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Creators/Authors contains: "Tong, Jindong"

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  1. ; ; (, Proceedings of the 42nd International Conference on Machine Learning)
    Free, publicly-accessible full text available July 8, 2026
  2. ; (, Proceedings of the 41st International Conference on Machine Learning)
    This paper studies sample average approximation (SAA) and its simple regularized variation in solving convex or strongly convex stochastic programming problems. Under heavy-tailed assumptions and comparable regularity conditions as in the typical SAA literature, we show — perhaps for the first time — that the sample complexity can be completely free from any complexity measure (e.g., logarithm of the covering number) of the feasible region. As a result, our new bounds can be more advantageous than the state-of-the-art in terms of the dependence on the problem dimensionality. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Journal of Nonlinear and Variational Analysis)
    Full Text Available