382 citations · 920 across the 11 of their papers we have counts for
11 papers
Flow-Loss: Learning Cardinality Estimates That Matter
Parimarjan Negi, Ryan Marcus, Andreas Kipf +4
Previous approaches to learned cardinality estimation have focused on improving average estimation error, but not all estimates matter equally. Since learned models inevitably make…
Neural Rate Control for Video Encoding using Imitation Learning
Hongzi Mao, Chenjie Gu, Miaosen Wang +9
In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rat…
Real-world Video Adaptation with Reinforcement Learning
Hongzi Mao, Shannon Chen, Drew Dimmery +5
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). We evaluate recently proposed RL-based ABR methods in Facebook's we…
Learned Garbage Collection
Lujing Cen, Ryan Marcus, Hongzi Mao +3
Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to fr…
Bao: Learning to Steer Query Optimizers
Ryan Marcus, Parimarjan Negi, Hongzi Mao +3
Query optimization remains one of the most challenging problems in data management systems. Recent efforts to apply machine learning techniques to query optimization challenges hav…
Interpreting Deep Learning-Based Networking Systems
Zili Meng, Minhu Wang, Jiasong Bai +3
While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable…
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta +2
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a d…
Neo: A Learned Query Optimizer
Ryan Marcus, Parimarjan Negi, Hongzi Mao +5
Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex component…
Learning Scheduling Algorithms for Data Processing Clusters
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan +2
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore work…
Variance Reduction for Reinforcement Learning in Input-Driven Environments
Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf +1
We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many appl…
Fluctuation-response Relation Unifies Dynamical Behaviors in Neural Fields
C. C. Alan Fung, K. Y. Michael Wong, Hongzi Mao +1
Anticipation is a strategy used by neural fields to compensate for transmission and processing delays during the tracking of dynamical information, and can be achieved by slow, loc…