Quick Overview: Authors: P. Kothari, A. Moitra, A. Wein FOCS 2025 - session 7C. TENORS Workshop 1, 22 - 26 September 2025, University of Konstanz. David Steurer, Cornell University Random Instances and Phase Transitions ...

Overcomplete Tensor Decomposition Via Koszul - Detailed Overview & Context

Authors: P. Kothari, A. Moitra, A. Wein FOCS 2025 - session 7C. TENORS Workshop 1, 22 - 26 September 2025, University of Konstanz. David Steurer, Cornell University Random Instances and Phase Transitions ... Sum-Of-Squares Algorithms For Over-Complete Tensor Decomposition Sham Kakade, Microsoft Research New England The Data Science Institute (DSI) hosted a seminar by Joyce Ho from Emory University on July 28, 2023. Read more about the DSI ...

by Miao Yin You can visit the Workshop's webpage here: . Short talks by postdoctoral members Topic: Analysis and design of convolutional networks Jeremy Charlier (university of Luxembourg) and Vladimir Makarenkov (UQAM). In this talk, we present a framework for causal inference for the “panel” or “longitudinal” setting from the lens of WEB: This lecture focuses implements an N-way Moses Charikar, Princeton University Semidefinite Optimization, Approximation and Applications ...

Latent or hidden variable models have applications in almost every domain, e.g., social network analysis, natural language ... Aditya Bhaskara, Google Research New York

Photo Gallery

Overcomplete Tensor Decomposition via Koszul-Young Flattenings
Pascal Koiran   An efficient uniqueness theorem for overcomplete tensor decomposition
Algorithms for Overcomplete Tensor Decomposition - Pravesh Kothari
Qing Qu: "Landscape Analysis for Overcomplete Tensor and Neural Collapse"
Average-Case Overcomplete Tensor Decomposition
Sum-Of-Squares Algorithms For Over-Complete Tensor Decomposition
Lec11: Tensor Decomposition via SoS
Tensor Decompositions for Estimating Latent Variable Models
Tensor Decompositions: A Quick Tour of Illustrative Applications
DSI | Tensor Factorization for Biomedical Representation Learning
Tensor Decompositions for Multi-Aspect Graph Analytics and Beyond- Evangelos (Vagelis) Papalexakis
spotlight 6: Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
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