T1.R3.2
Robust Classification under Noisy Labels: A Geometry-Aware Reliability Framework for Foundation Models
Ecem Bozkurt, Antonio Ortega, University of Southern California, United States
Session:
T1.R3: Machine Learning Lecture
Track:
Machine Learning and Artificial Intelligence
Location:
Isabela III
Presentation Time:
Tue, 16 Dec, 10:40 - 11:00 AST (UTC -4)
Session Co-Chairs:
Rene Vidal, University of Pennsylvania and Gonzalo Mateos, University of Rochester
Session T1.R3
T1.R3.1: SYMMETRIC RANK-ONE QUASI-NEWTON METHODS FOR DEEP LEARNING USING CUBIC REGULARIZATION
Aditya Ranganath, Lawrence Livermore National Laboratory, United States; Mukesh Singhal, Roummel Marcia, University of California, Merced, United States
T1.R3.2: Robust Classification under Noisy Labels: A Geometry-Aware Reliability Framework for Foundation Models
Ecem Bozkurt, Antonio Ortega, University of Southern California, United States
T1.R3.3: Toeplitz Covariance Estimation via Overparametrized Gradient Descent
Daniel Busbib, Ami Wiesel, The Hebrew University Of Jerusalem, Israel
T1.R3.4: Convergence of Agnostic Federated Averaging
Herlock (SeyedAbolfazl) Rahimi, Dionysis Kalogerias, Yale University, United States
Contacts