| An empirical study of the naive Bayes classifier I Rish IJCAI 2001 workshop on empirical methods in artificial intelligence 3 (22 …, 2001 | 5102 | 2001 |
| Learning representations from EEG with deep recurrent-convolutional neural networks P Bashivan, I Rish, M Yeasin, N Codella arXiv preprint arXiv:1511.06448, 2015 | 782 | 2015 |
| Learning to learn without forgetting by maximizing transfer and minimizing interference M Riemer, I Cases, R Ajemian, M Liu, I Rish, Y Tu, G Tesauro arXiv preprint arXiv:1810.11910, 2018 | 633 | 2018 |
| Critical event prediction for proactive management in large-scale computer clusters RK Sahoo, AJ Oliner, I Rish, M Gupta, JE Moreira, S Ma, R Vilalta, ... Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003 | 382 | 2003 |
| Mini-buckets: A general scheme for bounded inference R Dechter, I Rish Journal of the ACM (JACM) 50 (2), 107-153, 2003 | 290 | 2003 |
| Improving network robustness by edge modification A Beygelzimer, G Grinstein, R Linsker, I Rish Physica A: Statistical Mechanics and its Applications 357 (3-4), 593-612, 2005 | 244 | 2005 |
| Adaptive diagnosis in distributed systems I Rish, M Brodie, S Ma, N Odintsova, A Beygelzimer, G Grabarnik, ... IEEE Transactions on neural networks 16 (5), 1088-1109, 2005 | 244 | 2005 |
| Sparse modeling: theory, algorithms, and applications I Rish, G Grabarnik CRC press, 2014 | 241 | 2014 |
| Directional resolution: The davis-putnam procedure, revisited R Dechter, I Rish Principles of knowledge representation and reasoning, 134-145, 1994 | 233 | 1994 |
| Prediction and interpretation of distributed neural activity with sparse models MK Carroll, GA Cecchi, I Rish, R Garg, AR Rao NeuroImage 44 (1), 112-122, 2009 | 215 | 2009 |
| Towards continual reinforcement learning: A review and perspectives K Khetarpal, M Riemer, I Rish, D Precup Journal of Artificial Intelligence Research 75, 1401-1476, 2022 | 174 | 2022 |
| Resolution versus search: Two strategies for SAT I Rish, R Dechter Journal of Automated Reasoning 24 (1-2), 225-275, 2000 | 167 | 2000 |
| An analysis of data characteristics that affect naive Bayes performance I Rish, J Hellerstein, J Thathachar IBM TJ Watson Research Center 30, 1-8, 2001 | 150 | 2001 |
| Invariance principle meets information bottleneck for out-of-distribution generalization K Ahuja, E Caballero, D Zhang, JC Gagnon-Audet, Y Bengio, I Mitliagkas, ... Advances in Neural Information Processing Systems 34, 3438-3450, 2021 | 149 | 2021 |
| A survey on practical applications of multi-armed and contextual bandits D Bouneffouf, I Rish arXiv preprint arXiv:1904.10040, 2019 | 148 | 2019 |
| Real-time problem determination in distributed systems using active probing I Rish, M Brodie, N Odintsova, S Ma, G Grabarnik 2004 IEEE/IFIP Network Operations and Management Symposium (IEEE Cat. No …, 2004 | 137 | 2004 |
| Online fast adaptation and knowledge accumulation: a new approach to continual learning M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, L Caccia, ... arXiv preprint arXiv:2003.05856, 2020 | 127* | 2020 |
| Survey on applications of multi-armed and contextual bandits D Bouneffouf, I Rish, C Aggarwal 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | 123 | 2020 |
| Optimizing probe selection for fault localization M Brodie, I Rish, S Ma | 106 | 2001 |
| Intelligent probing: A cost-effective approach to fault diagnosis in computer networks M Brodie, I Rish, S Ma IBM systems journal 41 (3), 372-385, 2002 | 101 | 2002 |
Guillermo CecchiIBM ResearchVerified email at us.ibm.com
