Publications (114)
Global Optimality in Bivariate Gradient-based DAG Learning
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant i…
Automated Dependence Plots
David I. Inouye, Liu Leqi, Joon Sik Kim +2
In practical applications of machine learning, it is necessary to look beyond standard metrics such as test accuracy in order to validate various qualitative properties of a model.…
Robust Nonparametric Regression under Huber's -contamination Model
Simon S. Du, Yining Wang, Sivaraman Balakrishnan +2
We consider the non-parametric regression problem under Huber's -contamination model, in which an fraction of observations are subject to arbitrary adversarial noise. We f…
FILM: Following Instructions in Language with Modular Methods
So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar +2
Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level langua…
Improving Compositional Generalization in Classification Tasks via Structure Annotations
Juyong Kim, Pradeep Ravikumar, Joshua Ainslie +1
Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to g…
Context-Informed Ship Trajectory Prediction via Conditional Attention
Yuan Guan, Chandler Squires, Timothy Hu +1
The paper introduces the Conditional Informer, a Transformer-based model that predicts ship trajectories by explicitly conditioning vessel states on environmental contexts using a…