1 citations · 1 across the 7 of their papers we have counts for
7 papers
FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift
Herlock Rahimi, Dionysis Kalogerias
Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic client participation. Remedies…
Constrained Learning with Universally Learnable Concept Classes
Herlock SeyedAbolfazl Rahimi, Spyridon Pougkakiotis, Dionysis Kalogerias
We study constrained statistical learning over infinite-dimensional hypothesis classes in the fully nonconvex setting, and establish universal PACC learnability of the solutions of…
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Amirhossein Zare, Amirhessam Zare, Herlock Rahimi +2
Longitudinal treatment decisions from multivariate time-series data require predicting potential outcomes under future treatment sequences in the presence of time-varying confoundi…
Weighted Stochastic Differential Equation to Implement Wasserstein-Fisher-Rao Gradient Flow
Herlock Rahimi
Score-based diffusion models currently constitute the state of the art in continuous generative modeling. These methods are typically formulated via overdamped or underdamped Ornst…
Uncertainty-Aware Generative Oversampling Using an Entropy-Guided Conditional Variational Autoencoder
Amirhossein Zare, Amirhessam Zare, Parmida Sadat Pezeshki +5
Class imbalance remains a major challenge in machine learning, especially for high-dimensional biomedical data where nonlinear manifold structures dominate. Traditional oversamplin…
FedAVOT: Exact Distribution Alignment in Federated Learning via Masked Optimal Transport
Herlock, Rahimi, Dionysis Kalogerias
Federated Learning (FL) allows distributed model training without sharing raw data, but suffers when client participation is partial. In practice, the distribution of available use…
Convergence of Agnostic Federated Averaging
Herlock, Rahimi, Dionysis Kalogerias
Federated learning (FL) enables decentralized model training without centralizing raw data. However, practical FL deployments often face a key realistic challenge: Clients particip…