Author name cluster

Tim Kraska

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL

Yue Gong
Chuan Lei
Xiao Qin
Kapil Vaidya
Balakrishnan Narayanaswamy
Tim Kraska

Text-to-SQL systems translate natural language (NL) questions into SQL queries, enabling non-technical users to interact with structured data. While large language models (LLMs) have shown promising results on the text-to-SQL task, they often produce semantically incorrect yet syntactically valid queries, with limited insight into their reliability. We propose SQLens, an end-to-end framework for fine-grained detection and correction of semantic errors in LLM-generated SQL. SQLens integrates error signals from both the underlying database and the LLM to identify potential semantic errors within SQL clauses. It further leverages these signals to guide query correction. Empirical results on two public benchmarks show that SQLens outperforms the best LLM-based self-evaluation method by 25. 78% in F1 for error detection, and improves execution accuracy of out-of-the-box text-to-SQL systems by up to 20%.

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ICLR Conference 2021 Conference Paper

Partitioned Learned Bloom Filters

Kapil Vaidya
Eric Knorr
Michael Mitzenmacher
Tim Kraska

Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using a learned model for the represented set. However, previous methods for learned Bloom filters do not take full advantage of the learned model. Here we show how to frame the problem of optimal model utilization as an optimization problem, and using our framework derive algorithms that can achieve near-optimal performance in many cases.

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NeurIPS Conference 2019 Conference Paper

Park: An Open Platform for Learning-Augmented Computer Systems

Hongzi Mao
Parimarjan Negi
Akshay Narayan
Hanrui Wang
Jiacheng Yang
Haonan Wang
Ryan Marcus
ravichandra addanki

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems. Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.

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