527 research outputs found
HoloDetect: Few-Shot Learning for Error Detection
We introduce a few-shot learning framework for error detection. We show that
data augmentation (a form of weak supervision) is key to training high-quality,
ML-based error detection models that require minimal human involvement. Our
framework consists of two parts: (1) an expressive model to learn rich
representations that capture the inherent syntactic and semantic heterogeneity
of errors; and (2) a data augmentation model that, given a small seed of clean
records, uses dataset-specific transformations to automatically generate
additional training data. Our key insight is to learn data augmentation
policies from the noisy input dataset in a weakly supervised manner. We show
that our framework detects errors with an average precision of ~94% and an
average recall of ~93% across a diverse array of datasets that exhibit
different types and amounts of errors. We compare our approach to a
comprehensive collection of error detection methods, ranging from traditional
rule-based methods to ensemble-based and active learning approaches. We show
that data augmentation yields an average improvement of 20 F1 points while it
requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages
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 use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201
Query-Driven Learning for Next Generation Predictive Modeling & Analytics
As data-size is increasing exponentially, new paradigm shifts have to emerge allowing fast exploitation of data by every- body. Large-scale predictive analytics is restricted to wealthy organizations as small-scale enterprises (SMEs) struggle to compete and are inundated by the sheer monetary cost of either procuring data infrastructures or analyzing datasets over the Cloud. The aim of this work is to study mechanisms which can democratize analytics, in the sense of making them affordable, while at the same time ensuring high efficiency, scalability, and accuracy. The crux of this proposal lies in developing query-driven solutions that can be used off the Cloud thus minimizing costs. Our query-driven approach will learn and adapt on-the-fly machine learning models, based solely on query-answer interactions, which can be used for answering analytical queries. In this abstract we describe the methodology followed for the implementation and evaluation of the system designed
An experimental study of learned cardinality estimation
Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this thesis, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in production? Our study consists of three main parts. Firstly, we focus on the static environment (i.e., no data updates) and compare five new learned methods with eight traditional methods on four real-world datasets under a unified workload setting. The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs. Secondly, we explore whether these learned models are ready for dynamic environments (i.e., frequent data updates). We find that they can- not catch up with fast data updates and return large errors for different reasons. For less frequent updates, they can perform better but there is no clear winner among themselves. Thirdly, we take a deeper look into learned models and explore when they may go wrong. Our results show that the performance of learned methods can be greatly affected by the changes in correlation, skewness, or domain size. More importantly, their behaviors are much harder to interpret and often unpredictable. Based on these findings, we identify two promising research directions (control the cost of learned models and make learned models trustworthy) and suggest a number of research opportunities. We hope that our study can guide researchers and practitioners to work together to eventually push learned cardinality estimators into real database systems
Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective
Algorithmic rankers have a profound impact on our increasingly data-driven
society. From leisurely activities like the movies that we watch, the
restaurants that we patronize; to highly consequential decisions, like making
educational and occupational choices or getting hired by companies -- these are
all driven by sophisticated yet mostly inaccessible rankers. A small change to
how these algorithms process the rankees (i.e., the data items that are ranked)
can have profound consequences. For example, a change in rankings can lead to
deterioration of the prestige of a university or have drastic consequences on a
job candidate who missed out being in the list of the preferred top-k for an
organization. This paper is a call to action to the human-centered data science
research community to develop principled methods, measures, and metrics for
studying the interactions among the socio-technical context of use,
technological innovations, and the resulting consequences of algorithmic
rankings on multiple stakeholders. Given the spate of new legislations on
algorithmic accountability, it is imperative that researchers from social
science, human-computer interaction, and data science work in unison for
demystifying how rankings are produced, who has agency to change them, and what
metrics of socio-technical impact one must use for informing the context of
use.Comment: Accepted for Interrogating Human-Centered Data Science workshop at
CHI'2
Complaint-driven Training Data Debugging for Query 2.0
As the need for machine learning (ML) increases rapidly across all industry
sectors, there is a significant interest among commercial database providers to
support "Query 2.0", which integrates model inference into SQL queries.
Debugging Query 2.0 is very challenging since an unexpected query result may be
caused by the bugs in training data (e.g., wrong labels, corrupted features).
In response, we propose Rain, a complaint-driven training data debugging
system. Rain allows users to specify complaints over the query's intermediate
or final output, and aims to return a minimum set of training examples so that
if they were removed, the complaints would be resolved. To the best of our
knowledge, we are the first to study this problem. A naive solution requires
retraining an exponential number of ML models. We propose two novel heuristic
approaches based on influence functions which both require linear retraining
steps. We provide an in-depth analytical and empirical analysis of the two
approaches and conduct extensive experiments to evaluate their effectiveness
using four real-world datasets. Results show that Rain achieves the highest
recall@k among all the baselines while still returns results interactively.Comment: Proceedings of the 2020 ACM SIGMOD International Conference on
Management of Dat
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