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2020 – today
- 2022
[j4]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Making Learned Query Optimization Practical. SIGMOD Rec. 51(1): 6-13 (2022)- 2021
- [j3]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. Proc. VLDB Endow. 14(11): 2019-2032 (2021) - [c15]Ryan Marcus
, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Making Learned Query Optimization Practical. SIGMOD Conference 2021: 1275-1288 - [i10]Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Flow-Loss: Learning Cardinality Estimates That Matter. CoRR abs/2101.04964 (2021) - 2020
- [c14]Parimarjan Negi, Ryan Marcus, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh:
Cost-Guided Cardinality Estimation: Focus Where it Matters. ICDE Workshops 2020: 154-157 - [c13]Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy:
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. NeurIPS 2020 - [c12]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned garbage collection. MAPL@PLDI 2020: 38-44 - [c11]Zili Meng, Minhu Wang, Jiasong Bai, Mingwei Xu, Hongzi Mao, Hongxin Hu:
Interpreting Deep Learning-Based Networking Systems. SIGCOMM 2020: 154-171 - [i9]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska:
Bao: Learning to Steer Query Optimizers. CoRR abs/2004.03814 (2020) - [i8]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned Garbage Collection. CoRR abs/2004.13301 (2020) - [i7]Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy:
Real-world Video Adaptation with Reinforcement Learning. CoRR abs/2008.12858 (2020) - [i6]Hongzi Mao, Chenjie Gu, Miaosen Wang, Angie Chen, Nevena Lazic, Nir Levine, Derek Pang, Rene Claus, Marisabel Hechtman, Ching-Han Chiang, Cheng Chen, Jingning Han:
Neural Rate Control for Video Encoding using Imitation Learning. CoRR abs/2012.05339 (2020)
2010 – 2019
- 2019
- [j2]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul:
Neo: A Learned Query Optimizer. Proc. VLDB Endow. 12(11): 1705-1718 (2019) - [c10]Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan:
SageDB: A Learned Database System. CIDR 2019 - [c9]Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh:
Variance Reduction for Reinforcement Learning in Input-Driven Environments. ICLR (Poster) 2019 - [c8]Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh:
Park: An Open Platform for Learning-Augmented Computer Systems. NeurIPS 2019: 2490-2502 - [c7]Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh:
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. NeurIPS 2019: 3983-3993 - [c6]Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh:
Learning scheduling algorithms for data processing clusters. SIGCOMM 2019: 270-288 - [i5]Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul:
Neo: A Learned Query Optimizer. CoRR abs/1904.03711 (2019) - [i4]Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh:
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. CoRR abs/1906.08879 (2019) - [i3]Zili Meng, Minhu Wang, Mingwei Xu, Hongzi Mao, Jiasong Bai, Hongxin Hu:
Explaining Deep Learning-Based Networked Systems. CoRR abs/1910.03835 (2019) - 2018
- [i2]Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh:
Variance Reduction for Reinforcement Learning in Input-Driven Environments. CoRR abs/1807.02264 (2018) - [i1]Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh:
Learning Scheduling Algorithms for Data Processing Clusters. CoRR abs/1810.01963 (2018) - 2017
- [c5]Hongzi Mao, Ravi Netravali, Mohammad Alizadeh:
Neural Adaptive Video Streaming with Pensieve. SIGCOMM 2017: 197-210 - 2016
- [c4]Hongzi Mao, Mohammad Alizadeh, Ishai Menache, Srikanth Kandula:
Resource Management with Deep Reinforcement Learning. HotNets 2016: 50-56 - [c3]Kanthi Nagaraj, Dinesh Bharadia, Hongzi Mao, Sandeep Chinchali, Mohammad Alizadeh, Sachin Katti:
NUMFabric: Fast and Flexible Bandwidth Allocation in Datacenters. SIGCOMM 2016: 188-201 - 2015
- [j1]Fadel Adib, Chen-Yu Hsu, Hongzi Mao, Dina Katabi, Frédo Durand:
Capturing the human figure through a wall. ACM Trans. Graph. 34(6): 219:1-219:13 (2015) - [c2]Fadel Adib, Hongzi Mao, Zachary Kabelac, Dina Katabi, Robert C. Miller:
Smart Homes that Monitor Breathing and Heart Rate. CHI 2015: 837-846 - 2014
- [c1]Fadel Adib, Zachary Kabelac, Hongzi Mao, Dina Katabi, Robert C. Miller:
Demo: real-time breath monitoring using wireless signals. MobiCom 2014: 261-262
Coauthor Index
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last updated on 2024-10-07 22:14 CEST by the dblp team
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