{"total":230,"rows":[{"id":"2304.11127","title":"Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance","created":"2023-04-21","cited_by_count":147,"area":null},{"id":"2301.08727","title":"Neural Architecture Search: Insights from 1000 Papers","created":"2023-01-20","cited_by_count":139,"area":null},{"id":"2402.03921","title":"Large Language Models to Enhance Bayesian Optimization","created":"2024-02-06","cited_by_count":135,"area":null},{"id":"2209.00099","title":"Efficient Methods for Natural Language Processing: A Survey","created":"2022-08-31","cited_by_count":78,"area":null},{"id":"2312.04528","title":"Using Large Language Models for Hyperparameter Optimization","created":"2023-12-07","cited_by_count":71,"area":null},{"id":"2204.11051","title":"$\\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization","created":"2022-04-23","cited_by_count":70,"area":null},{"id":"2306.01324","title":"Hyperparameters in Reinforcement Learning and How To Tune Them","created":"2023-06-02","cited_by_count":59,"area":null},{"id":"2305.20028","title":"A Study of Bayesian Neural Network Surrogates for Bayesian Optimization","created":"2023-05-31","cited_by_count":50,"area":null},{"id":"2403.01131","title":"LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation","created":"2024-03-02","cited_by_count":45,"area":null},{"id":"2303.03186","title":"Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT","created":"2023-03-03","cited_by_count":41,"area":null},{"id":"2205.09095","title":"Achieving Risk Control in Online Learning Settings","created":"2022-05-18","cited_by_count":39,"area":null},{"id":"2205.13881","title":"Automated Dynamic Algorithm Configuration","created":"2022-05-27","cited_by_count":39,"area":null},{"id":"2306.08107","title":"AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks","created":"2023-06-13","cited_by_count":35,"area":null},{"id":"2403.09113","title":"AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning","created":"2024-03-14","cited_by_count":31,"area":null},{"id":"2202.01651","title":"A Survey of Methods for Automated Algorithm Configuration","created":"2022-02-03","cited_by_count":30,"area":null},{"id":"2308.13911","title":"A Wide Evaluation of ChatGPT on Affective Computing Tasks","created":"2023-08-26","cited_by_count":30,"area":null},{"id":"2305.13998","title":"SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes","created":"2023-05-23","cited_by_count":29,"area":null},{"id":"2410.16309","title":"In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics","created":"2024-10-07","cited_by_count":28,"area":null},{"id":"2202.10962","title":"Adaptive Cut Selection in Mixed-Integer Linear Programming","created":"2022-02-22","cited_by_count":26,"area":null},{"id":"2210.09943","title":"Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition","created":"2022-10-18","cited_by_count":26,"area":null},{"id":"2411.00625","title":"Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization","created":"2024-11-01","cited_by_count":24,"area":null},{"id":"2210.07913","title":"Efficiently Controlling Multiple Risks with Pareto Testing","created":"2022-10-14","cited_by_count":22,"area":null},{"id":"2311.14645","title":"A General Framework for User-Guided Bayesian Optimization","created":"2023-11-24","cited_by_count":22,"area":null},{"id":"2402.02355","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","created":"2024-02-04","cited_by_count":22,"area":null},{"id":"2207.09405","title":"Bayesian Generational Population-Based Training","created":"2022-07-19","cited_by_count":20,"area":null},{"id":"2302.09166","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","created":"2023-02-17","cited_by_count":20,"area":null},{"id":"2207.13676","title":"Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization","created":"2022-07-27","cited_by_count":18,"area":null},{"id":"2302.00441","title":"Scaling Laws for Hyperparameter Optimization","created":"2023-02-01","cited_by_count":16,"area":null},{"id":"2304.10255","title":"PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces","created":"2023-04-20","cited_by_count":15,"area":null},{"id":"2409.15844","title":"Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection","created":"2024-09-24","cited_by_count":15,"area":null},{"id":"2412.07507","title":"ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning","created":"2024-12-10","cited_by_count":15,"area":"deeprl"},{"id":"2205.05511","title":"Efficient Automated Deep Learning for Time Series