| Non-Gaussian information from weak lensing data via deep learning A Gupta, JMZ Matilla, D Hsu, Z Haiman Physical Review D 97 (10), 103515, 2018 | 118 | 2018 |
| Choroidal vascularity index in retinitis pigmentosa: an OCT study R Tan, R Agrawal, S Taduru, A Gupta, K Vupparaboina, J Chhablani Ophthalmic Surgery, Lasers and Imaging Retina 49 (3), 191-197, 2018 | 48 | 2018 |
| Measurements and modeling of near-surface radio propagation in glacial ice and implications for neutrino experiments C Deaconu, AG Vieregg, SA Wissel, J Bowen, S Chipman, A Gupta, ... Physical Review D 98 (4), 043010, 2018 | 43 | 2018 |
| Choroidal structural changes and vascularity index in stargardt disease on swept source optical coherence tomography D Ratra, R Tan, D Jaishankar, N Khandelwal, A Gupta, J Chhablani, ... Retina 38 (12), 2395-2400, 2018 | 42 | 2018 |
| Optical coherence tomography angiography characterisation of Best disease and associated choroidal neovascularisation A Guduru, A Gupta, M Tyagi, S Jalali, J Chhablani British Journal of Ophthalmology 102 (4), 444-447, 2018 | 38 | 2018 |
| Do dark matter halos explain lensing peaks? JMZ Matilla, Z Haiman, D Hsu, A Gupta, A Petri Physical Review D 94 (8), 083506, 2016 | 30 | 2016 |
| Comparative analysis of autofluorescence and OCT angiography in Stargardt disease A Guduru, M Lupidi, A Gupta, S Jalali, J Chhablani British Journal of Ophthalmology 102 (9), 1204-1207, 2018 | 28 | 2018 |
| A representation learning perspective on the importance of train-validation splitting in meta-learning N Saunshi, A Gupta, W Hu International Conference on Machine Learning, 9333-9343, 2021 | 23 | 2021 |
| Quantitative assessment of the choriocapillaris in patients with retinitis pigmentosa and in healthy individuals using OCT angiography A Guduru, M Al-Sheikh, A Gupta, H Ali, S Jalali, J Chhablani Ophthalmic Surgery, Lasers and Imaging Retina 49 (10), e122-e128, 2018 | 15 | 2018 |
| Understanding influence functions and datamodels via harmonic analysis N Saunshi, A Gupta, M Braverman, S Arora The Eleventh International Conference on Learning Representations, 2022 | 12 | 2022 |
| On Predicting Generalization using GANs Y Zhang, A Gupta, N Saunshi, S Arora ICLR 2022, 2021 | 12 | 2021 |
| Parameter identification in Markov chain choice models A Gupta, D Hsu Theoretical Computer Science 808, 99-107, 2020 | 10 | 2020 |
| A simple saliency method that passes the sanity checks A Gupta, S Arora arXiv preprint arXiv:1905.12152, 2019 | 8 | 2019 |
| Choroidal hyper-reflective foci and vascularity in retinal dystrophy D Hanumunthadu, MA Rasheed, A Goud, A Gupta, KK Vupparaboina, ... Indian Journal of Ophthalmology 68 (1), 130-133, 2020 | 6 | 2020 |
| Skill-Mix: A flexible and expandable family of evaluations for AI models D Yu, S Kaur, A Gupta, J Brown-Cohen, A Goyal, S Arora arXiv preprint arXiv:2310.17567, 2023 | 5 | 2023 |
| New definitions and evaluations for saliency methods: Staying intrinsic, complete and sound A Gupta, N Saunshi, D Yu, K Lyu, S Arora Advances in Neural Information Processing Systems 35, 33120-33133, 2022 | 5 | 2022 |
| Parameter identification in Markov chain choice models A Gupta, D Hsu International Conference on Algorithmic Learning Theory, 330-340, 2017 | 4 | 2017 |
| Online nonstochastic model-free reinforcement learning U Ghai, A Gupta, W Xia, K Singh, E Hazan Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Color Doppler studies of the transplant renal artery in patients with allograft rejection--correlation with graft biopsy. DS Rana, AK Bhalla, A Gupta, KK Kapoor, H Jauhari, PK Khanna Transplantation Proceedings 24 (5), 1886-1886, 1992 | 3 | 1992 |
| ATG induction in renal transplantation–A single center experience: 1961 D Khullar, J Gaikwad, M Malik, S Ojha, A Gupta, AK Bhalla, DS Rana, ... Transplantation 86 (2S), 645, 2008 | 1 | 2008 |
