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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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0111 · Jun 201519922001200920172026
8 results for VCG

New RL approach learns dynamic VCG mechanisms in unknown MDP environments.

problem Learning dynamic VCG mechanisms in unknown MDP environments.
method Reward-free online RL for exploration, combined with function approximation.
result Regret bound of O~(T2/3)\tilde{\mathcal{O}}(T^{2/3}) for dynamic VCG mechanism learning.

Calibrated models can lead to miscalibrated aggregations in strategic interactions.

problem Miscalibration in aggregated predictions from multiple calibrated models.
method Analysis of strategic interactions between calibrated predictors, proving conditions for miscalibration and comparing VCG and Brier-score aggregation methods.
result VCG aggregation method outperforms Brier-score in strategic settings, providing robustness and comparable accuracy.

Mechanism designs for unknown agent values in stochastic bandit settings.

problem Designing truthful mechanisms for maximizing social welfare in settings with unknown agent values and stochastic feedback.
method Developed a VCG-like mechanism with regret bounds for multi-round allocations, balancing agent and seller welfare.
result Achieved an $Ω(T^{ rac{2}{3}})$ lower bound for the maximum of welfare, agent utilities, and mechanism utility after TT rounds.

Some online advertising offers pay only when an ad elicits a response. Randomness and uncertainty about response rates make showing those ads a risky investment for online publishers. Like financial investors, publishers can use portfolio allocation over multiple advertising offers to pursue revenue while controlling r…

2015-06-05abs ↗pdf ↗

DriftLite improves inference quality of diffusion models without retraining.

problem Adapting pre-trained diffusion models to new target distributions without retraining.
method Lightweight, training-free particle-based approach that steers inference dynamics with optimal stability control.
result Consistently reduces variance and improves sample quality over existing methods.

Algorithm learns optimal dynamic mechanisms from data.

problem Designing optimal mechanisms for dynamic settings with unknown reward functions.
method Offline reinforcement learning with pessimism principle.
result Learned mechanisms are efficient, individually rational, and truthful.