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This paper proves the Ho–Nguyen conjecture on the order of density cancellation in over-fitted Gaussian mixtures for all cluster sizes M≥2, deriving exact maximum-likelihood exponents and local minimax rates, and applies the coefficient framework to identifiability issues in mixtures of experts.
The paper proposes that intransitive preferences in language models arise from multiple latent orderings and introduces a mixture Bradley–Terry model to analyze them, showing better explanatory power across various models and tasks.
This paper proposes MoPLEx, an algorithm for learning mixtures of Plackett-Luce models to handle heterogeneous preferences in AI alignment, showing improved clustering and ranking accuracy over baselines.
This paper proposes modeling the CLIP latent space using Mixtures of von Mises–Fisher distributions on the unit hypersphere, capturing its directional and multimodal structure better than Gaussian assumptions. The model improves long-tailed and out-of-distribution detection and provides a semantic decomposition of CLIP embeddings.
This paper introduces MMOT, an online mixture model learning framework based on optimal transport theory that addresses incremental learning with distributional shifts through dynamic centroid updates and improved class similarity estimation. The approach includes a Dynamic Preservation strategy to mitigate catastrophic forgetting and maintain class separability in latent space.