26 citations · 61 across the 13 of their papers we have counts for
13 papers
Regularized Operator Extrapolation Method For Stochastic Bilevel Variational Inequality Problems
Mohammad Khalafi, Digvijay Boob
The bilevel variational inequality (BVI) problem is a general model that captures various optimization problems, including VI-constrained optimization and equilibrium problems with…
Optimal Primal-Dual Algorithm with Last iterate Convergence Guarantees for Stochastic Convex Optimization Problems
Digvijay Boob, Mohammad Khalafi
This paper proposes a novel first-order algorithm that solves composite nonsmooth and stochastic convex optimization problem with function constraints. Most of the works in the lit…
First-order methods for Stochastic Variational Inequality problems with Function Constraints
Digvijay Boob, Qi Deng, Mohammad Khalafi
The monotone Variational Inequality (VI) is a general model with important applications in various engineering and scientific domains. In numerous instances, the VI problems are ac…
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems
Mohammad Khalafi, Digvijay Boob
We investigate a primal-dual (PD) method for the saddle point problem (SPP) that uses a linear approximation of the primal function instead of the standard proximal step, resulting…
Level Constrained First Order Methods for Function Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan
We present a new feasible proximal gradient method for constrained optimization where both the objective and constraint functions are given by the summation of a smooth, possibly n…
Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problems
Digvijay Boob, Cristóbal Guzmán
In this work, we conduct the first systematic study of stochastic variational inequality (SVI) and stochastic saddle point (SSP) problems under the constraint of differential priva…
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…
Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan
Functional constrained optimization is becoming more and more important in machine learning and operations research. Such problems have potential applications in risk-averse machin…
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…