activity
20182025
most citedDeep Unsupervised Cardinality Estimation

207 citations · 240 across the 5 of their papers we have counts for

collaborators

6 papers

cs.DC2025

The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution

Frank Sifei Luan, Ron Yifeng Wang, Yile Gu +12

While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Distributed data processing systems based on the batch or stream pr…

cs.DB2024★ 3 cited

Text2SQL is Not Enough: Unifying AI and Databases with TAG

Asim Biswal, Liana Patel, Siddarth Jha +5

AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge…

cs.LG2024★ 6 cited

Optimizing LLM Queries in Relational Data Analytics Workloads

Shu Liu, Asim Biswal, Amog Kamsetty +8

Batch data analytics is a growing application for Large Language Models (LLMs). LLMs enable users to perform a wide range of natural language tasks, such as classification, entity…

cs.DB2020★ 24 cited

NeuroCard: One Cardinality Estimator for All Tables

Zongheng Yang, Amog Kamsetty, Sifei Luan +4

Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex que…

cs.DB2019★ 207 cited

Deep Unsupervised Cardinality Estimation

Zongheng Yang, Eric Liang, Amog Kamsetty +7

Cardinality estimation has long been grounded in statistical tools for density estimation. To capture the rich multivariate distributions of relational tables, we propose the use o…

cs.LG2018

Targeted Adversarial Examples for Black Box Audio Systems

Rohan Taori, Amog Kamsetty, Brenton Chu +1

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adver…