| A Markov chain approximation to choice modeling J Blanchet, G Gallego, V Goyal Operations Research 64 (4), 886-905, 2016 | 370 | 2016 |
| MNL-bandit: A dynamic learning approach to assortment selection S Agrawal, V Avadhanula, V Goyal, A Zeevi Operations Research 67 (5), 1453-1485, 2019 | 240* | 2019 |
| On the power and limitations of affine policies in two-stage adaptive optimization D Bertsimas, V Goyal Mathematical programming 134 (2), 491-531, 2012 | 239 | 2012 |
| Near-optimal algorithms for capacity constrained assortment optimization A Désir, V Goyal, J Zhang Available at SSRN 2543309, 2014 | 142 | 2014 |
| Constrained assortment optimization under the Markov chain–based choice model A Désir, V Goyal, D Segev, C Ye Management Science 66 (2), 698-721, 2020 | 138* | 2020 |
| Robust markov decision processes: Beyond rectangularity V Goyal, J Grand-Clement Mathematics of Operations Research 48 (1), 203-226, 2023 | 130* | 2023 |
| Thompson sampling for the mnl-bandit S Agrawal, V Avadhanula, V Goyal, A Zeevi Conference on learning theory, 76-78, 2017 | 129 | 2017 |
| Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand V Goyal, R Levi, D Segev Operations Research 64 (1), 219-235, 2016 | 127 | 2016 |
| On the power of robust solutions in two-stage stochastic and adaptive optimization problems D Bertsimas, V Goyal Mathematics of Operations Research 35 (2), 284-305, 2010 | 113 | 2010 |
| How to pay, come what may: Approximation algorithms for demand-robust covering problems K Dhamdhere, V Goyal, R Ravi, M Singh 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05), 367-376, 2005 | 102 | 2005 |
| MIP reformulations of the probabilistic set covering problem A Saxena, V Goyal, MA Lejeune Mathematical programming 121 (1), 1-31, 2010 | 87 | 2010 |
| A geometric characterization of the power of finite adaptability in multistage stochastic and adaptive optimization D Bertsimas, V Goyal, XA Sun Mathematics of Operations Research 36 (1), 24-54, 2011 | 85 | 2011 |
| Online assortment optimization with reusable resources XY Gong, V Goyal, GN Iyengar, D Simchi-Levi, R Udwani, S Wang Management Science 68 (7), 4772-4785, 2022 | 79 | 2022 |
| Capacitated assortment optimization: Hardness and approximation A Désir, V Goyal, J Zhang Operations Research 70 (2), 893-904, 2022 | 71 | 2022 |
| A PTAS for the chance-constrained knapsack problem with random item sizes V Goyal, R Ravi Operations Research Letters 38 (3), 161-164, 2010 | 68 | 2010 |
| A tight characterization of the performance of static solutions in two-stage adjustable robust linear optimization D Bertsimas, V Goyal, BY Lu Mathematical Programming 150 (2), 281-319, 2015 | 66 | 2015 |
| Sparse process flexibility designs: Is the long chain really optimal? A Désir, V Goyal, Y Wei, J Zhang Operations Research 64 (2), 416-431, 2016 | 64 | 2016 |
| Shapley meets uniform: An axiomatic framework for attribution in online advertising R Singal, O Besbes, A Desir, V Goyal, G Iyengar The world wide web conference, 1713-1723, 2019 | 59 | 2019 |
| Online allocation of reusable resources: Achieving optimal competitive ratio V Goyal, G Iyengar, R Udwani arXiv preprint arXiv:2002.02430, 2020 | 50* | 2020 |
| Mallows-smoothed distribution over rankings approach for modeling choice A Désir, V Goyal, S Jagabathula, D Segev Operations Research 69 (4), 1206-1227, 2021 | 49* | 2021 |
Antoine DesirINSEADZweryfikowany adres z insead.edu
