8 papers
DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing
Sagnik Nandy, Samriddha Lahiry, Pragya Sur +1
Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance. A central challenge is determining the fusion granularity ac…
Multi-layer Cross-attention is Provably Optimal for Multi-modal In-context Learning
Nicholas Barnfield, Subhabrata Sen, Pragya Sur
Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural networks. However, existing results focus e…
Characterizing Finite-Dimensional Posterior Marginals in High-Dimensional GLMs via Leave-One-Out
Manuel Sáenz, Pragya Sur
We investigate Bayes posterior distributions in high-dimensional generalized linear models (GLMs) under the proportional asymptotics regime, where the number of features and sample…
Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
Yufan Li, Pragya Sur
We study the fundamental problem of calibrating a linear binary classifier of the form , where the feature vector is Gaussian, is a link function, and $\…
ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data
Kevin Luo, Yufan Li, Pragya Sur
Two key tasks in high-dimensional regularized regression are tuning the regularization strength for accurate predictions and estimating the out-of-sample risk. It is known that the…
Predictive Inference in Multi-environment Scenarios
John C. Duchi, Suyash Gupta, Kuanhao Jiang +1
We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We investigate two types of coverage suitable f…