ORBIT learns the (β-1)-smooth oracle price map via local polynomial approximation and bandit convex optimization in a semiparametric contextual pricing model, achieving regret Õ(T^{(2β-1)/(4β-3)} + √(dT)) with a matching lower bound for fixed d.
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RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.
A generalized variance-reduced ZO hard-thresholding algorithm removes prior limits on random directions for gradient estimates, yielding improved convergence rates under standard assumptions.
Double/debiased ML framework for average derivative effects in panel data with continuous treatments, two-way fixed effects, and endogeneity.
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
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Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
ORBIT learns the (β-1)-smooth oracle price map via local polynomial approximation and bandit convex optimization in a semiparametric contextual pricing model, achieving regret Õ(T^{(2β-1)/(4β-3)} + √(dT)) with a matching lower bound for fixed d.
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Robust Uniform Recovery of Structured Signals from Nonlinear Observations
RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.
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New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
A generalized variance-reduced ZO hard-thresholding algorithm removes prior limits on random directions for gradient estimates, yielding improved convergence rates under standard assumptions.
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Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
Double/debiased ML framework for average derivative effects in panel data with continuous treatments, two-way fixed effects, and endogeneity.
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Estimating Precision Matrices for High-Dimensional Interval-Valued Data
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
- Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety