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Found 24 papers, 7 papers with code
Lero: A Learning-to-Rank Query Optimizer
In this paper, we introduce a learning-to-rank query optimizer, called Lero, which builds on top of a native query optimizer and continuously learns to improve the optimization performance.
Robust Contextual Linear Bandits
Our experiments show that RoLinTS is comparably statistically efficient to the classic methods when the misspecification is low, more robust when the misspecification is high, and significantly more computationally efficient than its naive implementation.
Baihe: SysML Framework for AI-driven Databases
Based on the high level architecture, we then describe a concrete implementation of Baihe for PostgreSQL and present example use cases for learned query optimizers.
Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size
Cardinality estimation (CardEst), a central component of the query optimizer, plays a significant role in generating high-quality query plans in DBMS.
$\sbf{\delta^2}$-exploration for Reinforcement Learning
Effectively tackling the \emph{exploration-exploitation dilemma} is still a major challenge in reinforcement learning.
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
Random Effect Bandits
It is well known that side information, such as the prior distribution of arm means in Thompson sampling, can improve the statistical efficiency of the bandit algorithm.
Guidance and Teaching Network for Video Salient Object Detection
Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Bayesian exploration strategies like Thompson Sampling resolve this trade-off in a principled way by modeling and updating the distribution of the parameters of the action-value function, the outcome model of the environment.
A Unified Transferable Model for ML-Enhanced DBMS
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
Gradient descent temporal difference-difference learning
Off-policy learning algorithms, in which an agent updates the value function of the optimal policy while selecting actions using an independent exploration policy, provide an effective solution to the explore-exploit tradeoff and have proven to be of great practical value in reinforcement learning.
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications
To resolve this, we propose a new structure learning algorithm LEAST, which comprehensively fulfills our business requirements as it attains high accuracy, efficiency and scalability at the same time.
Self-correcting Q-Learning
The Q-learning algorithm is known to be affected by the maximization bias, i. e. the systematic overestimation of action values, an important issue that has recently received renewed attention.
FSPN: A New Class of Probabilistic Graphical Model
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
We apply a reinforcement learning (RL) based approach to learning optimal synchronization policies used for Parameter Server-based distributed training of machine learning models with Stochastic Gradient Descent (SGD).
AliGraph: A Comprehensive Graph Neural Network Platform
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.
Distributed, Parallel, and Cluster Computing
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution.
Subsampled Optimization: Statistical Guarantees, Mean Squared Error Approximation, and Sampling Method
For optimization on large-scale data, exactly calculating its solution may be computationally difficulty because of the large size of the data.
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size.
Optimal Subsampling for Large Sample Logistic Regression
In this paper, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression.
Optimal Subsampling Approaches for Large Sample Linear Regression
For unweighted estimation algorithm, we show that its resulting subsample estimator is not consistent to the full sample OLS estimator.
Poisson Subsampling Algorithms for Large Sample Linear Regression in Massive Data
Subsampling is one of efficient strategies to handle this problem.
