Christian Kroer

The openness of wireless communication and the recent development of software-defined radio technology, respectively, provide a low barrier and a wide range of capabilities for misbehavior, attacks, and defenses against attacks. Expand

We show how the LSFRP can be solved sub-optimally using the planner POPF and optimally with a mixed-integer program (MIP) and a novel method called Temporal Optimization Planning. Expand

We introduce dynamic thresholding for online learning algorithms, in which a threshold is set at every iteration such that any action in the decision tree with probability below the threshold set to zero probability. Expand

We develop the first general, algorithm-agnostic, solution quality guarantees for Nash equilibria computed in imperfect-recall abstractions, when implemented in the original (perfect-recALL) game. Expand

We derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points. Expand

This paper considers a smoothing procedure that relies on {\em pacing multipliers}: for each bidder, the platform applies a factor between 0 and 1 that uniformly scales the bids across all auctions. Expand

We investigate the acceleration of first-order methods for solving extensive-form games through better design of the dilated entropy function---a class of distance-generating functions related to the domains associated with the extensive- form games. Expand

In this paper, we show the first general, algorithm-agnostic, solution quality guarantees for Nash equilibria computed in imperfect-recall abstractions, when implemented in the original (perfect) game. Expand

We show that it is possible to leverage "optimistic" regret minimizers to achieve a $O(T^{-3/4})$ convergence rate within CFR. Expand

Abstraction has long been a key component in the practical solving of large-scale extensive-form games. Expand