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Mohammad Mohammadi Amiri

7 accepted papers

2026

Curated Synthetic Data Doesn’t Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences

ICML 2026poster

Recursive retraining of generative models poses a critical representation challenge: when synthetic outputs are curated based on a fixed reward signal, the model tends to collapse onto a narrow set of outputs that over-optimize that objective, causing diversity to vanish and failing to represent the…

Cited by 0SourceScholar
2026

The Alignment Game: A Theory of Long-Horizon Alignment Through Recursive Curation

AAAI 2026technical

In self-consuming generative models that train on their own outputs, alignment with user preferences becomes a recursive rather than one-time process. In this paper, we provide the first formal foundation for analyzing the long-term effects of such recursive retraining on alignment. Under a two-stag

Cited by 0SourcePDFScholar
2025

Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching

NeurIPS 2025poster

We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generative modeling framework that learns velocity fields between probability distributions and has shown strong performance co…

Cited by 0SourcecodeScholar
2024

Incentive-Aware Federated Learning with Training-Time Model Rewards

ICLR 2024poster

In federated learning (FL), incentivizing contributions of training resources (e.g., data, compute) from potentially competitive clients is crucial. Existing incentive mechanisms often distribute post-training monetary rewards, which suffer from practical challenges of timeliness and feasibility of…

Cited by 4SourcePDFScholar
2019

Computation Scheduling for Distributed Machine Learning with Straggling Workers

ICASSP 2019accepted

We study scheduling of computation tasks across n workers in a large scale distributed learning problem. Computation speeds of the workers are assumed to be heterogeneous and unknown to the master, and redundant computations are assigned to the workers in order to tolerate straggling workers. We con…

Cited by 0SourceScholar