BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. - Eleanor Batty; Matthew R. Whiteway; Shreya Saxena; Dan Biderman; Taiga Abe; Simon Musall; Winthrop Gillis; Jeffrey E. Markowitz; Anne Churchland; John P. Cunningham; Sandeep R. Datta; Scott W. Linderman; et al. (NeurIPS, 2019)

Bias-Free Scalable Gaussian Processes via Randomized Truncations. - Andres Potapczynski; Luhuan Wu; Dan Biderman; Geoff Pleiss; John P. Cunningham (ICML, 2021)

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. - Matthew R. Whiteway; Dan Biderman; Yoni Friedman; Mario Dipoppa; Estefany Kelly Buchanan; Anqi Wu; John Zhou; Niccolò Bonacchi; Nathaniel J. Miska; Jean-Paul Noel; Erica Rodriguez; Michael Schartner; et al. (PLoS Comput. Biol., 2021)

Towards a theory of learning dynamics in deep state space models. - Jakub Smékal; Jimmy T. H. Smith; Michael Kleinman; Dan Biderman; Scott W. Linderman (CoRR, 2024)

LoRA Learns Less and Forgets Less. - Dan Biderman; Jacob P. Portes; Jose Javier Gonzalez Ortiz; Mansheej Paul; Philip Greengard; Connor Jennings; Daniel King; Sam Havens; Vitaliy Chiley; Jonathan Frankle; Cody Blakeney; John Patrick Cunningham (Trans. Mach. Learn. Res., 2024)

Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models. - Avanika Narayan; Dan Biderman; Sabri Eyuboglu; Avner May; Scott W. Linderman; James Zou 001; Christopher Ré (CoRR, 2025)