preprints & working papers

  1. Contextual Explanation Networks Al-Shedivat, M., Dubey, A., Xing, E.P. In submission, 2018 [abstract] [arXiv] [pdf] [press: NLP Highlights]

conference & journal articles

2018

  1. [ICML]
    Learning Policy Representations in Multiagent Systems Grover, A., Al-Shedivat, M., Gupta, J.K., Burda, Y., Edwards, H. International Conference on Machine Learning (ICML), 2018 [abstract] [arXiv] [pdf] [supp] [poster]
  2. [ICML]
    DiCE: The Infinitely Differentiable Monte-Carlo Estimator Foerster, J.N., Farquhar, G.*, Al-Shedivat, M.*, Rocktäschel, T., Xing, E.P., Whiteson, S. International Conference on Machine Learning (ICML), 2018 [abstract] [arXiv] [pdf] * equal contribution
  3. [AAMAS]
    Learning with Opponent-Learning Awareness Foerster, J. N.*, Chen, R. Y.*, Al-Shedivat, M., Whiteson, S., Abbeel, P., Mordatch, I. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018 [abstract] [arXiv] [blog] [pdf]
  4. [ICLR]
    Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments Al-Shedivat, M., Bansal, T., Burda, Y., Sutskever, I., Mordatch, I., Abbeel, P. International Conference on Learning Representations (ICLR), Best Paper Award, 2018 [abstract] [arXiv] [blog] [code] [poster] [website] [press: WIRED, Quartz]

2017

  1. [JMLR]
    Learning Scalable Deep Kernels with Recurrent Structure Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., Xing, E.P. Journal of Machine Learning Research (JMLR), 2017 [abstract] [arXiv] [code] [pdf]

2016

  1. [NIPS]
    Learning HMMs with Nonparametric Emissions via Decompositions of Continuous Matrices Kandasamy, K.*, Al-Shedivat, M.*, Xing, E.P. Advances in Neural Information Processing Systems (NIPS), 2016 [abstract] [code] [pdf] [poster] * equal contribution
  2. [ICML]
    ADIOS: Architectures Deep In Output Space Cissé, M., Al-Shedivat, M., Bengio, S. International Conference on Machine Learning (ICML), 2016 [abstract] [code] [pdf] [supp] [poster]
  3. [Frontiers]
    Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, M., Cauwenberghs, G. Frontiers in Neuroscience, 2016 [abstract] [html] [pdf]

2015

  1. [TNANO]
    Stochasticity Modeling in Memristors Naous, R., Al-Shedivat, M., Salama, K.N. IEEE Transactions on Nanotechnology, 2015 [abstract] [pdf]
  2. [JETCAS]
    Memristors Empower Spiking Neurons With Stochasticity Al-Shedivat, M., Naous, R., Cauwenberghs, G., Salama, K. N. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2015 [abstract] [pdf]
  3. [NER]
    Inherently Stochastic Spiking Neurons for Probabilistic Neural Computation Al-Shedivat, M., Naous, R., Neftci, E., Cauwenberghs, G., Salama, K. N. International IEEE/EMBS Conference on Neural Engineering (NER), 2015 [abstract] [pdf] [poster]
  4. [ICLR]
    Learning Non-deterministic Representations with Energy-based Ensembles Al-Shedivat, M., Neftci, E., Cauwenberghs, G. International Conference on Learning Representations (ICLR), workshop track, 2015 [abstract] [code] [pdf]

2014

  1. [AAAI]
    Supervised Transfer Sparse Coding Al-Shedivat, M., Wang, J. J.-Y., Alzahrani, M., Huang, J. Z., Gao, X. AAAI conference on Artificial Intelligence, 2014 [abstract] [code] [pdf] [poster]

short papers & extended abstracts

  1. [AAMAS]
    Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs Grover, A., Al-Shedivat, M., Gupta, J., Burda, Y., Edwards, H. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018 [pdf]
  2. [ICLR]
    DiCE: The Infinitely Differentiable Monte-Carlo Estimator Foerster, J.N., Farquhar, G.*, Al-Shedivat, M.*, Rocktäschel, T., Xing, E.P., Whiteson, S. International Conference on Learning Representations (ICLR), Workshop, 2018 [abstract] [arXiv] [poster]
  3. [NIPS]
    The Intriguing Properties of Model Explanations Al-Shedivat, M., Dubey, A., Xing, E.P. Symposium on Interpretable Machine Learning, NIPS, 2017 [arXiv] [pdf] [poster] [spotlight]
  4. [NIPS]
    Personalized Survival Prediction with Contextual Explanation Networks Al-Shedivat, M., Dubey, A., Xing, E.P. Machine Learning for Healthcare Workshop, NIPS, 2017 [arXiv] [pdf] [poster] [spotlight]
  5. [NIPS]
    Scalable GP-LSTMs with Semi-Stochastic Gradients Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., Xing, E.P. Bayesian Deep Learning Workshop, NIPS, 2016 [arXiv] [code] [poster]
  6. [ICML]
    Learning Diverse Overcomplete Dictionaries via Determinantal Priors Al-Shedivat, M., Choe, Y.J., Spencer, N., Xing, E.P. Geometry in Machine Learning Workshop, ICML, 2016 [poster]
  7. [Cosyne]
    Neural Generative Models with Stochastic Synapses Capture Richer Representations Al-Shedivat, M., Neftci, E., Cauwenberghs, G. Computational and Systems Neuroscience (Cosyne), 2015 [code] [pdf] [poster]
  8. [FiO/LS]
    Shaping of Femtosecond Laser Pulses with Plasmonic Crystals Shcherbakov, M., Vabishchevich, P., Zubjuk, V., Al-Shedivat, M., Dolgova, T., Fedyanin, A. Frontiers in Optics, 2013

theses

  1. [M.Sc.]
    Al-Shedivat, M. “Brain-Inspired Stochastic Models and Implementations.” KAUST, 2015. [pdf]
  2. [B.Sc.]
    Аль-Шедиват, Маруан. “Фемтосекундная Динамика Преобразования Поляризации Света Хиральными Плазмонными Метаматериалами.” МГУ им. М.В. Ломоносова, 2013. [pdf]