21 citations · 24 across the 11 of their papers we have counts for
9 papers · 1 filter
Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learning
Pengfei Li, Mohammad Khalil
Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these updates uniformly across all lay…
PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis
Pengfei Li, QinYi Liu, Mohammad Khalil
Pretrained tabular generators can reproduce training records even when aggregate utility remains high. When retraining is unavailable or too costly, sampling and release are the re…
MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation
Pengfei Li, Mohammad Khalil
This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint distribution in the original he…
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sam Urmian, Qinyi Liu, Mohammad Khalil
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through se…
Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
Pengfei Li, Mohammad Khalil
While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundament…
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
Qinyi Liu, Mohammad Khalil, Naman Goel
Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal…
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Mohammad Khalil, Ronas Shakya, Qinyi Liu
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. Whil…
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
Mohammad Khalil, Sam Urmian, Ronas Shakya +1
In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for gene…
Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms
Qinyi Liu, Oscar Deho, Sam Urmian +3
The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purp…