learning algorithms
FDS Colloquium: Brice Huang (MIT), “Algorithmic thresholds in random optimization problems”
Abstract: Optimizing high-dimensional functions generated from random data is a central problem in modern statistics and machine learning. As these objectives are highly non-convex, […]
FDS Special Seminar: Orr Paradise (Berkeley), “Models that prove their own correctness”
Abstract: This talk introduces Self-Proving models, a new class of models that formally prove the correctness of their outputs via an Interactive Proof system. […]
FDS Colloquium: Bento Natura (Columbia), “Faster Exact Linear Programming”
Optional Zoom link: https://yale.zoom.us/j/99342713421 Abstract: We present a novel algorithm to solve various subclasses of linear programs, with a particular focus on strongly polynomial […]
FDS Colloquium: Quanquan C. Liu, “Massive Graph Algorithms from Theory to Practice and Back”
“Massive Graph Algorithms from Theory to Practice and Back” Abstract: In the face of massive graph data, there is increased interest in developing novel […]
S&DS Seminar: Johan Ugander (Stanford), “Harvesting randomness to understand computational social systems”
Speaker: Johan UganderAssociate Professor, Management Science & Engineering (MS&E)Institute for Computational & Mathematical Engineering (ICME)Cisco Systems Faculty Scholar, School of EngineeringStanford University Monday, March […]
FDS Colloquium: Lior Pachter (Caltech), “Some open and solved problems in dimensionality reduction (for single-cell genomics data)”
Speaker: Lior S. PachterBren Professor of Computational Biology and Computing Mathematical SciencesDivision of Biology and Biological EngineeringCalifornia Institute of Technology Wednesday, April 3, 2024Lunch: […]