The polynomial learning with errors problem and the smearing condition. - Liljana Babinkostova; Ariana Chin; Aaron Kirtland; Vladyslav Nazarchuk; Esther Plotnick (J. Math. Cryptol., 2022)

Inverse Scaling: When Bigger Isn't Better. - Ian R. McKenzie; Alexander Lyzhov; Michael Pieler; Alicia Parrish; Aaron Mueller; Ameya Prabhu; Euan McLean; Aaron Kirtland; Alexis Ross; Alisa Liu; Andrew Gritsevskiy; Daniel Wurgaft; et al. (Trans. Mach. Learn. Res., 2023)

An Unstructured Mesh Approach to Nonlinear Noise Reduction for Coupled Systems. - Aaron Kirtland; Jonah Botvinick-Greenhouse; Marianne Debrito; Megan Osborne; Casey Johnson; Robert Scott Martin; Samuel J. Araki; Daniel Eckhardt (SIAM J. Appl. Dyn. Syst., 2023)

Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. - Cameron Allen; Aaron Kirtland; Ruo Yu Tao; Sam Lobel; Daniel Scott; Nicholas Petrocelli; Omer Gottesman; Ronald Parr; Michael L. Littman; George Konidaris 001 (NeurIPS, 2024)

REBUS: A Robust Evaluation Benchmark of Understanding Symbols. - Andrew Gritsevskiy; Arjun Panickssery; Aaron Kirtland; Derik Kauffman; Hans Gundlach; Irina Gritsevskaya; Joe Cavanagh; Jonathan Chiang; Lydia La Roux; Michelle Hung (CoRR, 2024)