Search Results - Babanezhad, Reza

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  1. 1
    A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage

    A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage by Zolaktaf, Zainab, Babanezhad, Reza, Pottinger, Rachel

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  2. 2
    SVRG meets AdaGrad: painless variance reduction

    SVRG meets AdaGrad: painless variance reduction by Dubois-Taine, Benjamin, Vaswani, Sharan, Babanezhad, Reza, Schmidt, Mark, Lacoste-Julien, Simon

    Published in Machine learning
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  3. 3
    (Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum

    (Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum by Dang, Anh, Babanezhad, Reza, Vaswani, Sharan

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  4. 4
    Fast Online Node Labeling for Very Large Graphs

    Fast Online Node Labeling for Very Large Graphs by Zhou, Baojian, Sun, Yifan, Babanezhad, Reza

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  5. 5
    Geometry-Aware Universal Mirror-Prox

    Geometry-Aware Universal Mirror-Prox by Babanezhad, Reza, Lacoste-Julien, Simon

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  6. 6
    Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent

    Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent by Vaswani, Sharan, Dubois-Taine, Benjamin, Babanezhad, Reza

    Published in arXiv.org
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  7. 7
    Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

    Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees by Vaswani, Sharan, Kazemi, Amirreza, Babanezhad, Reza, Nicolas Le Roux

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  8. 8
    Fast Convergence of Softmax Policy Mirror Ascent

    Fast Convergence of Softmax Policy Mirror Ascent by Asad, Reza, Babanezhad, Reza, Laradji, Issam, Nicolas Le Roux, Vaswani, Sharan

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  9. 9
    M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning

    M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning by Laradji, Issam, Babanezhad, Reza

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  10. 10
    Iterative Methods via Locally Evolving Set Process

    Iterative Methods via Locally Evolving Set Process by Zhou, Baojian, Sun, Yifan, Harikandeh, Reza Babanezhad, Guo, Xingzhi, Yang, Deqing, Xiao, Yanghua

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  11. 11
    Towards Painless Policy Optimization for Constrained MDPs

    Towards Painless Policy Optimization for Constrained MDPs by Jain, Arushi, Vaswani, Sharan, Babanezhad, Reza, Szepesvari, Csaba, Precup, Doina

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  12. 12
    Promoting Exploration in Memory-Augmented Adam using Critical Momenta

    Promoting Exploration in Memory-Augmented Adam using Critical Momenta by Malviya, Pranshu, Mordido, Gonçalo, Baratin, Aristide, Harikandeh, Reza Babanezhad, Huang, Jerry, Lacoste-Julien, Simon, Pascanu, Razvan, Chandar, Sarath

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  13. 13
    Semantics Preserving Adversarial Learning

    Semantics Preserving Adversarial Learning by Ousmane Amadou Dia, Barshan, Elnaz, Babanezhad, Reza

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  14. 14
    Target-based Surrogates for Stochastic Optimization

    Target-based Surrogates for Stochastic Optimization by Jonathan Wilder Lavington, Vaswani, Sharan, Babanezhad, Reza, Schmidt, Mark, Nicolas Le Roux

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  15. 15
    SVRG Meets AdaGrad: Painless Variance Reduction

    SVRG Meets AdaGrad: Painless Variance Reduction by Dubois-Taine, Benjamin, Vaswani, Sharan, Babanezhad, Reza, Schmidt, Mark, Lacoste-Julien, Simon

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  16. 16
    A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage

    A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage by Zolaktaf, Zainab, Babanezhad, Reza, Pottinger, Rachel

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  17. 17
    An Analysis of the Adaptation Speed of Causal Models

    An Analysis of the Adaptation Speed of Causal Models by Rémi Le Priol, Harikandeh, Reza Babanezhad, Bengio, Yoshua, Lacoste-Julien, Simon

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  18. 18
    To Each Optimizer a Norm, To Each Norm its Generalization

    To Each Optimizer a Norm, To Each Norm its Generalization by Vaswani, Sharan, Babanezhad, Reza, Gallego-Posada, Jose, Mishkin, Aaron, Lacoste-Julien, Simon, Nicolas Le Roux

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  19. 19
    Reducing the variance in online optimization by transporting past gradients

    Reducing the variance in online optimization by transporting past gradients by Arnold, Sébastien M R, Pierre-Antoine Manzagol, Babanezhad, Reza, Mitliagkas, Ioannis, Nicolas Le Roux

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  20. 20
    Process Patterns for Web Engineering

    Process Patterns for Web Engineering by Babanezhad, R, Bibalan, Y M, Ramsin, R

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