Yundi Qian

Bayesian Security Games for Controlling Contagion

Influence blocking games have been used to model adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the efforts of an influencer to spread his agenda across a social network. Previous work has assumed that the influence graph structure is known with certainty by both players. However, in reality, there is often significant information asymmetry between the mitigator and the influencer. We introduce a model of this information asymmetry as a two-player zero-sum Bayesian game. Nearly all past work in influence maximization and social network analysis suggests that graph structure is fundamental in strategy generation, leading to an expectation that solving the Bayesian game exactly is crucial. Surprisingly, we show through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed near optimally by ignoring the vast majority of it and simply solving an abstracted game with a few randomly chosen types. This suggests that optimal strategies of games that do not model the full range of uncertainty in influence blocking games are typically robust to uncertainty about the influence graph structure.

A fast table lookup based, statistical model driven non-uniform unit selection TTS

For multi-channel TTS applications, e.g. in a cloud service, it is highly desirable that high quality speech can be synthesized in low complexity. In this paper, we propose a fast table lookup based, statistical model driven approach to non-uniform unit selection TTS for that purpose. In TTS training, the voice font of all waveform segments is organized as a Gaussian kernel coded hash table and a table for storing quantized costs of all possible concatenation segment pairs. In synthesis, waveform segments with non-uniform lengths are first selected to construct a candidate lattice by looking up the Gaussian kernel coded hash table, and the best path is searched in the lattice by minimizing the accumulated concatenation scores, which are retrieved from the quantization table for possible concatenations. Experimental results show that the new approach can significantly reduce the search complexity while keep a high TTS voice quality.

Planning and learning in security games

We present two new critical domains where security games are applied to generate randomized patrol schedules. For each setting, we present the current research that we have produced. We then propose two new challenges to build accurate schedules that can be deployed effectively in the real world. The first is a planning challenge. Current schedules cannot handle interruptions. Thus, more expressive models, that allow for reasoning over stochastic actions, are needed. The second is a learning challenge. In several security domains, data can be used to extract information about both the environment and the attacker. This information can then be used to improve the defenders strategies.