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Adhi Saravanan

2 accepted papers

2026

Meta Flow Maps enable scalable reward alignment

ICML 2026poster

Controlling generative models—whether via inference-time steering or fine-tuning—is expensive. Control relies on estimating the value function—typically necessitating costly trajectory simulations. To eliminate this bottleneck, we introduce *Meta Flow Maps (MFMs)*, stochastic extensions of consisten…

Cited by 0SourceScholar
2026

Scaling Bayesian Experimental Design to High-Dimensions with Information-Guided Diffusion

ICLR 2026poster

We present DiffBED, a Bayesian experimental design (BED) approach that scales to problems with high-dimensional design spaces. Our key insight is that current BED approaches typically cannot be scaled to real high--dimensional design problems because of the need to specify a likelihood model that re…

Cited by 0SourceScholar