26 citations · 30 across the 3 of their papers we have counts for
6 papers
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan +1
Nonconvex sparse models have received significant attention in high-dimensional machine learning. In this paper, we study a new model consisting of a general convex or nonconvex ob…
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob +2
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversari…
Flowless: Extracting Densest Subgraphs Without Flow Computations
Digvijay Boob, Yu Gao, Richard Peng +4
We propose a simple and computationally efficient method for dense subgraph discovery, which is a classic problem both in theory and in practice. It is well known that dense subgra…
Faster width-dependent algorithm for mixed packing and covering LPs
Digvijay Boob, Saurabh Sawlani, Di Wang
In this paper, we give a faster width-dependent algorithm for mixed packing-covering LPs. Mixed packing-covering LPs are fundamental to combinatorial optimization in computer scien…
Complexity of Training ReLU Neural Network
Digvijay Boob, Santanu S. Dey, Guanghui Lan
In this paper, we explore some basic questions on the complexity of training neural networks with ReLU activation function. We show that it is NP-hard to train a two-hidden layer f…
Theoretical properties of the global optimizer of two layer neural network
Digvijay Boob, Guanghui Lan
In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number o…