410 citations · 583 across the 38 of their papers we have counts for
15 papers · 1 filter
SALUS: Automated Auditing of NL-to-SQL Benchmarks through Weak Supervision of Multi-Agent Output
Shiyuan Zhou, Ashwin Gerard Colaco, Sainyam Galhotra +1
Natural language to SQL (NL-to-SQL) benchmarks are foundational to progress in data analysis research, yet recent work has shown that widely-used benchmarks contain significant ann…
AutoPrunedRetriever: Persistent Reasoning Graphs for Efficient and Extensible Retrieval-Augmented Generation
Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4
Retrieval-augmented generation (RAG) has become a standard approach for knowledge-intensive LLM tasks. Complex reasoning remains challenging because queries often require composing…
Octopus: A Lightweight Entity-Aware System for Multi-Table Data Discovery and Cell-Level Retrieval
Wen-Zhi Li, Sainyam Galhotra
Tabular data constitute a dominant form of information in modern data lakes and repositories, yet discovering the relevant tables to answer user questions remains challenging. Exis…
MetaJoin: Adaptive Join Selections for Heterogeneous Tables
Ning Wang, Sainyam Galhotra
Joining heterogeneous tables requires different forms of correspondence, including exact agreement, syntactic transformation, semantic similarity, and entity-level matching. The ap…
Towards General-Purpose Data Discovery: A Programming Languages Approach
Andrew Kang, Yashnil Saha, Sainyam Galhotra
Efficient and effective data discovery is critical for many modern applications in machine learning and data science. One major bottleneck to the development of a general-purpose d…
TQL: Towards Type-Driven Data Discovery
Andrew Kang, Sainyam Galhotra
Existing query languages for data discovery exhibit system-driven designs that emphasize database features and functionality over user needs. We propose a re-prioritization of the…
A Theoretical Framework for Distribution-Aware Dataset Search
Aryan Esmailpour, Sainyam Galhotra, Rahul Raychaudhury +1
Effective data discovery is a cornerstone of modern data-driven decision-making. Yet, identifying datasets with specific distributional characteristics, such as percentiles or pref…
Fair and Actionable Causal Prescription Ruleset
Benton Li, Nativ Levy, Brit Youngmann +2
Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, o…
Faster Algorithms for Fair Max-Min Diversification in
Yash Kurkure, Miles Shamo, Joseph Wiseman +2
The task of extracting a diverse subset from a dataset, often referred to as maximum diversification, plays a pivotal role in various real-world applications that have far-reaching…
METAM: Goal-Oriented Data Discovery
Sainyam Galhotra, Yue Gong, Raul Castro Fernandez
Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and da…
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
Sainyam Galhotra, Amir Gilad, Sudeepa Roy +1
What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical sce…
Ver: View Discovery in the Wild
Yue Gong, Zhiru Zhu, Sainyam Galhotra +1
We present Ver, a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries…
DataExposer: Exposing Disconnect between Data and Systems
Sainyam Galhotra, Anna Fariha, Raoni Lourenço +3
As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of the data. For example,…
Adaptive Rule Discovery for Labeling Text Data
Sainyam Galhotra, Behzad Golshan, Wang-Chiew Tan
Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learnin…
Efficient and Effective ER with Progressive Blocking
Sainyam Galhotra, Donatella Firmani, Barna Saha +1
Blocking is a mechanism to improve the efficiency of Entity Resolution (ER) which aims to quickly prune out all non-matching record pairs. However, depending on the distributions o…