4 papers
DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling
Yubo Gao, Renbo Tu, Gennady Pekhimenko +1
Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to train neural networks. During trai…
BHCast: Unlocking Black Hole Plasma Dynamics from a Single Blurry Image with Long-Term Forecasting
Renbo Tu, Ali SaraerToosi, Nicholas S. Conroy +2
The Event Horizon Telescope (EHT) delivered the first image of a black hole by capturing the light from its surrounding accretion flow, revealing structure but not dynamics. Simula…
NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements
Ali SaraerToosi, Renbo Tu, Kamyar Azizzadenesheli +2
Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measur…
Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes
Mayuka Jayawardhana, Renbo, Samuel Dooley +6
Large language models (LLMs) perform remarkably well on tabular datasets in zero- and few-shot settings, since they can extract meaning from natural language column headers that de…