| Time | Speaker | Title |
|---|---|---|
| 9:00 | Cyrus Rashtchian (UCSD) | Opening Remarks |
| 9:10 | David Woodruff (CMU) | Strong Coresets for k-Median and Subspace Approximation, Goodbye Dimension |
| 9:40 | Suriya Gunasekar (TTIC) | Characterizing Implicit Bias in Terms of Optimization Geometry |
| 10:10 | Rebecca Willett (UChicago) | Low Algebraic Dimension Matrix Completion |
| 10:40 | Hanyu Zhang (UW) | From Non-parametric to Parametric: Manifold Coordinates with Physical Meaning |
| 11:10 | Coffee Break | |
| 11:30 | Samory Kpotufe (Columbia) | Kernel Sketching yields Kernel JL |
| 12:00 | Nina Balcan (CMU) | Data Driven Clustering |
| 12:20 | Organizers | Closing Remarks |
| 12:30 | Lunch |
Machine learning and data science applications heavily rely on geometric ideas and algorithms, such as clustering, metric embeddings, sketching, and nearest neighbor search. Unfortunately, classical computational models and worst-case analysis often fail to capture both the nuances of modern datasets and the overwhelming success of many methods in practice. Therefore, the time is ripe to focus on geometric algorithms with an eye towards relevant use cases. This workshop will introduce participants to both (i) the theoretical achievements in the area and (ii) the current and future applications of these ideas. Several excellent speakers will present their research, which should be of interest to anyone working in the union of theoretical computer science, machine learning, geometry, and statistics.
When
Sunday, June 23, 2019
9:00am - 12:30pm
Where
STOC 2019
Room: West 212C
