77 citations · 584 across the 84 of their papers we have counts for
4 papers · 1 filter
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
Arun Verma, Zhongxiang Dai, Yao Shu +1
We study a novel variant of the parameterized bandits problem in which the learner can observe additional auxiliary feedback that is correlated with the observed reward. The auxili…
Quantum Bayesian Optimization
Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma +3
Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been the…
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh +2
Existing neural active learning algorithms have aimed to optimize the predictive performance of neural networks (NNs) by selecting data for labelling. However, other than a good pr…
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
Tiedong Liu, Bryan Kian Hsiang Low
We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves stat…