The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and semantics of these tables makes it challenging to build a unified method that can effectively leverage the information they contain. Meanwhile, Large language models (LLMs) are becoming an increasingly integral component of web infrastructure for tasks like semantic search. This raises a crucial question: can we leverage these already-deployed LLMs to classify structured data in web-native tables (e.g., product catalogs, knowledge base exports, scientific data portals), avoiding the need for specialized models or extensive retraining? This work investigates a lightweight paradigm, $\textbf{Ta}$ble $\textbf{R}$epresentation with $\textbf{L}$anguage Model~($\textbf{TaRL}$), for few-shot tabular classification that directly utilizes semantic embeddings of individual table rows. We first show that naive application of these embeddings underperforms compared to specialized tabular models. We then demonstrate that their potentials can be unlocked with two key techniques: removing the common component from all embeddings and calibrating the softmax temperature. We show that a simple meta-learner, trained on handcrafted features, can learn to predict an appropriate temperature. This approach achieves performance comparable to state-of-the-art models in low-data regimes ($k \leq 32$) of semantically-rich tables. Our findings demonstrate the viability of reusing existing LLM infrastructure for efficient semantics-driven pathway to reuse existing LLM infrastructure for Web table understanding.

Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be help-ful for such data discovery tasks. In this paper, we present TabSketchFM, a neural tabular model for data discovery over data lakes. First, we propose novel pre-training: a sketch-based approach to enhance the effectiveness of data discovery in neural tabular models. Second, we finetune the pretrained model for identifying unionable, joinable, and subset table pairs and show significant improvement over previous tabular neural models. Third, we present a detailed ablation study to highlight which sketches are crucial for which tasks. Fourth, we use these finetuned models to perform table search; i.e., given a query table, find other tables in a corpus that are unionable, joinable, or that are subsets of the query. Our results demonstrate significant improvements in F1 scores for search compared to state-of-the-art techniques. Finally, we show significant transfer across datasets and tasks establishing that our model can generalize across different tasks and over different data lakes.

In this paper, we present a method for constructing a feature-engineered random forest by transforming the features of a given data set using a set of diverse and randomized transforms. The transformed features are then used for creating splits at each node of a random forest. In particular, we use sum-product features because of their strong expressive power. This type of on-the-fly feature engineering has significant advantages over traditional random forests because it adds to the diversity of the splits. Such a diversity further helps in variance reduction; over and above the variance reduction ability offered by traditional random forests. We show the advantages of the proposed approach over traditional random forests and other well-established baselines using extensive experimental evaluation.

Join discovery is a crucial part of exploration on data lakes. It often involves finding joinable tables that are semantically relevant. However, data lakes often contain numeric tables with unreliable column headers, and ID columns whose text names have been lost. Finding semantically relevant joins over numeric tables is a challenge. State-of-the-art describes join discovery using semantic similarity, but do not consider purely numeric tables. In this paper, we describe a system, NumJoin that includes two novel approaches for discovering joinable tables in a data lake: one that maps tables to knowledge graphs, and another that leverages numeric types and distributions. We demonstrate the effectiveness of NumJoin on a large data lake, including transportation data and finance data.

We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: ➀ After selecting the best hyperparameter using a held-out set, we train the final model using all of the training data - since this may or may not improve future generalization error, should one do this? ② During optimization such as via SGD (stochastic gradient descent), we must set the optimization tolerance ρ - since it trades off predictive accuracy with computation cost, how should one set it? Toward these problems, we introduce the hold-in risk (the error due to not using the whole training data), and the model class mis-specification risk (the error due to having chosen the wrong model class) in a theoretical view which is simple, general, and suggests heuristics that can be used when faced with a dataset instance. In proof-of-concept studies in synthetic data where theoretical quantities can be controlled, we show that these heuristics can, respectively, ➀ always perform at least as well as always performing retraining or never performing retraining, ② either improve performance or reduce computational overhead by 2× with no loss in predictive performance. * Full version: https://arxiv.org/abs/2301.05131

Data preparation is a crucial first step to any data analysis problem. This task is largely manual, performed by a person familiar with the data domain. DataRinse is a system designed to extract relevant transforms from large scale static analysis of repositories of code. Our motivation is that in any large enterprise, multiple personas such as data engineers and data scientists work on similar datasets. However, sharing or re-using that code is not obvious and difficult to execute. In this paper, we demonstrate DataRinse to handle data preparation, such that the system recommends code designed to help with the preparation of a column for data analysis more generally. We show that DataRinse does not simply shard expressions observed in code but also uses analysis to group expressions applied to the same field such that related transforms appear coherently to a user. It is a human-in-the-loop system where the users select relevant code snippets produced by DataRinse to apply on their dataset.

Feature engineering is a crucial but tedious task that requires up to 80% of the total time in data science projects. A significant challenge is when data consists of tables from different data sources, thus data scientists need to wisely aggregate and join tables while performing feature engineering task. In this work, we demonstrate a novel system called OneBM (One Button Machine), that enables data scientists to increase their efficiency with automated feature engineering for relational data. OneBM takes as input a relational dataset with multiple tables and its entity relation diagram (ERD) which can be declared with a novel, easy-to-use drag-and-drop graphical user interface. The system then automatically identifies and executes relevant joins and aggregates in the data, and generates new features with a rich set of transformations for various types of data including but not limited to time-series, sequences, number sets and itemsets, etc. The generated features then can be used by automated model selection and hyper-parameter optimization algorithms to complete a fully end-to-end automated data science (or AutoDS) workflow. A follow-up user evaluation illustrated how data scientists can perform multi-table feature engineering tasks in minutes using our system, compared to repeatedly coding SQL-like queries to transform and aggregate relational data requiring weeks of manual labor for comparable performance. In the live demos we plan to show two use cases with real-world datasets (video demos are available at the links in the footnote): sale prediction1and call center user experience2. Pre-registered partcipants can play with these use-cases and the given datasets via Watson Studio on the cloud.

A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that performs well on a given data is still challenging. Automatically providing a good set of models to users for a given dataset saves both time and effort from using trial-and-error approaches with a wide variety of available models along with parameter optimization. We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset. With its flexible zero-conf design, AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users and provides a trained model that is ready to use. For given data, AutoAI-TS utilizes a wide variety of models including classical statistical models, Machine Learning (ML) models, statistical-ML hybrid models and deep learning models along with various transformations to create forecasting pipelines. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline. The paper describe in detail all the technical aspects of AutoAI-TS along with extensive benchmarking on a variety of real world data sets for various use-cases. Benchmark results show that AutoAI-TS, with no manual configuration from the user, automatically trains and selects pipelines that on average outperform existing state-of-the-art time series forecasting toolkits.

Building a robust predictive model requires an array of steps such as data imputation, feature transformations, estimator selection, hyper-parameter search, ensemble construction, amongst others. Due to this vast, complex and heterogeneous space of operations, off-the-shelf optimization methods offer infeasible solutions for realistic response time requirements. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use of available tools. Over time, they develop an understanding of the behavior of operators, and perform serial decision making under uncertainty, colloquially referred to as educated guesswork. With an unprecedented demand for application of supervised machine learning, there is a call for solutions that automatically search for a suitable combination of operators across these tasks while minimize the modeling error. We introduce a novel system called APRL (Autonomous Predictive modeler via Reinforcement Learning), that uses past experience through reinforcement learning to optimize sequential decision making from within a set of diverse actions under a budget constraint. Our experiments demonstrate the superiority of the proposed approach over known AutoML systems that utilize Bayesian optimization or genetic algorithms.