273 citations · 329 across the 7 of their papers we have counts for
8 papers
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Yuanzhi Li, Yingyu Liang
Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-laye…
Learning Mixtures of Linear Regressions with Nearly Optimal Complexity
Yuanzhi Li, Yingyu Liang
Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regre…
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations
Yuanzhi Li, Yingyu Liang
Non-negative matrix factorization is a basic tool for decomposing data into the feature and weight matrices under non-negativity constraints, and in practice is often solved in the…
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Non-negative matrix factorization is a popular tool for decomposing data into feature and weight matrices under non-negativity constraints. It enjoys practical success but is poorl…
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
Yuanzhi Li, Andrej Risteski
The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has bee…
Recovery guarantee of weighted low-rank approximation via alternating minimization
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated as a weighted low-rank approxi…
Linear Algebraic Structure of Word Senses, with Applications to Polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang +2
Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses resid…
A Latent Variable Model Approach to PMI-based Word Embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang +2
Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-…