This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies.

As societal awareness of climate change grows, corporate climate policy engagements are attracting attention.

We did not find significant difference between the two datasets.

However, these advantages are not always observed, and the impact of PC inference on learning is not theoretically well understood.

We study a dynamic pricing problem for third-party platform service fees under strategic, far-sighted customers. In each time period, the platform sets a service fee based on historical data, observes the resulting transaction quantities, and collects revenue.

To address this shortcoming, we introduce'Graph Structured Prediction Energy Networks,' for which we develop inference techniques that allow to both model explicit local and implicit

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We also use our approach for estimating covariance structure for a number of real-world datasets and show that it consistently outperforms state-of-the-art estimators at a fraction of the computational cost.

The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs).