Machine learning and evolutionary techniques in interplanetary trajectory design. - Dario Izzo; Christopher Iliffe Sprague; Dharmesh Tailor (CoRR, 2018)

On the stability analysis of optimal state feedbacks as represented by deep neural models. - Dario Izzo; Dharmesh Tailor; Thomas Vasileiou (CoRR, 2018)

Unconscious Biases in Neural Populations Coding Multiple Stimuli. - Sander W. Keemink; Dharmesh Tailor; Mark C. W. van Rossum (Neural Comput., 2018)

Learning the optimal state-feedback via supervised imitation learning. - Dharmesh Tailor; Dario Izzo (CoRR, 2019)

On the Stability Analysis of Deep Neural Network Representations of an Optimal State Feedback. - Dario Izzo; Dharmesh Tailor; Thomas Vasileiou (IEEE Trans. Aerosp. Electron. Syst., 2021)

The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. - Peter Nickl; Lu Xu; Dharmesh Tailor; Thomas Möllenhoff; Mohammad Emtiyaz Khan (NeurIPS, 2023)

Exploiting Inferential Structure in Neural Processes. - Dharmesh Tailor; Mohammad Emtiyaz Khan; Eric T. Nalisnick (UAI, 2023)

Learning to Defer to a Population: A Meta-Learning Approach. - Dharmesh Tailor; Aditya Patra; Rajeev Verma; Putra Manggala; Eric T. Nalisnick (AISTATS, 2024)

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence. - Dharmesh Tailor; Alvaro H. C. Correia; Eric T. Nalisnick; Christos Louizos (ICLR, 2025)

On Equivariant Model Selection through the Lens of Uncertainty. - Putri A. van der Linden; Alexander Timans; Dharmesh Tailor; Erik J. Bekkers (CoRR, 2025)