We consider the problem of minimizing the number of matrix-vector queries needed for accurate trace estimation in the dynamic setting where our underlying matrix is changing slowly, such as during an optimization process.

Inther valued, forsuf"andanyp2[1,2], p log ( 1/ )/" p number isnecessary"kAkp errorwith1 .

Unlike static network pruning, channel gating optimizes CNN inference atrun-time byexploiting input-specific characteristics, which allows substantially reducing the compute cost with almost no accuracyloss.

This view enables a structured variational approximation capturing dependencies across variables in the model.

The goal of reinforcement learning algorithms is to estimate and/or optimise the value function.

We present a computationally efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both stochastic and adversarial environments. The bound against oblivious adversaries is O( αT), where T is the time horizon andα is the independence number of the feedback graph.