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This paper describes using a fine-tuned multimodal LLM based on Qwen3.5 to simulate student response probabilities and estimate item difficulty parameters for multiple-choice assessments, approximating 3PL and MCM curves.
This paper presents improved algorithms for learning truncated Boolean product distributions, achieving sample complexity that matches the untruncated minimax rate using the concept of influence from Boolean function analysis.
A tutorial overview of simulation-based inference (SBI) with machine learning, covering Bayesian and frequentist frameworks, methods like neural posterior estimation, and validation techniques.
This paper presents a Bayesian filtering approach to learn Lagrangian dynamics from partial, noisy measurements by parameterizing kinetic and potential energies with neural networks and jointly estimating states and parameters via maximum likelihood.
This paper introduces the degeneracy distillery, a method that automatically detects and resolves degenerate parameter combinations in physical models by estimating and flattening the Fisher information matrix, reducing the simulation budget required for neural posterior estimation while providing physical insight.
This paper diagnoses the loss landscape of gradient-based inversion for the Gray-Scott reaction-diffusion system, showing that direct backpropagation fails due to flat plateaus and sharp cliffs, while PINN components like residual loss smooth the landscape. The findings provide design implications for PINN-type methods.