Aravindan Vijayaraghavan | DeepAI

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∙ 08/20/2023

Higher-Order Cheeger Inequality for Partitioning with Buffers

We prove a new generalization of the higher-order Cheeger inequality for...

0 Konstantin Makarychev, et al. ∙

∙ 12/07/2022

Computing linear sections of varieties: quantum entanglement, tensor decompositions and beyond

We study the problem of finding elements in the intersection of an arbit...

0 Nathaniel Johnston, et al. ∙

∙ 11/22/2022

The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound

The most widely used technique for solving large-scale semidefinite prog...

0 Liam O'Carroll, et al. ∙

∙ 09/06/2022

Classification Protocols with Minimal Disclosure

We consider multi-party protocols for classification that are motivated ...

0 Jinshuo Dong, et al. ∙

∙ 08/04/2022

Agnostic Learning of General ReLU Activation Using Gradient Descent

We provide a convergence analysis of gradient descent for the problem of...

0 Pranjal Awasthi, et al. ∙

∙ 06/06/2022

Training Subset Selection for Weak Supervision

Existing weak supervision approaches use all the data covered by weak si...

0 Hunter Lang, et al. ∙

∙ 07/21/2021

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations

We present polynomial time and sample efficient algorithms for learning ...

0 Pranjal Awasthi, et al. ∙

∙ 02/26/2021

Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances

Several works have shown that perturbation stable instances of the MAP i...

0 Hunter Lang, et al. ∙

∙ 11/07/2020

Graph cuts always find a global optimum (with a catch)

We prove that the alpha-expansion algorithm for MAP inference always ret...

0 Hunter Lang, et al. ∙

∙ 10/06/2020

Learning a mixture of two subspaces over finite fields

We study the problem of learning a mixture of two subspaces over 𝔽_2^n. ...

0 Aidao Chen, et al. ∙

∙ 07/30/2020

Efficient Tensor Decomposition

This chapter studies the problem of decomposing a tensor into a sum of c...

0 Aravindan Vijayaraghavan, et al. ∙

∙ 05/31/2020

Estimating Principal Components under Adversarial Perturbations

Robustness is a key requirement for widespread deployment of machine lea...

0 Pranjal Awasthi, et al. ∙

∙ 04/22/2020

Scheduling Precedence-Constrained Jobs on Related Machines with Communication Delay

We consider the problem of scheduling n precedence-constrained jobs on m...

0 Biswaroop Maiti, et al. ∙

∙ 11/29/2019

Adversarially Robust Low Dimensional Representations

Adversarial or test time robustness measures the susceptibility of a mac...

0 Pranjal Awasthi, et al. ∙

∙ 11/12/2019

On Robustness to Adversarial Examples and Polynomial Optimization

We study the design of computationally efficient algorithms with provabl...

16 Pranjal Awasthi, et al. ∙

∙ 11/29/2018

Smoothed Analysis in Unsupervised Learning via Decoupling

Smoothed analysis is a powerful paradigm in overcoming worst-case intrac...

12 Aditya Bhaskara, et al. ∙

∙ 10/12/2018

Block Stability for MAP Inference

To understand the empirical success of approximate MAP inference, recent...

0 Hunter Lang, et al. ∙

∙ 04/23/2018

Towards Learning Sparsely Used Dictionaries with Arbitrary Supports

Dictionary learning is a popular approach for inferring a hidden basis o...

0 Pranjal Awasthi, et al. ∙

∙ 12/04/2017

Clustering Stable Instances of Euclidean k-means

The Euclidean k-means problem is arguably the most widely-studied cluste...

0 Abhratanu Dutta, et al. ∙

∙ 11/23/2017

Clustering Semi-Random Mixtures of Gaussians

Gaussian mixture models (GMM) are the most widely used statistical model...

0 Pranjal Awasthi, et al. ∙

∙ 11/06/2017

Alpha-expansion is Exact on Stable Instances

Approximate algorithms for structured prediction problems---such as the ...

0 Hunter Lang, et al. ∙

∙ 10/31/2017

On Learning Mixtures of Well-Separated Gaussians

We consider the problem of efficiently learning mixtures of a large numb...

0 Oded Regev, et al. ∙

∙ 11/14/2013

Smoothed Analysis of Tensor Decompositions

Low rank tensor decompositions are a powerful tool for learning generati...

0 Aditya Bhaskara, et al. ∙

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