Sanjeev Arora

Publications376
h-index72
Citations25,700
Highly Influential Citations2,331
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A Simple but Tough-to-Beat Baseline for Sentence Embeddings
On Exact Computation with an Infinitely Wide Neural Net
TLDR
The current paper gives the first efficient exact algorithm for computing the extension of NTK to convolutional neural nets, which it is called Convolutional NTK (CNTK), as well as an efficient GPU implementation of this algorithm.
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
TLDR
This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: a tighter characterization of training speed, an explanation for why training a neuralNet with random labels leads to slower training, and a data-dependent complexity measure.
Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
TLDR
The previous best approximation algorithm for the problem (due to Christofides) achieves a 3/2-aproximation in polynomial time.
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications
TLDR
A simple meta-algorithm is presented that unifies many of these disparate algorithms and derives them as simple instantiations of the meta-Algorithm.
Stronger generalization bounds for deep nets via a compression approach
TLDR
These results provide some theoretical justification for widespread empirical success in compressing deep nets and show generalization bounds that're orders of magnitude better in practice.
A Practical Algorithm for Topic Modeling with Provable Guarantees
TLDR
This paper presents an algorithm for topic model inference that is both provable and practical and produces results comparable to the best MCMC implementations while running orders of magnitude faster.
Proof verification and hardness of approximation problems
TLDR
The authors improve on their result by showing that NP=PCP(logn, 1), which has the following consequences: (1) MAXSNP-hard problems do not have polynomial time approximation schemes unless P=NP; and (2) for some epsilon >0 the size of the maximal clique in a graph cannot be approximated within a factor of n/sup ePSilon / unless P =NP.
Proof verification and the hardness of approximation problems
TLDR
It is proved that no MAX SNP-hard problem has a polynomial time approximation scheme, unless NP = P, and there exists a positive ε such that approximating the maximum clique size in an N-vertex graph to within a factor of Nε is NP-hard.
Learning Topic Models -- Going beyond SVD
TLDR
This paper formally justifies Nonnegative Matrix Factorization (NMF) as a main tool in this context, which is an analog of SVD where all vectors are nonnegative, and gives the first polynomial-time algorithm for learning topic models without the above two limitations.
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