SOGAS is the first Grover-search-based quantum algorithm for fixed-confidence discrete simulation optimization, returning near-optimal solutions with quadratic speedup in query complexity.
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19 Pith papers cite this work. Polarity classification is still indexing.
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A Fenchel-Young loss formulation turns data-driven inverse optimization into a differentiable problem solvable by gradient methods, with claimed theoretical guarantees and superior empirical performance on noisy data.
Extends Thompson sampling analysis to Borel MDPs via a three-term regret decomposition and shows exponential convergence of residual regret to zero under extended assumptions.
Establishes a regret lower bound proving that polynomial effective optimism rules out minimax-optimal rates for GP-UCB on Matérn kernels under uniform confidence.
A new doubly stochastic Poisson process incorporating Taylor's law produces a closed-form power-law staffing formula that bridges square-root and linear safety rules for over-dispersed arrivals.
Introduces Indep and Correl models for correlated arrivals in online matching and develops algorithms with constant-factor guarantees that outperform fluid relaxations on high-variance data.
Proposes an adaptive quantile schedule in Bayesian risk MDPs for online RL that starts robust and gradually encourages exploration, supported by asymptotic normality characterization and sublinear Bayesian regret bounds.
A Markovian equilibrium model for ride-hailing that treats vehicle decisions as an infinite-horizon semi-Markov process and solves for consistent traffic flows and acceptance rates via fixed-point iteration.
UCB allocation of human labels under PPI++ residual difficulty achieves O(ln B/B²) regret to the Neyman oracle and cuts uniform’s 10–12% efficiency gap to 2–6% on a 68-task digital-twin survey.
TimeMark is a trustworthy time watermarking framework that achieves exact generation-time recovery from AI-generated content with theoretically perfect accuracy by using time-dependent cryptographic keys, random non-stored bit sequences, and two-stage encoding with error-correcting codes.
LLM-MAS uses prompt-embedded design choices to drive multi-agent LLM simulations modeled as a controlled Markov chain, with an on-trajectory algorithm for zeroth-order gradient-based optimization of steady-state performance.
Different binarization extended formulations for network flow MIPs cause large differences in solver performance that the authors attribute to cutting-plane generation, with a family of mixed-integer rounding inequalities showing particular benefit.
Derives necessary and sufficient conditions for vanishing regret in the censored data-driven newsvendor under a DRO ambiguity set defined by the max historical order quantity, and proposes a near-optimal adaptive algorithm with finite-sample bounds.
SCOPE is a parameter-free splicing-based algorithm for sparsity-constrained optimization of strongly convex smooth objectives that achieves linear convergence and exact support recovery without relying on RIP-type conditions.
Tree-based discretization paired with ILP matching delivers computationally efficient and less biased estimates of the average treatment effect on the treated.
RecPIE jointly optimizes recommendation predictions and LLM-generated natural-language explanations via alternating training and reinforcement learning, yielding 3-4% accuracy gains and higher human preference on Google Maps POI data.
SFLA strengthens an existing convex inner-approximation for RHS-WDRJCC, reducing constraints and tightening ancillary variables to achieve faster computation with no added conservativeness and potential improvement over W-CVaR.
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Staffing under Taylor's Law: A Unifying Framework for Bridging Square-root and Linear Safety Rules
A new doubly stochastic Poisson process incorporating Taylor's law produces a closed-form power-law staffing formula that bridges square-root and linear safety rules for over-dispersed arrivals.