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Junming Yin

4 accepted papers

2026

When LLMs get significantly worse: A statistical approach to detect model degradations

ICLR 2026poster

Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and others without accuracy guarantees like quantization. In all of these cases it is crucial to ensure that the model quality has not…

Cited by 0SourcecodeScholar
2025

Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models

NeurIPS 2025poster

Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Without seeing any real-world data, models pretrained on purely synthetic datasets generalize remarkably well across diverse d…

Cited by 0SourceScholar
2020

Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models

UAI 2020poster

Recent advances in variational auto-encoder (VAE) have demonstrated the possibility of approximating the intractable posterior distribution with a variational distribution parameterized by a neural network. To optimize the variational objective of VAE, the reparameterization trick is commonly applie…