Animashree Anandkumar
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- Anima Anandkumar7 talks
- Anish Agarwal9 talks
- Ankur Moitra18 talks
- Shiva Amiri2 talks
- Abhiram Ravikumar2 talks
- Rahul Sharma7 talks
| title | conference | date |
|---|---|---|
| Foundations for AI+Science | PyTorch Conference 2025 | 2025-10-22 |
Introduces neural operators that map between continuous function spaces so models run at any resolution and learn from discretized samples, claims the 2021 AI… | ||
| Animashree Anandkumar, Electrical Engineering and CS Dept, UC Irvine @ MLconf SEA | MLconf Seattle 2015 | 2015-05-01 |
Tensor methods claim consistent parameter recovery for topic models, community/mixed‑membership models, HMMs and ICA by decomposing third-order moments instead… | ||
| Constructing Informative Features for Discriminative Learning | Information Theory, Learning and Big Data (Simons Institute, 2015) | 2015-03-16 |
Constructs a pipeline that estimates higher-order score functions from unlabeled X (via autoencoders or score matching), forms cross-moments E[y ⊗ score_k(X)]… | ||
| Tensor Methods for Learning Latent Variable Models: Theory and Practice | Spectral Algorithms: From Theory to Practice 2014 | 2014-10-27 |
Shows that method-of-moments using third-order tensor moments plus whitening via rank-k SVD gives consistent, polynomial-time recovery of latent-variable… | ||
| Robust PCA via Non-convex Methods: Provable Bounds | Semidefinite Optimization, Approximation and Applications 2014 | 2014-09-24 |
Claims a provable non‑convex algorithm that exactly recovers a low‑rank plus sparse decomposition under the same deterministic incoherence and sparsity regimes… | ||
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