activity
20182021
most citedOn the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization

5 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2021

DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation

Anish Acharya, Rudrajit Das

In this paper we study test time decoding; an ubiquitous step in almost all sequential text generation task spanning across a wide array of natural language processing (NLP) proble…

stat.ML2020

Faster Non-Convex Federated Learning via Global and Local Momentum

Rudrajit Das, Anish Acharya, Abolfazl Hashemi +3

We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of to converge to an -stationary point (i.e., $\math…

cs.LG20205 cited

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization

Abolfazl Hashemi, Anish Acharya, Rudrajit Das +3

In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e. averaging over the network) steps. M…

cs.LG2019

On the Separability of Classes with the Cross-Entropy Loss Function

Rudrajit Das, Subhasis Chaudhuri

In this paper, we focus on the separability of classes with the cross-entropy loss function for classification problems by theoretically analyzing the intra-class distance and inte…

cs.LG2018

Sparse Kernel PCA for Outlier Detection

Rudrajit Das, Aditya Golatkar, Suyash P. Awate

In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a…