Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm.

An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents to make better predictions than they would individually, but may not be willing to release labeled data or model parameters.

An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents to make better predictions than they would individually, but may not be willing to release labeled data or model parameters.

At inference time, these models are computationally intensive, especially with volumetric data.

Further, we extend our(ε,δ)-LDP algorithm toGeneralized Linear Bandits,which enjoysa sub-linear regret O(T3/4/ε) and is conjectured to be nearly optimal. Note that given the existingΩ(T) lower bound for DP contextual linear bandits [35], our result shows afundamental difference between LDP and DP contextual bandits learning.

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process.