3 citations · 8 across the 5 of their papers we have counts for
7 papers
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization
Nicolás García Trillos, Aditya Kumar Akash, Sixu Li +2
Adversarial attacks pose significant challenges in many machine learning applications, particularly in the setting of distributed training and federated learning, where malicious a…
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks
Aditya Kumar Akash, Sixu Li, Nicolás García Trillos
Based on the concepts of Wasserstein barycenter (WB) and Gromov-Wasserstein barycenter (GWB), we propose a unified mathematical framework for neural network (NN) model fusion and u…
Learning Invariant Representations using Inverse Contrastive Loss
Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi +1
Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach corresponds to the so-called information bottleneck principle i…
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret
Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N. Ravi +1
Algorithmic decision making based on computer vision and machine learning technologies continue to permeate our lives. But issues related to biases of these models and the extent t…
Stochastic Bandits with Delayed Composite Anonymous Feedback
Siddhant Garg, Aditya Kumar Akash
We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback…
Lower Bounds for Graph Exploration Using Local Policies
Aditya Kumar Akash, Sandor P. Fekete, Seoung Kyou Lee +3
We give lower bounds for various natural node- and edge-based local strategies for exploring a graph. We consider this problem both in the setting of an arbitrary graph as well as…