We develop new models and algorithms for learning the temporal dynamics of thetopic polytopes andrelated geometric objects thatarise intopic model based inference.

In many sequential decision making applications, the change of decision would bring an additional cost, such as the wear-and-tear cost associated with changing server status. To control the switching cost, we introduce the problem of online convex optimization with continuous switching constraint, where the goal is to achieve a small regret given a budget on the overall switching cost. We first investigate the hardness of the problem, and provide a lower bound of orderΩ( T)whentheswitchingcostbudgetS = Ω( T),andΩ(min{T/S,T}) whenS = O( T), where T is the time horizon. The essential idea is to carefully design an adaptive adversary, who can adjust the loss function according to thecumulative switchingcostofthe playerincurredso farbasedonthe orthogonal technique. We then develop a simple gradient-based algorithm which enjoys the minimax optimal regret bound.

The techniques of machine unlearning, also known as concept erasing, have been developed to address these risks.

Weprovide aunified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem isstrictly harder than the standard IL problem by presenting an exponential sample complexity separation between ILand ILFO.

Weprovide aunified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem isstrictly harder than the standard IL problem by presenting an exponential sample complexity separation between ILand ILFO.

This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.

This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.