25 citations · 74 across the 42 of their papers we have counts for
10 papers · 1 filter
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Dang Nguyen, Sunil Gupta, Santu Rana +2
Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) method…
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Hung Tran-The, Sunil Gupta, Santu Rana +1
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is a active and open research problems particularly when no assumptions are made on function structure. The mai…
Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta +3
Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Ba…
Cost-aware Multi-objective Bayesian optimisation
Majid Abdolshah, Alistair Shilton, Santu Rana +2
The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the…
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Thommen George Karimpanal, Santu Rana, Sunil Gupta +2
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic e…
Accelerating Experimental Design by Incorporating Experimenter Hunches
Cheng Li, Santu Rana, Sunil Gupta +8
Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when…