publications

* denotes equal contribution

An up-to-date list is available on Google Scholar.

PhD thesis

  1. Ph.D.
    Al-Shedivat, M. (2021). Principles of Learning in Multitask Settings: A Probabilistic Perspective. Carnegie Mellon University.

preprints

  1. arXiv
    A Field Guide to Federated Optimization
    Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Aguera y Arcas, B., Al-Shedivat, M., and others,
    arXiv preprint, 2021

conference & journal articles

2021

  1. EMNLP
    Knowledge-Aware Meta-learning for Low-Resource Text Classification
    Yao, H., Wu, Y.-X., Al-Shedivat, M., and Xing, E.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
  2. Progressive Generation of Long Text with Pretrained Language Models
    Tan, B., Yang, Z., Al-Shedivat, M., Xing, E., and Hu, Z.
    In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
  3. Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
    Al-Shedivat, M., Gillenwater, J., Xing, E., and Rostamizadeh, A.
    In International Conference on Learning Representations (ICLR), 2021
  4. AISTATS
    On Data Efficiency of Meta-learning
    Al-Shedivat, M., Li, L., Xing, E., and Talwalkar, A.
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021

2020

  1. Regularizing Black-box Models for Improved Interpretability
    In Advances in Neural Information Processing Systems (NeurIPS), 2020
  2. Contextual Explanation Networks
    Al-Shedivat, M., Dubey, A., and Xing, E. P.
    Journal of Machine Learning Research (JMLR), 2020

2019

  1. NAACL Full Oral
    Consistency by Agreement in Zero-shot Neural Machine Translation
    Al-Shedivat, M., and Parikh, A.P.
    In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019
  2. A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
    Mao, J.*, Foerster, J.*, Rocktäschel, T., Al-Shedivat, M., Farquhar, G., and Whiteson, S.
    In International Conference on Machine Learning (ICML), 2019

2018

  1. ICML Full Oral
    Learning Policy Representations in Multiagent Systems
    Grover, A., Al-Shedivat, M., Gupta, J.K., Burda, Y., and Edwards, H.
    In International Conference on Machine Learning (ICML), 2018
  2. ICML Full Oral
    DiCE: The Infinitely Differentiable Monte-Carlo Estimator
    Foerster, J.N., Farquhar, G.*, Al-Shedivat, M.*, Rocktäschel, T., Xing, E. P., and Whiteson, S.
    In International Conference on Machine Learning (ICML), 2018
  3. Learning with Opponent-Learning Awareness
    Foerster, J. N.*, Chen, R. Y.*, Al-Shedivat, M., Whiteson, S., Abbeel, P., and Mordatch, I.
    In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018
  4. ICLR Best Paper Award
    Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
    In International Conference on Learning Representations (ICLR), 2018

2017

  1. Learning Scalable Deep Kernels with Recurrent Structure
    Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., and Xing, E.
    Journal of Machine Learning Research (JMLR), 2017

2016

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

2015

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

2014

  1. Supervised Transfer Sparse Coding
    Al-Shedivat, M., Wang, J. J.-Y., Alzahrani, M., Huang, J. Z., and Gao, X.
    In AAAI conference on Artificial Intelligence, 2014

technical reports & short papers

  1. medRxiv
    Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning
    Lengerich, B.*, Al-Shedivat, M.*, Alavi, A., Williams, J., Labbaki, S., and Xing, E.
    medRxiv preprint, 2020
  2. arXiv
    Learning from Imperfect Annotations
    Platanios, E. A., Al-Shedivat, M., Xing, E., and Mitchell, T.
    arXiv preprint, 2020
  3. On the Complexity of Exploration in Goal-Driven Navigation
    Al-Shedivat, M.*, Lee, L.*, Salakhutdinov, R., and Xing, E.P.
    In Relational Representation Learning Workshop, NeurIPS, 2018
  4. Contextual Explanation Networks Enable Integrated Analysis Of Imaging And Genomic Data
    Lengerich, B.J., Al-Shedivat, M., Dubey, A., Alavi, A., Williams, J., and Xing, E.P.
    In 26th conference on Intelligent Systems for Molecular Biology (ISMB), 2018
  5. Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs
    Grover, A., Al-Shedivat, M., Gupta, J., Burda, Y., and Edwards, H.
    In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018
  6. NeurIPS Spotlight
    The Intriguing Properties of Model Explanations
    Al-Shedivat, M., Dubey, A., and Xing, E.P.
    In Symposium on Interpretable Machine Learning, NeurIPS, 2017
  7. NeurIPS Spotlight
    Personalized Survival Prediction with Contextual Explanation Networks
    Al-Shedivat, M., Dubey, A., and Xing, E.P.
    In Machine Learning for Healthcare Workshop, NeurIPS, 2017
  8. Scalable GP-LSTMs with Semi-Stochastic Gradients
    Al-Shedivat, M., Wilson, A.G., Saatchi, Y., Hu, Z., and Xing, E.P.
    In Bayesian Deep Learning Workshop, NeurIPS, 2016
  9. Learning Diverse Overcomplete Dictionaries via Determinantal Priors
    Al-Shedivat, M., Choe, Y.J., Spencer, N., and Xing, E.P.
    In Geometry in Machine Learning Workshop, ICML, 2016
  10. Neural Generative Models with Stochastic Synapses Capture Richer Representations
    Al-Shedivat, M., Neftci, E., and Cauwenberghs, G.
    In Computational and Systems Neuroscience (Cosyne), 2015
  11. FiO/LS
    Shaping of Femtosecond Laser Pulses with Plasmonic Crystals
    Shcherbakov, M., Vabishchevich, P., Zubjuk, V., Al-Shedivat, M., Dolgova, T., and Fedyanin, A.
    In Frontiers in Optics, 2013

other theses

  1. M.Sc.
    Al-Shedivat, M. (2015). Brain-inspired Stochastic Models and Implementations. KAUST.
  2. B.Sc.
    Аль-Шедиват, М. (2013). Фемтосекундная динамика преобразования поляризации света хиральными плазмонными метаматериалами. МГУ им. М.В. Ломоносова.