{"total":62,"rows":[{"id":"2208.06102","title":"Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training","created":"2022-08-12","cited_by_count":79,"area":null},{"id":"2311.07989","title":"Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code","created":"2023-11-14","cited_by_count":62,"area":null},{"id":"2302.06873","title":"Lero: A Learning-to-Rank Query Optimizer","created":"2023-02-14","cited_by_count":26,"area":null},{"id":"2201.01441","title":"Balsa: Learning a Query Optimizer Without Expert Demonstrations","created":"2022-01-05","cited_by_count":18,"area":null},{"id":"2201.00561","title":"Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction","created":"2022-01-03","cited_by_count":16,"area":null},{"id":"2510.27176","title":"Glia: A Human-Inspired AI for Automated Systems Design and Optimization","created":"2025-10-31","cited_by_count":16,"area":null},{"id":"2511.07663","title":"Cortex AISQL: A Production SQL Engine for Unstructured Data","created":"2025-11-10","cited_by_count":11,"area":null},{"id":"2108.04526","title":"A Survey on Deep Reinforcement Learning for Data Processing and Analytics","created":"2021-08-10","cited_by_count":10,"area":null},{"id":"2404.11581","title":"E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model","created":"2024-04-17","cited_by_count":9,"area":null},{"id":"2506.07675","title":"QUITE: A Query Rewrite System Beyond Rules with LLM Agents","created":"2025-06-09","cited_by_count":8,"area":"agents"},{"id":"2210.05508","title":"Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data","created":"2022-10-11","cited_by_count":6,"area":null},{"id":"2502.12908","title":"Graph Neural Networks for Databases: A Survey","created":"2025-02-18","cited_by_count":6,"area":null},{"id":"2405.03924","title":"NeurDB: An AI-powered Autonomous Data System","created":"2024-05-07","cited_by_count":5,"area":null},{"id":"2108.10130","title":"No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees","created":"2021-08-23","cited_by_count":4,"area":null},{"id":"2307.02626","title":"Real-time Workload Pattern Analysis for Large-scale Cloud Databases","created":"2023-07-05","cited_by_count":4,"area":null},{"id":"2401.03723","title":"Sibyl: Forecasting Time-Evolving Query Workloads","created":"2024-01-08","cited_by_count":4,"area":null},{"id":"2411.02862","title":"The Unreasonable Effectiveness of LLMs for Query Optimization","created":"2024-11-05","cited_by_count":4,"area":"lr"},{"id":"2306.06798","title":"Kepler: Robust Learning for Faster Parametric Query Optimization","created":"2023-06-11","cited_by_count":3,"area":null},{"id":"2406.16349","title":"AnnotatedTables: A Large Tabular Dataset with Language Model Annotations","created":"2024-06-24","cited_by_count":3,"area":null},{"id":"2412.02372","title":"HERO: Hint-Based Efficient and Reliable Query Optimizer","created":"2024-12-03","cited_by_count":3,"area":null},{"id":"2505.04404","title":"In-Context Adaptation to Concept Drift for Learned Database Operations","created":"2025-05-07","cited_by_count":3,"area":null},{"id":"2210.13573","title":"Conditionally Risk-Averse Contextual Bandits","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2210.13625","title":"Deploying a Steered Query Optimizer in Production at Microsoft","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2212.10318","title":"Learned-Database Systems Security","created":"2022-12-20","cited_by_count":2,"area":null},{"id":"2308.13641","title":"ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges","created":"2023-08-25","cited_by_count":2,"area":null},{"id":"2512.09548","title":"Supporting Dynamic Agentic Workloads: How Data and Agents Interact","created":"2025-12-10","cited_by_count":2,"area":"lr"},{"id":"2604.06566","title":"AI-Driven Research for Databases","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2105.13336","title":"TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system","created":"2021-05-27","cited_by_count":1,"area":null},{"id":"2307.11704","title":"JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning","created":"2023-07-21","cited_by_count":1,"area":null},{"id":"2312.06357","title":"FOSS: A Self-Learned Doctor for Query Optimizer","created":"2023-12-11","cited_by_count":1,"area":null},{"id":"2401.01280","title":"GEqO: ML-Accelerated Semantic Equivalence Detection","created":"2024-01-02","cited_by_count":1,"area":null},{"id":"2401.15210","title":"Robust Plan Evaluation based on Approximate Probabilistic Machine Learning","created":"2024-01-26","cited_by_count":1,"area":null},{"id":"2405.04814","title":"A Novel Technique for Query Plan Representation Based on Graph Neural Nets","created":"2024-05-08","cited_by_count":1,"area":null},{"id":"2405.07770","title":"Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation","created":"2024-05-13","cited_by_count":1,"area":"deeprl"},{"id":"2411.04525","title":"GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2412.01709","title":"Query Performance Explanation through Large Language Model for HTAP Systems","created":"2024-12-02","cited_by_count":1,"area":null},{"id":"2503.08131","title":"Large Scale Multi-Task Bayesian Optimization with Large Language Models","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2503.08994","title":"DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation","created":"2025-03-12","cited_by_count":1,"area":null},{"id":"2206.05778","title":"Learning-Based Data Storage [Vision] (Technical Report)","created":"2022-06-12","cited_by_count":0,"area":null},{"id":"2306.00845","title":"BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment","created":"2023-06-01","cited_by_count":0,"area":null},{"id":"2501.17414","title":"Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization","created":"2025-01-29","cited_by_count":0,"area":null},{"id":"2502.20233","title":"Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2504.06399","title":"Low Rank Learning for Offline Query Optimization","created":"2025-04-08","cited_by_count":0,"area":null},{"id":"2504.19142","title":"BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning","created":"2025-04-27","cited_by_count":0,"area":"deeprl"},{"id":"2506.05853","title":"Training-Free Query Optimization via LLM-Based Plan Similarity","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2507.03384","title":"LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization","created":"2025-07-04","cited_by_count":0,"area":null},{"id":"2507.17778","title":"An advanced AI driven database system","created":"2025-07-22","cited_by_count":0,"area":null},{"id":"2511.06780","title":"OntoTune: Ontology-Driven Learning for Query Optimization with Convolutional Models","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2512.12624","title":"CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2512.14358","title":"TiCard: Deployable EXPLAIN-only Residual Learning for Cardinality Estimation","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2603.01570","title":"Adversarial Query Synthesis via Bayesian Optimization","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.13835","title":"MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.14725","title":"RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2604.20145","title":"Pre-Execution Query Slot-Time Prediction in Cloud Data Warehouses: A Feature-Scoped Machine Learning Approach","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.17831","title":"Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics","created":"2026-05-18","cited_by_count":0,"area":"agents"},{"id":"2607.22621","title":"Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.28311","title":"Fully Inductive Cardinality Estimation","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.07945","title":"ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.08639","title":"Smart Compaction: Predicting Compaction Utility from Lakehouse Table Metadata","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2608.26746","title":"FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.04787","title":"Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.11390","title":"VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents","created":"2026-09-10","cited_by_count":0,"area":"rag"}],"next_cursor":null}