Search Results for author:
Found 17 papers, 14 papers with code
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over.
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models.
Data Science with LLMs and Interpretable Models
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #2 on
Question Answering
on MedQA
(using extra training data)
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
We further demonstrate the promise of applying PE on large foundation models such as Stable Diffusion to tackle challenging private datasets with a small number of high-resolution images.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.
Ranked #31 on
Arithmetic Reasoning
on GSM8K
Capabilities of GPT-4 on Medical Challenge Problems
We also evaluate performance on the MultiMedQA suite of benchmark datasets.
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
Differentially Private Estimation of Heterogeneous Causal Effects
Our meta-algorithm can work with simple, single-stage CATE estimators such as S-learner and more complex multi-stage estimators such as DR and R-learner.
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment.
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy.
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers.
