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no code implementations • 12 Sep 2022 • Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I. Shamir, Rakesh Shivanna, Qiqi Yan

For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem.

no code implementations • 2 Feb 2022 • Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy, Sagar Jain, Ed H. Chi

We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices.

9 code implementations • 19 Aug 2020 • Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng, Sagar Jain, Dong Lin, Lichan Hong, Ed H. Chi

Learning effective feature crosses is the key behind building recommender systems.

Ranked #3 on Click-Through Rate Prediction on Criteo

no code implementations • 10 Feb 2020 • Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, Sagar Jain

Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.

no code implementations • 6 Nov 2018 • Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya

Our proposed algorithm, {\it Pref-Rank}, predicts the underlying ranking using an SVM based approach over the chosen embedding of the product graph, and is the first to provide \emph{statistical consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's footrule}, with a required sample complexity of $O(n^2 \chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic number} of the complement graph $\bar{G}$.

no code implementations • NeurIPS 2015 • Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach

We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov\'{a}sz~$\vartheta$ function.

no code implementations • NeurIPS 2014 • Rakesh Shivanna, Chiranjib Bhattacharyya

This, for the first time, relates labelled sample complexity to graph connectivity properties, such as the density of graphs.

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