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
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
Directional resolution: The davis-putnam procedure, revisited
R Dechter, I Rish
Principles of knowledge representation and reasoning, 134-145, 1994
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
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
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
Resolution versus search: Two strategies for SAT
I Rish, R Dechter
Journal of Automated Reasoning 24 (1-2), 225-275, 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
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
Optimizing probe selection for fault localization
M Brodie, I Rish, S Ma
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
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
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
Summarizing CSP hardness with continuous probability distributions
D Frost, I Rish, L Vila
AAAI/IAAI, 327-333, 1997
A scheme for approximating probabilistic inference
R Dechter, I Rish
arXiv preprint arXiv:1302.1534, 2013
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