COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Optimal score estimation via empirical Bayes smoothing

## Optimal score estimation via empirical Bayes smoothingAdd to your list(s) Download to your calendar using vCal - Andre Wibisono (Yale University)
- Monday 15 July 2024, 16:00-17:00
- External.
If you have a question about this talk, please contact nobody. DMLW01 - International workshop on diffusions in machine learning: foundations, generative models, and optimisation We study the problem of estimating the score function of an unknown probability distribution $\rho ^{2(\rho}*)}$ that is commonly used in the score matching literature, highlighting the curse of dimensionality where sample complexity for accurate score estimation grows exponentially with the dimension $d$. Leveraging key insights in empirical Bayes theory as well as a new convergence rate of smoothed empirical distribution in Hellinger distance, we show that a regularized score estimator based on a Gaussian kernel attains this rate, shown optimal by a matching minimax lower bound. We also discuss the implication of our theory on the sample complexity of score-based generative models. Joint work with Yihong Wu and Kaylee Yang.
This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- External
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- bld31
Note that ex-directory lists are not shown. |
## Other listsCancer Biology 2016 Cambridge Centre for Political Thought Explore Islam Week 2014 (EIW)## Other talksRebricking frames and bases TBA Reinforced Galton-Watson processes. Welcome: EA Pro-Vice-Chancellor Research, Julian Blow Mixing time of random walk on dynamical random cluster Inference theme - Emulation-based inference for complex spatial infectious disease models |