Berkeley's ADA Aligns Simulation-Trained Models to Noisy Real Experiments
anshulkundaje · x · 2026-11-06
A UC Berkeley team (Sergey Levine, Aditi Krishnapriyan et al.) presents Adversarial Distribution Alignment (ADA) at NeurIPS: a method that aligns generative models trained on cheap-but-flawed simulations (e.g., classical-force-field molecular dynamics) to full real-world distributions using only partial, noisy observations like cryo-EM images. Unlike approaches matching only observable expectations, ADA matches full distributions across correlated high-dimensional observations — demoed by steering an MD model's unfolded-state oversampling toward the experimentally observed folded state.
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