Karen Schroeder — accepted AI-conference papers

2026

Benchmarking World-Model Learning with Environment-Level Queries

Archana Warrier, Dat Nguyen, Michelangelo Naim, Moksh Jain, Yichao Liang, Karen Schroeder +5

ICML 2026poster

World models are central to building AI agents capable of flexible reasoning and planning. Yet current evaluations (i) test only properties measurable from observed interactions within an environment, such as next-frame prediction or task return, and (ii) do not test whether a learned model supports…

Cited by 0 Source ↗Scholar ↗

2019

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data

Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark Churchland, John P. Cunningham

NeurIPS 2019poster

Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random func…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