TTPFTS is the first anytime algorithm for Pareto set identification in multi-objective multi-armed bandits, benchmarked on synthetic environments, applied to molecular discovery, equipped with a new uncertainty metric, and supported by an asymptotic correctness proof.
Grygorenko, Dmytro S
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces BOBa, a multi-armed bandit method for scalable surrogate optimization that adaptively allocates inference and evaluations to promising partitions of ultra-large chemical libraries.
GLACIER combines graph, SMILES, and descriptor encoders with Finsler fusion and contrastive distillation to produce an efficient multimodal model for molecular property prediction.
citing papers explorer
-
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
TTPFTS is the first anytime algorithm for Pareto set identification in multi-objective multi-armed bandits, benchmarked on synthetic environments, applied to molecular discovery, equipped with a new uncertainty metric, and supported by an asymptotic correctness proof.
-
Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
Introduces BOBa, a multi-armed bandit method for scalable surrogate optimization that adaptively allocates inference and evaluations to promising partitions of ultra-large chemical libraries.
-
GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction
GLACIER combines graph, SMILES, and descriptor encoders with Finsler fusion and contrastive distillation to produce an efficient multimodal model for molecular property prediction.