Adam with Bandit Sampling for Deep Learning
Adam is a widely used optimization method for training deep learning models. It computes individual adaptive learning rates for different parameters. In this paper, we propose a generalization of...
Transformer with Memory Replay
Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have very high sample...
QuickSel: Quick Selectivity Learning with Mixture Models
Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning...
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional...
Joins on Samples: A Theoretical Guide for Practitioners
Despite decades of research on approximate query processing (AQP), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common belief in the...
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts...
A Bandit Approach to Maximum Inner Product Search
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant...
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing...
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint sets. We provide a faster convergence rate for FW without line search, showing that a previously overlooked variant of...
Visualization-Aware Sampling for Very Large Databases
Interactive visualizations are crucial in ad hoc data exploration and analysis. However, with the growing number of massive datasets, generating visualizations in interactive timescales is...
BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data
In this paper, we present BlinkDB, a massively parallel, sampling-based approximate query engine for running ad-hoc, interactive SQL queries on large volumes of data. The key insight that BlinkDB...
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
Decades of research have sought to improve transaction processing performance and scalability in database management systems (DBMSs). However, significantly less attention has been dedicated to...
Active Learning for Crowd-Sourced Databases
Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objects, or analyzing...