Vogelstein, Joshua T.

15 publications

TMLR 2025 Simple Calibration via Geodesic Kernels Jayanta Dey, Haoyin Xu, Ashwin De Silva, Joshua T Vogelstein

TMLR 2024 Independence Testing for Temporal Data Cencheng Shen, Jaewon Chung, Ronak Mehta, Ting Xu, Joshua T Vogelstein

NeurIPS 2024 Prospective Learning: Learning for a Dynamic Future Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, Pratik Chaudhari

NeurIPSW 2024 Prospective Learning: Learning for a Dynamic Future Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T Vogelstein, Pratik Chaudhari

ICML 2023 Polarity Is All You Need to Learn and Transfer Faster Qingyang Wang, Michael Alan Powell, Eric W Bridgeford, Ali Geisa, Joshua T Vogelstein

CoLLAs 2023 Prospective Learning: Principled Extrapolation to the Future Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

ICML 2023 The Value of Out-of-Distribution Data Ashwin De Silva, Rahul Ramesh, Carey Priebe, Pratik Chaudhari, Joshua T Vogelstein

ICCV 2023 Why Do Networks Have Inhibitory/negative Connections? Qingyang Wang, Mike A. Powell, Ali Geisa, Eric Bridgeford, Carey E. Priebe, Joshua T. Vogelstein

NeurIPSW 2022 The Value of Out-of-Distribution Data Ashwin De Silva, Rahul Ramesh, Carey Priebe, Pratik Chaudhari, Joshua T Vogelstein

JMLR 2021 Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein

MLOSS 2021 Mvlearn: Multiview Machine Learning in Python Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

JMLR 2020 Sparse Projection Oblique Randomer Forests Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

JMLR 2019 GraSPy: Graph Statistics in Python Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein

NeurIPS 2013 Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T Vogelstein, David B Dunson

NeurIPS 2013 Real-Time Inference for a Gamma Process Model of Neural Spiking David E Carlson, Vinayak Rao, Joshua T Vogelstein, Lawrence Carin