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Creators/Authors contains: "Aggarwal, Vaneet"

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  1. ; ; ( , Advances in Neural Information Processing Systems 36 (NeurIPS 2023))
    Free, publicly-accessible full text available December 30, 2024
  2. ; ; ( , 2023 62nd IEEE Conference on Decision and Control (CDC))
    Free, publicly-accessible full text available December 13, 2024
  3. ; ; ; ; ( , Advances in neural information processing systems)
    Free, publicly-accessible full text available December 1, 2024
  4. ; ; ( , IEEE Transactions on Emerging Topics in Computational Intelligence)
    Free, publicly-accessible full text available August 1, 2024
  5. ; ; ; ; ( , Proceedings of the 40th International Conference on Machine Learning)
    Free, publicly-accessible full text available July 3, 2024
  6. ; ; ; ; ; ; ( , IEEE/ACM Transactions on Networking)
    Free, publicly-accessible full text available July 10, 2024
  7. ; ; ; ( , Proceedings of the International Workshop on Artificial Intelligence and Statistics)
    We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investigate the same problem assuming a submodular and monotone reward function. In this work, we study a more general problem, i.e., when the reward function is not necessarily monotone, and the submodularity is assumed only in expectation. We propose Randomized Greedy Learning (RGL) algorithm and theoretically prove that it achieves a $\frac{1}{2}$-regret upper bound of $\Tilde{\mathcal{O}}(n T^{\frac{2}{3}})$ for horizon $T$ and number of arms $n$. We also show in experiments that RGL empirically outperforms other full-bandit variants in submodular and non-submodular settings. 
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    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ( , IEEE Communications Magazine)
    Free, publicly-accessible full text available May 8, 2024
  9. ; ; ; ( , IEEE Transactions on Information Theory)
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  10. ; ; ; ; ; ; ( , IEEE/ACM Transactions on Networking)
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