MLSP-48.3
EXACT PARTITIONING OF HIGH-ORDER PLANTED MODELS WITH A TENSOR NUCLEAR NORM CONSTRAINT
Chuyang Ke, Jean Honorio, Purdue University, United States of America
Session:
Learning Theory and Algorithms II
Track:
Machine Learning for Signal Processing
Location:
Gather Area G
Presentation Time:
Fri, 13 May, 20:00 - 20:45 China Time (UTC +8)
Fri, 13 May, 12:00 - 12:45 UTC
Fri, 13 May, 12:00 - 12:45 UTC
Session Chair:
Tommy Alstrøm, Technical University of Denmark
Session MLSP-48
MLSP-48.1: CASCADING BANDIT UNDER DIFFERENTIAL PRIVACY
Kun Wang, Shuai Li, Shanghai Jiao Tong University, China; Jing Dong, University of Michigan, China; Baoxiang Wang, The Chinese University of Hong Kong, Shenzhen, China
MLSP-48.2: Iterative Re-weighted Least Squares Algorithms for Non-negative Sparse and Group-sparse Recovery
Angshul Majumdar, Indraprastha Institute of Information Technology, India
MLSP-48.3: EXACT PARTITIONING OF HIGH-ORDER PLANTED MODELS WITH A TENSOR NUCLEAR NORM CONSTRAINT
Chuyang Ke, Jean Honorio, Purdue University, United States of America
MLSP-48.4: No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds
Ahmed Imtiaz Humayun, Anastasios Kyrillidis, Richard Baraniuk, Rice University, United States of America; Randall Balestriero, Meta AI Research, United States of America
MLSP-48.5: DEEP KERNEL LEARNING NETWORKS WITH MULTIPLE LEARNING PATHS
Ping Xu, Yue Wang, Xiang Chen, Zhi Tian, George Mason University, United States of America
MLSP-48.6: PROVABLE SAMPLE COMPLEXITY GUARANTEES FOR LEARNING OF CONTINUOUS-ACTION GRAPHICAL GAMES WITH NONPARAMETRIC UTILITIES
Adarsh Barik, Jean Honorio, Purdue University, United States of America