Forecasting","created":"2022-05-11","cited_by_count":14,"area":null},{"id":"2206.03493","title":"DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning","created":"2022-06-07","cited_by_count":14,"area":null},{"id":"2207.00879","title":"Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces","created":"2022-07-02","cited_by_count":14,"area":null},{"id":"2406.03348","title":"Position: A Call to Action for a Human-Centered AutoML Paradigm","created":"2024-06-05","cited_by_count":14,"area":null},{"id":"2510.02798","title":"OptunaHub: A Platform for Black-Box Optimization","created":"2025-10-03","cited_by_count":14,"area":null},{"id":"2206.02511","title":"Transfer Learning based Search Space Design for Hyperparameter Tuning","created":"2022-06-06","cited_by_count":13,"area":null},{"id":"2406.05250","title":"LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation","created":"2024-06-07","cited_by_count":13,"area":null},{"id":"2303.06532","title":"Automated Design of Metaheuristic Algorithms: A Survey","created":"2023-03-12","cited_by_count":12,"area":null},{"id":"2212.11142","title":"BaCO: A Fast and Portable Bayesian Compiler Optimization Framework","created":"2022-12-01","cited_by_count":11,"area":null},{"id":"2401.03244","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","created":"2024-01-06","cited_by_count":11,"area":null},{"id":"2401.17842","title":"Explainable Benchmarking for Iterative Optimization Heuristics","created":"2024-01-31","cited_by_count":10,"area":null},{"id":"2505.21034","title":"LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms","created":"2025-05-27","cited_by_count":10,"area":null},{"id":"2505.21974","title":"BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL","created":"2025-05-28","cited_by_count":10,"area":null},{"id":"2510.27610","title":"ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling","created":"2025-10-31","cited_by_count":10,"area":null},{"id":"2211.14411","title":"c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization","created":"2022-11-26","cited_by_count":9,"area":null},{"id":"2304.13339","title":"OpenBox: A Python Toolkit for Generalized Black-box Optimization","created":"2023-04-26","cited_by_count":9,"area":null},{"id":"2401.01192","title":"Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems","created":"2024-01-02","cited_by_count":9,"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":"2405.07640","title":"Hyperparameter Importance Analysis for Multi-Objective AutoML","created":"2024-05-13","cited_by_count":9,"area":null},{"id":"2503.21023","title":"Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework","created":"2025-03-26","cited_by_count":9,"area":null},{"id":"2309.03581","title":"Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning","created":"2023-09-07","cited_by_count":8,"area":null},{"id":"2206.02663","title":"TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning","created":"2022-06-06","cited_by_count":7,"area":null},{"id":"2304.04142","title":"Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization","created":"2023-04-09","cited_by_count":7,"area":null},{"id":"2306.01276","title":"Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization","created":"2023-06-02","cited_by_count":7,"area":null},{"id":"2310.13261","title":"DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee","created":"2023-10-20","cited_by_count":7,"area":null},{"id":"2405.03419","title":"Automated Metaheuristic Algorithm Design with Autoregressive Learning","created":"2024-05-06","cited_by_count":7,"area":null},{"id":"2405.15393","title":"Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization","created":"2024-05-24","cited_by_count":7,"area":null},{"id":"2412.14409","title":"Multi-task Representation Learning for Mixed Integer Linear Programming","created":"2024-12-18","cited_by_count":7,"area":null},{"id":"2206.03966","title":"FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization","created":"2022-06-08","cited_by_count":6,"area":null},{"id":"2211.00759","title":"Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning","created":"2022-11-01","cited_by_count":6,"area":null},{"id":"2211.01455","title":"PI is back! Switching Acquisition Functions in Bayesian Optimization","created":"2022-11-02","cited_by_count":6,"area":null},{"id":"2307.04648","title":"Can ChatGPT's Responses Boost Traditional Natural Language Processing?","created":"2023-07-06","cited_by_count":6,"area":null},{"id":"2307.08175","title":"Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models","created":"2023-07-17","cited_by_count":6,"area":null},{"id":"2401.14919","title":"PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2405.03389","title":"Don't Waste Your Time: Early Stopping Cross-Validation","created":"2024-05-06","cited_by_count":6,"area":null},{"id":"2408.05040","title":"BoFire: Bayesian Optimization