Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and
traditional dataset creation methods are labor intensive, costly, and require extensive …
traditional dataset creation methods are labor intensive, costly, and require extensive …
Lico: Large language models for in-context molecular optimization
Optimizing black-box functions is a fundamental problem in science and engineering. To
solve this problem, many approaches learn a surrogate function that estimates the …
solve this problem, many approaches learn a surrogate function that estimates the …
Latent energy-based odyssey: Black-box optimization via expanded exploration in the energy-based latent space
Offline Black-Box Optimization (BBO) aims at optimizing a black-box function using the
knowledge from a pre-collected offline dataset of function values and corresponding input …
knowledge from a pre-collected offline dataset of function values and corresponding input …
Guided trajectory generation with diffusion models for offline model-based optimization
Optimizing complex and high-dimensional black-box functions is ubiquitous in science and
engineering fields. Unfortunately, the online evaluation of these functions is restricted due to …
engineering fields. Unfortunately, the online evaluation of these functions is restricted due to …
Position Paper: Leveraging Foundational Models for Black-Box Optimization: Benefits, Challenges, and Future Directions
Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of
innovation in the machine learning research domain, resulting in substantial impact across …
innovation in the machine learning research domain, resulting in substantial impact across …
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Zero-shot optimization involves optimizing a target task that was not seen during training,
aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It …
aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It …
Reinforced In-Context Black-Box Optimization
Black-Box Optimization (BBO) has found successful applications in many fields of science
and engineering. Recently, there has been a growing interest in meta-learning particular …
and engineering. Recently, there has been a growing interest in meta-learning particular …
GROOT: Effective Design of Biological Sequences with Limited Experimental Data
Latent space optimization (LSO) is a powerful method for designing discrete, high-
dimensional biological sequences that maximize expensive black-box functions, such as …
dimensional biological sequences that maximize expensive black-box functions, such as …
Generative Adversarial Model-Based Optimization via Source Critic Regularization
Offline model-based optimization seeks to optimize against a learned surrogate model
without querying the true oracle objective function during optimization. Such tasks are …
without querying the true oracle objective function during optimization. Such tasks are …
Predicting from Strings: Language Model Embeddings for Bayesian Optimization
Bayesian Optimization is ubiquitous in the field of experimental design and blackbox
optimization for improving search efficiency, but has been traditionally restricted to …
optimization for improving search efficiency, but has been traditionally restricted to …