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.

Complex systems are often decomposed into modular subsystems for engineering tractability. Although various equation based white-box modeling techniques make use of such structure, learning based methods have yet to incorporate these ideas broadly. We present a modular simulation framework for modeling homogeneous multibody dynamical systems, which combines ideas from graph neural networks and neural differential equations. We learn to model the individual dynamical subsystem as a neural ODE module. Full simulation of the composite system is orchestrated via spatio-temporal message passing between these modules. An arbitrary number of modules can be combined to simulate systems of a wide variety of coupling topologies. We evaluate our framework on a variety of systems and show that message passing allows coordination between multiple modules over time for accurate predictions and in certain cases, enables zero-shot generalization to new system configurations. Furthermore, we show that our models can be transferred to new system configurations with lower data requirement and training effort, compared to those trained from scratch.

Wepropose anovel self-supervisory signal that we call cross-input consistency: we construct two distinct inputs for the same sequence ofvideo, byhiding different information about the sequence in each input.

Inspired by consistency of visual saliency between different vision models, a surrogate model isexpected toimprovetheattack performance viatransferability.