58 citations · 103 across the 10 of their papers we have counts for
7 papers · 1 filter
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
Davoud Ataee Tarzanagh, Mingchen Li, Pranay Sharma +1
Stochastic approximation with multiple coupled sequences (MSA) has found broad applications in machine learning as it encompasses a rich class of problems including bilevel optimiz…
Model Sparsity Can Simplify Machine Unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram +5
In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Altho…
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Yihua Zhang, Pranay Sharma, Parikshit Ram +3
Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for…
Federated Minimax Optimization with Client Heterogeneity
Pranay Sharma, Rohan Panda, Gauri Joshi
Minimax optimization has seen a surge in interest with the advent of modern applications such as GANs, and it is inherently more challenging than simple minimization. The difficult…
On the Convergence of Federated Averaging with Cyclic Client Participation
Yae Jee Cho, Pranay Sharma, Gauri Joshi +3
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full cl…
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti +1
Data-heterogeneous federated learning (FL) systems suffer from two significant sources of convergence error: 1) client drift error caused by performing multiple local optimization…
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning
Sajad Khodadadian, Pranay Sharma, Gauri Joshi +1
Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling observations from the environment is usually split across multiple agents. However, tra…