Neural Architecture Search for CTR Prediction

Ravi Krishna joins us today to talk about his recent work on a differentiable NAS framework for ads CTR prediction. He discussed what CTR prediction is about and why his NAS framework helps in building neural networks for better ads recommendation. Listen to learn about methodology, related literature and his results.

[Click for additional show notes](https://dataskeptic.com/blog/episodes/2022/neural-architecture-search-for-ctr-prediction)

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Guest

Ravi Krishna: Bio: Ravi Krishna graduated with a B.S. in EECS from UC Berkeley at the age of 19 after being admitted with a Regents' and Chancellor's Scholarship out of 10th grade at age 16. While at Berkeley he worked in the Berkeley AI Research lab on large-scale deep recommender systems, and especially on the optimization of their architecture with neural architecture search. He has also worked at Facebook on the improvement of such deep recommenders' accuracy and efficiency for the critical problem of ad click-through-rate prediction.