2 citations · 2 across the 5 of their papers we have counts for
6 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 …
Intrinsic Riemannian Cross-covariance for Manifold-valued Random Objects
Carlos Soto, Cheng Wang, Yujing Huang +1
Covariance estimation yields a fundamental second-order statistic underlying representation learning, dimension reduction, and dependence modeling. While covariance has been well u…
Rao Differential Privacy
Carlos Soto
Differential privacy (DP) has recently emerged as a definition of privacy to release private estimates. DP calibrates noise to be on the order of an individuals contribution. Due t…
Differentially Private Geodesic Regression
Aditya Kulkarni, Carlos Soto
In statistical applications it has become increasingly common to encounter data structures that live on non-linear spaces such as manifolds. Classical linear regression, one of the…
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation
Carlos Soto, Matthew Reimherr, Aleksandra Slavkovic +1
In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently ti…
Differential Privacy Over Riemannian Manifolds
Matthew Reimherr, Karthik Bharath, Carlos Soto
In this work we consider the problem of releasing a differentially private statistical summary that resides on a Riemannian manifold. We present an extension of the Laplace or K-no…