huangdaolang.bsky.social
PhD student at Aalto University 🇫🇮
Probabilistic ML, amortized inference.
See more at huangdaolang.com
9 posts
189 followers
250 following
Getting Started
comment in response to
post
Great list! Can I join?
comment in response to
post
8/ Join us at our poster session at #NeurIPS2024. Unfortunately this year I can’t attend in person, but @lacerbi.bsky.social will present our work. We are excited to discuss and explore future directions in Bayesian experimental design and amortization!
comment in response to
post
7/ Experiments show TNDP significantly outperforms traditional methods across various tasks, including targeted active learning and hyperparameter optimization, retrosynthesis planning.
comment in response to
post
6/ Our Transformer Neural Decision Process (TNDP) unifies experimental design and decision-making in a single framework, allowing instant design proposals while maintaining high decision quality.
comment in response to
post
5/ We introduce Decision Utility Gain (DUG) to guide experimental design with a direct focus on optimizing decision-making tasks, moving beyond traditional information-theoretic objectives. DUG measures the improvement in the maximum expected utility from observing a new experimental design.
comment in response to
post
4/ In our work, we present a new amortized BED framework that optimizes experiments directly for downstream decision-making.
comment in response to
post
3/ But what if our goal goes beyond parameter inference? In many real-world tasks like medical diagnosis, we care more about making the right decisions than learning model parameters.
comment in response to
post
2/ Bayesian Experimental Design (BED) is a powerful framework to optimize experiments aimed at reducing uncertainty about unknown system parameters. Recent amortized BED methods use pre-trained neural networks for instant design proposals.