A partition-based similarity for classification distributions. - Hayden S. Helm; Ronak D. Mehta; Brandon Duderstadt; Weiwei Yang 004; Christopher M. White; Ali Geisa; Joshua T. Vogelstein; Carey E. Priebe (CoRR, 2020)

Inducing a hierarchy for multi-class classification problems. - Hayden S. Helm; Weiwei Yang 004; Sujeeth Bharadwaj; Kate Lytvynets; Oriana Riva; Christopher M. White; Ali Geisa; Carey E. Priebe (CoRR, 2021)

Towards a theory of out-of-distribution learning. - Ali Geisa; Ronak D. Mehta; Hayden S. Helm; Jayanta Dey; Eric Eaton; Carey E. Priebe; Joshua T. Vogelstein (CoRR, 2021)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

Out-of-distribution Detection Using Kernel Density Polytopes. - Jayanta Dey; Ashwin De Silva; Will LeVine; Jong M. Shin; Haoyin Xu; Ali Geisa; Tiffany Chu; Leyla Isik; Joshua T. Vogelstein (CoRR, 2022)

Why Do Networks Need Negative Weights? - Qingyang Wang 002; Michael A. Powell; Ali Geisa; Eric Bridgeford; Joshua T. Vogelstein (CoRR, 2022)

Why do networks have inhibitory/negative connections? - Qingyang Wang 002; Michael A. Powell; Ali Geisa; Eric Bridgeford; Carey E. Priebe; Joshua T. Vogelstein (ICCV, 2023)

Polarity Is All You Need to Learn and Transfer Faster. - Qingyang Wang 002; Michael Alan Powell; Eric W. Bridgeford; Ali Geisa; Joshua T. Vogelstein (ICML, 2023)