Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
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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.
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.
citing papers explorer
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
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Quantum Grover Adaptive Search for Discrete Simulation Optimization
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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A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization
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.
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Thompson Sampling for Infinite-Horizon Discounted Decision Processes
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.
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On the Suboptimality of GP-UCB under Polynomial Effective Optimism
Establishes a regret lower bound proving that polynomial effective optimism rules out minimax-optimal rates for GP-UCB on Matérn kernels under uniform confidence.
-
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.
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A Nonparametric Framework for Online Stochastic Matching with Correlated 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.
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Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs
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.
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A Markovian Traffic Equilibrium Model for Ride-Hailing
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.
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Allocating Human Oversight in AI-Enabled Analytics
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.
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TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
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.
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Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
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.
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Cutting Planes for Binarized Network Flow Problems
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.
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The Data-Driven Censored Newsvendor Problem
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.
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Sparsity-Constraint Optimization via Splicing Iteration
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.
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A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
Tree-based discretization paired with ILP matching delivers computationally efficient and less biased estimates of the average treatment effect on the treated.
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Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
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.
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Strengthened and Faster Linear Approximation to Joint Chance Constraints with Wasserstein Ambiguity
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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