanford University)

I'm a third year Ph.D. Student at Rice University and working with Dr. Anshumali Shrivastava. My research topic is hashing in large-scale learning. I work closely with Dr. Rebecca Steorts on Record Linkage. I had my undergrad in Berkeley and my Advisor was Randy Katz. My topic was data mining.

Chaojian Li (Rice University)
Weiyang Liu (University of Cambridge)
Le Song (Georgia Institute of Technology)
Yingyan Lin (Rice University)

The assistant professor working on energy-efficient machine learning systems

Anshumali Shrivastava (Rice University / ThirdAI Corp.)

More from the Same Authors