Kexin Rong

According to our database, Kexin Rong authored at least 18 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Falcon: Fair Active Learning using Multi-armed Bandits.

Ki Hyun Tae, Hantian Zhang, Jaeyoung Park, Kexin Rong, Steven Euijong Whang

Proc. VLDB Endow., January, 2024

Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native.

Yao Lu, Song Bian, Lequn Chen, Yongjun He, Yulong Hui, Matthew Lentz, Beibin Li, Fei Liu, Jialin Li, Qi Liu, Rui Liu, Xiaoxuan Liu, Lin Ma, Kexin Rong, Jianguo Wang, Yingjun Wu, Yongji Wu, Huanchen Zhang, Minjia Zhang, Qizhen Zhang, Tianyi Zhou, Danyang Zhuo

CoRR, 2024

2023

Interactive Demonstration of EVA.

Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj

Proc. VLDB Endow., 2023

Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques.

Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai

Proc. VLDB Endow., 2023

DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data.

Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong

Proc. ACM Manag. Data, 2023

Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data.

Renzhi Wu, Jingfan Meng, Jie Jeff Xu, Huayi Wang, Kexin Rong

CoRR, 2023

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization.

Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov

CoRR, 2023

2021

Improving computational and human efficiency in large-scale data analytics.

Kexin Rong

PhD thesis, 2021

2020

Approximate Partition Selection for Big-Data Workloads using Summary Statistics.

Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Alexander Levis

Proc. VLDB Endow., 2020

2019

CrossTrainer: Practical Domain Adaptation with Loss Reweighting.

Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis

Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

Rehashing Kernel Evaluation in High Dimensions.

Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

MacroBase: Prioritizing Attention in Fast Data.

Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri

ACM Trans. Database Syst., 2018

Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science.

Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Alexander Levis, Gregory C. Beroza

Proc. VLDB Endow., 2018

2017

ASAP: Prioritizing Attention via Time Series Smoothing.

Kexin Rong, Peter Bailis

Proc. VLDB Endow., 2017

ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization.

Kexin Rong, Peter Bailis

CoRR, 2017

Demonstration: MacroBase, A Fast Data Analysis Engine.

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

MacroBase: Prioritizing Attention in Fast Data.

Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Prioritizing Attention in Analytic Monitoring.

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri

Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017