Prof. Cyrus Shahabi, Director of the Integrated Media Systems Center
(IMSC), made a significant contribution to this year’s International Conference on
Machine Learning (ICML), one of the premier conferences in the field of machine
learning. Prof. Shahabi delivered an oral presentation on his paper titled, “Theoretical
Analysis of Learned Database Operations under Distribution Shift through Distribution
Learnability.” (https://icml.cc/virtual/2024/poster/33068)

ICML 2024 was highly competitive, with 9,473 total submissions from researchers
around the globe. Of these, only 2,609 papers were accepted for publication, and a
mere 144 were selected for the coveted oral presentation slots, underscoring the high
regard for Prof. Shahabi’s work.

Prof. Shahabi’s presentation highlighted the work of his PhD student, Sepanta
Zeighami, now a postdoctoral researcher at UC Berkeley. Their research focuses on
using machine learning for database operations such as indexing, cardinality estimation,
and sorting, which have shown significant performance benefits. However, when
datasets change and data distribution shifts, learned models can suffer from
performance degradation, sometimes falling below non-learned alternatives. The paper
addresses this issue by presenting the first known theoretical framework that
characterizes the performance of learned models in dynamic datasets. Their analysis
introduces the concept of distribution learnability, offering theoretical bounds that explain
when and why learned models can outperform traditional methods, providing crucial
insights for their practical applicability in real-world systems. See below for the Viterbi’s
news on this work:
https://viterbischool.usc.edu/news/2024/07/usc-at-the-international-conferences-on-
machine-learning-icml/

The ICML conference, held annually, is a key platform for the dissemination and
discussion of groundbreaking research in machine learning. This year’s event, featuring
leading academics and industry experts, provided an opportunity for researchers to
share insights and advancements that will shape the future of the field.

Prof. Shahabi’s selection for an oral presentation at such a prestigious conference
highlights the importance and innovative nature of his research, further solidifying his
position as a leading figure in the machine learning community.