9 papers · 1 filter
Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution
Shravan Chaudhari, Yoav Wald, Suchi Saria
As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. Such shifts can take many forms: new cl…
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
Hsing-Huan Chung, Shijun Li, Yoav Wald +3
Multimodal irregular time series (MITS) consist of asynchronous and irregularly sampled observations from heterogeneous numerical and textual channels. In healthcare, for example,…
FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning
Xing Han, Shravan Chaudhari, Tanvi Ranade +2
Real-world model deployment across multiple domains requires multimodal models to operate under two complementary regimes: (1) multi-task pretraining, tasks are co-available at des…
On the Invariance and Generality of Neural Scaling Laws
Xing Han, Ziyin Liu, Suchi Saria +1
Neural scaling laws establish a predictable relationship between model performance and data or compute, offering crucial guidance for resource allocation in new domains and tasks.…
Massively Multimodal Foundation Models: A Framework for Capturing Interactions with Specialized Mixture-of-Experts
Xing Han, Hsing-Huan Chung, Joydeep Ghosh +2
Modern applications increasingly involve many heterogeneous input streams, such as clinical sensors, wearable device data, imaging, and text, each with distinct measurement models,…
Improving Coverage in Combined Prediction Sets with Weighted p-values
Gina Wong, Drew Prinster, Suchi Saria +2
Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets. For complex scenarios involving multiple tria…