1 citations · 1 across the 6 of their papers we have counts for
8 papers
How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Aditya Kulkarni, Davide Sartor, Carlos Soto +1
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than …
Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds
Davide Sartor, Meghan E. Huber, Donghyun Kim +1
Bayesian Optimization (BO) has become an established methodology for minimizing black-box functions of a vector input. Often, however, this parameter vector arises from the discret…
Active Subspaces in Infinite Dimension
Poorbita Kundu, Nathan Wycoff
Active subspace analysis uses the leading eigenspace of the gradient's second moment to conduct supervised dimension reduction. In this article, we extend this methodology to real-…
On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization
Nathan Wycoff
In Bayesian Optimization (BO), additive assumptions can mitigate the twin difficulties of modeling and searching a complex function in high dimension. However, common acquisition f…
Surrogate Active Subspaces for Jump-Discontinuous Functions
Nathan Wycoff
Surrogate modeling and active subspaces have emerged as powerful paradigms in computational science and engineering. Porting such techniques to computational models in the social s…
Neko: a Library for Exploring Neuromorphic Learning Rules
Zixuan Zhao, Nathan Wycoff, Neil Getty +2
The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking mod…
Towards On-Chip Bayesian Neuromorphic Learning
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
If edge devices are to be deployed to critical applications where their decisions could have serious financial, political, or public-health consequences, they will need a way to si…
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
As neural networks have begun performing increasingly critical tasks for society, ranging from driving cars to identifying candidates for drug development, the value of their abili…