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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17 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.
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.
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.