58 citations · 117 across the 12 of their papers we have counts for
14 papers
Efficient Provably Private Classification with a Tabular Foundation Model
Talal Alrawajfeh, Cristiana Diaconu, Ossi Räisä +5
Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level information, creating a need for acc…
Stroke Patches: Customizable Artistic Image Styling Using Regression
Ian Jaffray, John Bronskill
We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit contr…
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
Aliaksandra Shysheya, John Bronskill, James Requeima +4
We introduce a simple method for probabilistic predictions on tabular data based on Large Language Models (LLMs) called JoLT (Joint LLM Process for Tabular data). JoLT uses the in-…
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
James Requeima, John Bronskill, Dami Choi +2
Machine learning practitioners often face significant challenges in formally integrating their prior knowledge and beliefs into predictive models, limiting the potential for nuance…
SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay
Lachlan Thorpe, Lewis Bawden, Karanjot Vendal +2
We present a transformer decoder based sports simulation engine, SportsNGEN, trained on sports player and ball tracking sequences, that is capable of generating sustained gameplay…
On the Efficacy of Differentially Private Few-shot Image Classification
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill +5
There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models. These DP models are ty…