| Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile GE Mena, PP Martinez, AS Mahmud, PA Marquet, CO Buckee, ... Science 372 (6545), eabg5298, 2021 | 168 | 2021 |
| Learning Latent Permutations With Gumbel-Sinkhorn Networks G Mena, D Belanger, S Linderman, J Snoek The Sixth International Conference on Learning Representations (ICLR), 2018 | 148 | 2018 |
| NeuroPAL: a multicolor atlas for whole-brain neuronal identification in C. elegans E Yemini, A Lin, A Nejatbakhsh, E Varol, R Sun, GE Mena, ADT Samuel, ... Cell 184 (1), 272-288. e11, 2021 | 100* | 2021 |
| Statistical Bounds for Entropic Optimal Transport: Sample Complexity and the Central Limit Theorem G Mena, J Weed Advances in Neural Information Processing Systems 32, 2019 | 84 | 2019 |
| Reparameterizing The Birkhoff Polytope for Variational Permutation Inference SW Linderman, GE Mena, H Cooper, L Paninski, JP Cunningham The 21nd International Conference on Artificial Intelligence and Statistics …, 2017 | 43 | 2017 |
| Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays GE Mena, LE Grosberg, S Madugula, P Hottowy, A Litke, J Cunningham, ... PLoS computational biology 13 (11), e1005842, 2017 | 37 | 2017 |
| Optimization of electrical stimulation for a high-fidelity artificial retina NP Shah, S Madugula, L Grosberg, G Mena, P Tandon, P Hottowy, A Sher, ... 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 714-718, 2019 | 23 | 2019 |
| Sinkhorn Networks: Using Optimal Transport Techniques to Learn Permutations G Mena, D Belanger, G Muñoz, J Snoek NIPS workshop on Optimal Transport & Machine Learning, 2017 | 12 | 2017 |
| A unified framework for de-duplication and population size estimation (contributed discussion) N Ju, N Biswas, PE Jacob, G Mena, J O'Leary, E Pompe Bayesian Analysis 15 (2), 2020 | 11* | 2020 |
| Statistical Atlas of C. elegans Neurons E Varol, A Nejatbakhsh, R Sun, G Mena, E Yemini, O Hobert, L Paninski International Conference on Medical Image Computing and Computer-Assisted …, 2020 | 8 | 2020 |
| On Quadrature Methods for Refractory Point Process Likelihoods G Mena, L Paninski Neural computation 26 (12), 2790-2797, 2014 | 7 | 2014 |
| Sinkhorn EM: an expectation-maximization algorithm based on entropic optimal transport G Mena, A Nejatbakhsh, E Varol, J Niles-Weed arXiv preprint arXiv:2006.16548, 2020 | 4 | 2020 |
| Large-scale Multi Electrode Array Spike Sorting Algorithm Introducing Concurrent Recording and Stimulation G Mena, L Grosberg, F Kellison-Linn, E Chichilnisky, L Paninski NIPS workshop on Statistical Methods for Understanding Neural Systems, 2015 | 4 | 2015 |
| Sinkhorn Permutation Variational Marginal Inference G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski 2nd Symposium on Advances in Approximate Bayesian Inference, 2019 | 2 | 2019 |
| Toward Bayesian Permutation Inference for Identifying Neurons in C. elegans. G Mena, S Linderman, D Belanger, J Snoek, J Cunningham, L Paninski NIPS workshop on Worm's Neural Information Processing (WNIP)., 2017 | 2 | 2017 |
| The unequal impact of the COVID-19 pandemic on life expectancy across Chile GE Mena, JM Aburto medRxiv, 2021 | | 2021 |
| Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data GE Mena Columbia University, 2018 | | 2018 |
| The unequal impact of the COVID-19 pandemic on life expectancy across Chile (preprint) G Mena, JM Aburto | | |
| Optimal Transport: Sample Complexity and the Central Limit Theorem G Mena, J Niles-Weed | | |
| Reparameterizing the Birkhoff Polytope for Variational Permutation Inference: Supplementary Material SW Linderman, GE Mena, H Cooper, L Paninski, JP Cunningham | | |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
