Relation Extraction Using Convolution Tree Kernel Expanded with Entity Features

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Some New Results for the Sobolev-Type Fractional Order Delay Systems with Noncompact Semigroup

The topological structure of solution sets for the Sobolev-type fractional order delay systems with noncompact semigroup is studied. Based on a fixed point principle for multivalued maps, the existence result is obtained under certain mild conditions. With the help of multivalued analysis tools, the compactness of the solution set is also obtained. Finally, we apply the obtained abstract results to the partial differential inclusions

Topological structure of solution sets for fractional evolution inclusions of Sobolev type

Abstract The paper is devoted to establishing the solvability and topological property of solution sets for the fractional evolution inclusions of Sobolev type. We obtain the existence of mild solutions under the weaker conditions that the semigroup generated by −AE−1 $-AE^{-1}$ is noncompact as well as F is weakly upper semicontinuous with respect to the second variable. On the same conditions, the topological structure of the set of all mild solutions is characterized. More specifically, we prove that the set of all mild solutions is compact and the solution operator is u.s.c. Finally, an example is given to illustrate our abstract results

Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning

We expose the danger of reward poisoning in offline multi-agent reinforcement learning (MARL), whereby an attacker can modify the reward vectors to different learners in an offline data set while incurring a poisoning cost. Based on the poisoned data set, all rational learners using some confidence-bound-based MARL algorithm will infer that a target policy - chosen by the attacker and not necessarily a solution concept originally - is the Markov perfect dominant strategy equilibrium for the underlying Markov Game, hence they will adopt this potentially damaging target policy in the future. We characterize the exact conditions under which the attacker can install a target policy. We further show how the attacker can formulate a linear program to minimize its poisoning cost. Our work shows the need for robust MARL against adversarial attacks