Irina Rish
Irina Rish
University of Montreal / Mila -Quebec AI Institute
Verified email at - Homepage
Cited by
Cited by
An empirical study of the naive Bayes classifier
I Rish
IJCAI 2001 workshop on empirical methods in artificial intelligence 3 (22 …, 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
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
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
Mini-buckets: A general scheme for bounded inference
R Dechter, I Rish
Journal of the ACM (JACM) 50 (2), 107-153, 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
Sparse modeling: theory, algorithms, and applications
I Rish, G Grabarnik
CRC press, 2014
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
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
Directional resolution: The davis-putnam procedure, revisited
R Dechter, I Rish
Principles of knowledge representation and reasoning, 134-145, 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
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
Resolution versus search: Two strategies for SAT
I Rish, R Dechter
Journal of Automated Reasoning 24 (1), 225-275, 2000
A survey on practical applications of multi-armed and contextual bandits
D Bouneffouf, I Rish
arXiv preprint arXiv:1904.10040, 2019
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
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
Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning
M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, ...
Advances in Neural Information Processing Systems 33, 16532-16545, 2020
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
Using language processing and speech analysis for the identification of psychosis and other disorders
CM Corcoran, GA Cecchi
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5 (8), 770-779, 2020
Active sampling collaborative prediction method for end-to-end performance prediction
I Rish, GJ Tesauro
US Patent 7,640,224, 2009
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