Adaptive Second Order Coresets for Data-efficient Machine Learning. - Omead Pooladzandi; David Davini; Baharan Mirzasoleiman (ICML, 2022)

De-Biasing Generative Models using Counterfactual Methods. - Sunay Bhat; Jeffrey Jiang; Omead Pooladzandi; Gregory J. Pottie (CoRR, 2022)

Hypothesis Testing using Causal and Causal Variational Generative Models. - Jeffrey Jiang; Omead Pooladzandi; Sunay Bhat; Gregory J. Pottie (CoRR, 2022)

Improving Levenberg-Marquardt Algorithm for Neural Networks. - Omead Pooladzandi; Yiming Zhou (CoRR, 2022)

Exploring the Potential of VAE Decoders for Enhanced Speech Re-Synthesis. - Omead Pooladzandi; Xilin Li; Yang Gao; Lalin Theverapperuma (SSP, 2023)

Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization. - Omead Pooladzandi; Pasha Khosravi; Erik Nijkamp; Baharan Mirzasoleiman (CoRR, 2023)

Towards Composable Distributions of Latent Space Augmentations. - Omead Pooladzandi; Jeffrey Jiang; Sunay Bhat; Gregory J. Pottie (CoRR, 2023)

Fast Training of Generalizable Deep Neural Networks - Omead Pooladzandi (2023)

PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics. - Omead Pooladzandi; Sunay Bhat; Jeffrey Jiang; Alexander Branch; Gregory J. Pottie (NeurIPS, 2024)

Curvature-Informed SGD via General Purpose Lie-Group Preconditioners. - Omead Pooladzandi; Xilin Li (CoRR, 2024)

PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based Models. - Omead Pooladzandi; Jeffrey Jiang; Sunay Bhat; Gregory J. Pottie (CoRR, 2024)

Diverse Imitation Learning via Self-Organizing Generative Models. - Arash Vahabpour; Tianyi Wang; Qiujing Lu; Omead Pooladzandi; Vwani Roychowdhury (IEEE Trans. Neural Networks Learn. Syst., 2025)