Scott Clark
where else to find them
no profile links on file
what they talk about
where they keep going back
- MLconf SF2 editions2014–2016
all 5 in the filters
on a stage with
- Bryce Meredigonce
- John Melas-Kyriazionce
- Will Murphyonce
who talks about the same things
- Nguyen Hong Quan6 talks
- Thomas Huijskens3 talks
- Alexandra Johnson5 talks
- Ramki Ramakrishna2 talks
- Pawel Skrzypek11 talks
- Thomas Wiecki7 talks
| title | conference | date |
|---|---|---|
| Exploring Tradeoffs in Experiment Management as part of AI Platforms | TWIMLcon: AI Platforms 2019 | 2019-10-01 |
Framework for deciding what to build versus buy for Experiment Management, applied to deployments across firms totaling $500B market cap and trading clients… | ||
| Startup Showcase | The AI Conference 2017 | 2017-06-02 |
Platform that maps materials data from PDFs and handbooks into searchable structured records and runs physics-aware ML and simulation to cut R&D cycles 2–5x… | ||
| Scott Clark, CEO & Co-Founder, SigOpt at MLconf Seattle 2017 | MLconf Seattle 2017 | 2017-05-19 |
Claims Bayesian optimization using a Gaussian process surrogate and expected improvement acquisition beats grid and random search on real deep learning tasks… | ||
| Using Bayesian Optimization to Tune Machine Learning Models | MLconf SF 2016 | 2016-11-11 |
Argues that Bayesian global optimization (Gaussian-process surrogate with expected improvement acquisition) finds better hyperparameters with far fewer costly… | ||
| DBTB INT Scott Clark r | Data By the Bay 2016 | 2016-05-16 |
Claims Bayesian optimization outperforms grid and random search for hyperparameter tuning, reducing experiment time and cost; reports applicability to neural… | ||
| Optimizing Machine Learning Models | Data By the Bay 2016 | 2016-05-16 |
Bayesian global optimization via SigOpt claims to find better hyperparameters far faster than grid or random search, trading exploration and exploitation using… | ||
| Scott Clark, Software Engineer, Yelp | MLconf SF 2014 | 2014-11-14 |
MOE is an open source Bayesian global optimizer that models an expensive black‑box objective with a Gaussian Process, fits its kernel hyperparameters… | ||
| nothing matches | ||