Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.

However, the exact formula for front-door adjustment depends on the structure of the graph, which is difficult to learn in practice.

In contrast, in non-private settings, practitioners commonly utilize "adaptive" hyperparameter optimization methods such as Gaussian process-based optimization, which select the next candidate based on information gathered from previous outputs. This substantial contrast between private and non-private hyperparameter optimization underscores a critical concern. In our paper, we introduce DP-HyPO, a pioneering framework for "adaptive"

Müller et al. [2021], Nguyen et al. [2022] builds a GP model for both the function value and the

Machine learning (ML) algorithms can solve many prediction tasks with greater accuracy than classical methods.

Meta wowed Wall Street with improvements in ad targeting fueled by AI alongside huge investment. Big tech earnings so far this week have sent a clear warning: investors are willing to overlook soaring spending on artificial intelligence if it fuels strong growth, but are quick to punish companies that fall short. The contrast was clear in Thursday's stock market reaction to earnings from Microsoft and Meta, highlighting how dramatically the stakes have changed since the launch of ChatGPT started the AI boom more than three years ago. Shares of the Instagram parent surged more than 9% on strong sales, while those of Microsoft slumped 10% after its cloud business failed to impress. "The market appears to be questioning whether these massive capital expenditure hikes will generate sufficient returns," said Jesse Cohen, senior analyst at Investing.com.