Framework Intended for Real Experiments","created":"2024-08-09","cited_by_count":6,"area":null},{"id":"2505.21372","title":"Improving LLM-based Global Optimization with Search Space Partitioning","created":"2025-05-27","cited_by_count":6,"area":null},{"id":"2510.05516","title":"NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information","created":"2025-10-07","cited_by_count":6,"area":null},{"id":"2201.12767","title":"Bayesian Optimization For Multi-Objective Mixed-Variable Problems","created":"2022-01-30","cited_by_count":5,"area":null},{"id":"2306.04262","title":"Self-Adjusting Weighted Expected Improvement for Bayesian Optimization","created":"2023-06-07","cited_by_count":5,"area":null},{"id":"2307.00670","title":"Automatic MILP Solver Configuration By Learning Problem Similarities","created":"2023-07-02","cited_by_count":5,"area":null},{"id":"2310.02540","title":"Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data","created":"2023-10-04","cited_by_count":5,"area":null},{"id":"2312.01692","title":"Risk-Controlling Model Selection via Guided Bayesian Optimization","created":"2023-12-04","cited_by_count":5,"area":null},{"id":"2502.20265","title":"On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($\\lambda$,$\\lambda$))-GA","created":"2025-02-27","cited_by_count":5,"area":"deeprl"},{"id":"2506.19540","title":"Overtuning in Hyperparameter Optimization","created":"2025-06-24","cited_by_count":5,"area":null},{"id":"2603.19897","title":"Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes","created":"2026-03-20","cited_by_count":5,"area":null},{"id":"2607.20468","title":"InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents","created":"2026-05-20","cited_by_count":5,"area":"agents"},{"id":"2111.14756","title":"Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers","created":"2021-11-29","cited_by_count":4,"area":null},{"id":"2204.00998","title":"AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures","created":"2022-04-03","cited_by_count":4,"area":null},{"id":"2310.09766","title":"Pseudo-Bayesian Optimization","created":"2023-10-15","cited_by_count":4,"area":null},{"id":"2502.01276","title":"HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2502.04030","title":"Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging","created":"2025-02-06","cited_by_count":4,"area":"lr"},{"id":"2505.05049","title":"UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2505.05226","title":"Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2505.17804","title":"Hyperparameter Optimization via Interacting with Probabilistic Circuits","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2506.22621","title":"Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design","created":"2025-06-27","cited_by_count":4,"area":null},{"id":"2507.06192","title":"SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads","created":"2025-07-08","cited_by_count":4,"area":null},{"id":"2507.22876","title":"Discovering heuristics in a complex SAT solver with large language models","created":"2025-07-30","cited_by_count":4,"area":null},{"id":"2201.00207","title":"AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection","created":"2022-01-01","cited_by_count":3,"area":null},{"id":"2208.00204","title":"Tackling Neural Architecture Search With Quality Diversity Optimization","created":"2022-07-30","cited_by_count":3,"area":null},{"id":"2210.03324","title":"AutoML for Climate Change: A Call to Action","created":"2022-10-07","cited_by_count":3,"area":null},{"id":"2305.08624","title":"Mastering the exploration-exploitation trade-off in Bayesian Optimization","created":"2023-05-15","cited_by_count":3,"area":null},{"id":"2401.04478","title":"TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction","created":"2024-01-09","cited_by_count":3,"area":null},{"id":"2404.01965","title":"Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks","created":"2024-04-02","cited_by_count":3,"area":null},{"id":"2409.18827","title":"ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning","created":"2024-09-27","cited_by_count":3,"area":"deeprl"},{"id":"2411.09238","title":"Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers","created":"2024-11-14","cited_by_count":3,"area":null},{"id":"2502.04290","title":"Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants","created":"2025-02-06","cited_by_count":3,"area":null},{"id":"2503.03625","title":"Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?","created":"2025-03-05","cited_by_count":3,"area":null},{"id":"2503.05574","title":"BARK: A Fully Bayesian Tree Kernel for Black-box Optimization","created":"2025-03-07","cited_by_count":3,"area":null}],"next_cursor":"100"}