Random Laplace Feature Maps for Semigroup Kernels on Histograms. - Jiyan Yang; Vikas Sindhwani; Quanfu Fan; Haim Avron; Michael W. Mahoney (CVPR, 2014)

Modeling and online control of generalized energy storage networks. - Junjie Qin; Yinlam Chow; Jiyan Yang; Ram Rajagopal (e-Energy, 2014)

Quantile Regression for Large-Scale Applications. - Jiyan Yang; Xiangrui Meng; Michael W. Mahoney (SIAM J. Sci. Comput., 2014)

Tensor machines for learning target-specific polynomial features. - Jiyan Yang; Alex Gittens (CoRR, 2015)

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (IEEE BigData, 2016)

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. - Alex Gittens; Jey Kottalam; Jiyan Yang; Michael F. Ringenburg; Jatin Chhugani; Evan Racah; Mohitdeep Singh; Yushu Yao; Curt Fischer; Oliver Rübel; Benjamin P. Bowen; Norman G. Lewis; et al. (IPDPS Workshops, 2016)

Sub-sampled Newton Methods with Non-uniform Sampling. - Peng Xu; Jiyan Yang; Farbod Roosta-Khorasani; Christopher Ré; Michael W. Mahoney (NIPS, 2016)

Feature-distributed sparse regression: a screen-and-clean approach. - Jiyan Yang; Michael W. Mahoney; Michael A. Saunders; Yuekai Sun (NIPS, 2016)

Weighted SGD for ℓp Regression with Randomized Preconditioning. - Jiyan Yang; Yinlam Chow; Christopher Ré; Michael W. Mahoney (SODA, 2016)

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (CoRR, 2016)

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. - Haim Avron; Vikas Sindhwani; Jiyan Yang; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. - Jiyan Yang; Xiangrui Meng; Michael W. Mahoney (Proc. IEEE, 2016)

Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty. - Junjie Qin; Yinlam Chow; Jiyan Yang; Ram Rajagopal (IEEE Trans. Smart Grid, 2016)

Weighted SGD for $ ell_p$ Regression with Randomized Preconditioning. - Jiyan Yang; Yin-Lam Chow; Christopher Ré; Michael W. Mahoney (J. Mach. Learn. Res., 2017)

A Study of BFLOAT16 for Deep Learning Training. - Dhiraj D. Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; et al. (CoRR, 2019)

Post-Training 4-bit Quantization on Embedding Tables. - Hui Guan 001; Andrey Malevich; Jiyan Yang; Jongsoo Park; Hector Yuen (CoRR, 2019)

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. - Hao-Jun Michael Shi; Dheevatsa Mudigere; Maxim Naumov; Jiyan Yang (KDD, 2020)

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. - Qingquan Song; Dehua Cheng; Hanning Zhou; Jiyan Yang; Yuandong Tian; Xia Hu 001 (KDD, 2020)

ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training. - Qinqing Zheng; Bor-Yiing Su; Jiyan Yang; Alisson G. Azzolini; Qiang Wu; Ou Jin; Shri Karandikar; Hagay Lupesko; Liang Xiong; Eric Zhou (CoRR, 2020)

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. - Maxim Naumov; John Kim; Dheevatsa Mudigere; Srinivas Sridharan 002; Xiaodong Wang 020; Whitney Zhao; Serhat Yilmaz; Changkyu Kim; Hector Yuen; Mustafa Ozdal; Krishnakumar Nair; Isabel Gao; et al. (CoRR, 2020)

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. - Qingquan Song; Dehua Cheng; Hanning Zhou; Jiyan Yang; Yuandong Tian; Xia Hu (CoRR, 2020)

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data. - Mao Ye 006; Dhruv Choudhary; Jiecao Yu; Ellie Wen; Zeliang Chen; Jiyan Yang; Jongsoo Park; Qiang Liu 001; Arun Kejariwal (CoRR, 2020)

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. - Kiwan Maeng; Shivam Bharuka; Isabel Gao; Mark C. Jeffrey; Vikram Saraph; Bor-Yiing Su; Caroline Trippel; Jiyan Yang; Mike Rabbat; Brandon Lucia; Carole-Jean Wu (CoRR, 2020)

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. - Antonio A. Ginart; Maxim Naumov; Dheevatsa Mudigere; Jiyan Yang; James Zou 001 (ISIT, 2021)

Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters. - Yuzhen Huang; Xiaohan Wei; Xing Wang; Jiyan Yang; Bor-Yiing Su; Shivam Bharuka; Dhruv Choudhary; Zewei Jiang; Hai Zheng; Jack Langman (KDD, 2021)

Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. - Kiwan Maeng; Shivam Bharuka; Isabel Gao; Mark C. Jeffrey; Vikram Saraph; Bor-Yiing Su; Caroline Trippel; Jiyan Yang; Mike Rabbat; Brandon Lucia; Carole-Jean Wu (MLSys, 2021)

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; Leon Gao; et al. (CoRR, 2021)

Software-hardware co-design for fast and scalable training of deep learning recommendation models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Zhihao Jia; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 2022)

DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction. - Buyun Zhang; Liang Luo; Xi Liu; Jay Li; Zeliang Chen; Weilin Zhang; Xiaohan Wei; Yuchen Hao; Michael Tsang; Wenjun Wang; Yang Liu; Huayu Li; et al. (CoRR, 2022)

Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking. - Xuewei Wang; Qiang Jin; Shengyu Huang; Min Zhang; Xi Liu; Zhengli Zhao; Yukun Chen; Zhengyu Zhang; Jiyan Yang; Ellie Wen; Sagar Chordia; Wenlin Chen; et al. (AdKDD@KDD, 2023)

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations. - Danwei Li; Zhengyu Zhang; Siyang Yuan; Mingze Gao; Weilin Zhang; Chaofei Yang; Xi Liu; Jiyan Yang (KDD, 2023)

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale. - Wei Wen; Kuang-Hung Liu; Igor Fedorov; Xin Zhang; Hang Yin; Weiwei Chu; Kaveh Hassani; Mengying Sun; Jiang Liu; Xu Wang; Lin Jiang; Yuxin Chen; et al. (CoRR, 2023)

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale. - Wei Wen; Kuang-Hung Liu; Igor Fedorov; Xin Zhang 054; Hang Yin; Weiwei Chu; Kaveh Hassani; Mengying Sun; Jiang Liu; Xu Wang; Lin Jiang; Yuxin Chen; et al. (WWW (Companion Volume), 2024)

AutoML for Large Capacity Modeling of Meta's Ranking Systems. - Hang Yin; Kuang-Hung Liu; Mengying Sun; Yuxin Chen; Buyun Zhang; Jiang Liu 003; Vivek Sehgal; Rudresh Rajnikant Panchal; Eugen Hotaj; Xi Liu; Daifeng Guo; Jamey Zhang; et al. (WWW (Companion Volume), 2024)