Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Directed Graph Grammars for Sequence-based Learning
Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While many effective encoders exist for DAGs, i…
Bayesian OptimizationGraph GenerationProperty PredictionBayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
Bayesian optimization (BO) with preference-based feedback has recently garnered significant attention due to its emerging applications. We refer to this problem as Bayesian Optimization from Human Feedback (BOHF), which …
Bayesian OptimizationSimplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
The optimization of expensive black-box functions is ubiquitous in science and engineering. A common solution to this problem is Bayesian optimization (BO), which is generally comprised of two components: (i) a surrogate…
Bayesian OptimizationThompson SamplingBOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
Bayesian optimization (BO) offers an efficient pipeline for optimizing black-box functions with the help of a Gaussian process prior and an acquisition function (AF). Recently, in the context of single-objective BO, lear…
Bayesian OptimizationHyperparameter OptimizationLanguage ModelingLanguage Modelling+1Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their actions after actively exploring causes and …
Bayesian OptimizationImproving LLM-based Global Optimization with Search Space Partitioning
Large Language Models (LLMs) have recently emerged as effective surrogate models and candidate generators within global optimization frameworks for expensive blackbox functions. Despite promising results, LLM-based metho…
Bayesian Optimizationglobal-optimizationLLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms
Bayesian optimization (BO) is a powerful class of algorithms for optimizing expensive black-box functions, but designing effective BO algorithms remains a manual, expertise-driven task. Recent advancements in Large Langu…
Bayesian OptimizationBenchmarkingLanguage ModelingLanguage Modelling+2T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search
Real-world multimodal misinformation often arises from mixed forgery sources, requiring dynamic reasoning and adaptive verification. However, existing methods mainly rely on static pipelines and limited tool usage, limit…
Bayesian OptimizationMisinformationsyftr: Pareto-Optimal Generative AI
Retrieval-Augmented Generation (RAG) pipelines are central to applying large language models (LLMs) to proprietary or dynamic data. However, building effective RAG flows is complex, requiring careful selection among vect…
Bayesian OptimizationRAGRetrieval-augmented GenerationComposable Cross-prompt Essay Scoring by Merging Models
Recent advances in cross-prompt automated essay scoring (AES) typically train models jointly on all source prompts, often requiring additional access to unlabeled target prompt essays simultaneously. However, using all s…
AllAutomated Essay ScoringBayesian OptimizationComputational EfficiencyHiLAB: A Hybrid Inverse-Design Framework
HiLAB (Hybrid inverse-design with Latent-space learning, Adjoint-based partial optimizations, and Bayesian optimization) is a new paradigm for inverse design of nanophotonic structures. Combining early-terminated topolog…
Bayesian OptimizationSpectral Mixture Kernels for Bayesian Optimization
Bayesian Optimization (BO) is a widely used approach for solving expensive black-box optimization tasks. However, selecting an appropriate probabilistic surrogate model remains an important yet challenging problem. In th…
Bayesian OptimizationEnhancing Monte Carlo Dropout Performance for Uncertainty Quantification
Knowing the uncertainty associated with the output of a deep neural network is of paramount importance in making trustworthy decisions, particularly in high-stakes fields like medical diagnosis and autonomous systems. Mo…
Bayesian OptimizationMedical DiagnosisUncertainty QuantificationAudio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models
The rise of Large Audio Language Models (LAMs) brings both potential and risks, as their audio outputs may contain harmful or unethical content. However, current research lacks a systematic, quantitative evaluation of LA…
Bayesian OptimizationSpeech Synthesistext-to-speechText to Speech+1Steering Generative Models with Experimental Data for Protein Fitness Optimization
Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences. Recent developments in steering protein gener…
Bayesian OptimizationThompson SamplingFew-Shot Test-Time Optimization Without Retraining for Semiconductor Recipe Generation and Beyond
We introduce Model Feedback Learning (MFL), a novel test-time optimization framework for optimizing inputs to pre-trained AI models or deployed hardware systems without requiring any retraining of the models or modificat…
Bayesian OptimizationRecipe Generation$\texttt{LLINBO}$: Trustworthy LLM-in-the-Loop Bayesian Optimization
Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkable adaptability in low-data regimes, mak…
Bayesian OptimizationGaussian ProcessesPhilosophySequential Decision MakingReasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs
Many real-world scientific and industrial applications require the optimization of expensive black-box functions. Bayesian Optimization (BO) provides an effective framework for such problems. However, traditional BO meth…
Bayesian OptimizationKnowledge GraphsAsymptotic Performance of Time-Varying Bayesian Optimization
Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy and expensive to evaluate. Is it possible for the instantaneous regret of a TV…
Bayesian OptimizationTurbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and model uncertainty. However, GP inference…
Bayesian OptimizationGaussian ProcessesPoint Processes