| JuMP: A modeling language for mathematical optimization I Dunning, J Huchette, M Lubin SIAM Review 59 (2), 295-320, 2017 | 1905 | 2017 |
| Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures L Espeholt, H Soyer, R Munos, K Simonyan, V Mnih, T Ward, Y Doron, ... International Conference on Machine Learning, 1407-1416, 2018 | 1660 | 2018 |
| Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castañeda, ... Science 364 (6443), 859-865, 2019 | 978 | 2019 |
| Population Based Training of Neural Networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 877 | 2017 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 424 | 2020 |
| Computing in Operations Research using Julia M Lubin, I Dunning INFORMS Journal on Computing, 2013 | 410 | 2013 |
| PuLP: a linear programming toolkit for python S Mitchell, M O’Sullivan, I Dunning The University of Auckland, Auckland, New Zealand, 2011 | 403 | 2011 |
| Inequity aversion improves cooperation in intertemporal social dilemmas E Hughes, JZ Leibo, M Phillips, K Tuyls, E Dueñez-Guzman, ... Advances in Neural Information Processing Systems, 3326-3336, 2018 | 252 | 2018 |
| Bayesian action decoder for deep multi-agent reinforcement learning J Foerster, F Song, E Hughes, N Burch, I Dunning, S Whiteson, ... International Conference on Machine Learning, 1942-1951, 2019 | 180 | 2019 |
| Multistage Robust Mixed-Integer Optimization with Adaptive Partitions D Bertsimas, I Dunning Operations Research 64 (4), 980-998, 2016 | 162 | 2016 |
| Reformulation versus cutting-planes for robust optimization D Bertsimas, I Dunning, M Lubin Computational Management Science 13 (2), 195-217, 2016 | 144 | 2016 |
| What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO I Dunning, S Gupta, J Silberholz INFORMS Journal on Computing, 2018 | 134 | 2018 |
| Extended formulations in mixed integer conic quadratic programming JP Vielma, I Dunning, J Huchette, M Lubin Mathematical Programming Computation 9 (3), 369-418, 2017 | 53 | 2017 |
| Malthusian reinforcement learning JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ... arXiv preprint arXiv:1812.07019, 2018 | 47 | 2018 |
| OpenSolver: Open source optimisation for Excel AJ Mason, I Dunning 45th Annual Conference of the ORSNZ, 2010 | 46 | 2010 |
| Learning Fast Optimizers for Contextual Stochastic Integer Programs V Nair, K Dvijotham, I Dunning, O Vinyals UAI, 2018 | 25 | 2018 |
| Advances in robust and adaptive optimization: algorithms, software, and insights IR Dunning Massachusetts Institute of Technology, 2016 | 24 | 2016 |
| Deep reinforcement learning with fast updating recurrent neural networks and slow updating recurrent neural networks IR Dunning, W Czarnecki, ME Jaderberg US Patent 10,872,293, 2020 | 15 | 2020 |
| Dippy: A Simplified Interface for Advanced Mixed-integer Programming Q Lim, C Walker, I Dunning, S Mitchell Department of Engineering Science, School of Engineering, University of Auckland, 2012 | 11 | 2012 |
| Relative robust and adaptive optimization D Bertsimas, I Dunning INFORMS Journal on Computing 32 (2), 408-427, 2020 | 9 | 2020 |
Miles LubinHudson River TradingVerified email at hudson-trading.com
