Publication

You can also find my papers listed in Google Scholar, or dblp.

Journal

• [J3] Hyojung Lee, Hyeryung Jang, Jungseul Ok, Sangwoo Moon, Sumyeong Ahn, and Yung Yi, "An Empirical Study on 802.11-based Real-time Video Transmission between Vehicles: Impact of PHY/MAC Parameters and Encoding Methods," submitted to Information & Communication Technology Express, 2017.
• [J2] Hyeryung Jang, Se-Young Yun, Jinwoo Shin, and Yung Yi, "Game Theoretic Perspective of Optimal CSMA,” to appear at IEEE Transactions on Wireless Communications, 2017. (A shorter version has been published INFOCOM 2014.)
• [J1] Hyojung Lee, Hyeryung Jang, Jeong-woo Cho, and Yung Yi, "Traffic Scheduling and Revenue Distribution among Providers in the Internet: Trade-offs and Impacts,” IEEE Journal on Selected Areas in Communications (JSAC) Special Issue on Game Theory for Networks, vol.35, no.2, pp.421-431, 2017.

Conference

• [C7] Hyeryung Jang, Hyungwon Choi, Yung Yi, and Jinwoo Shin, "Adiabatic Persistent Contrastive Divergence Learning," in Proceedings of IEEE International Symposium on Information Theory (ISIT), 2017. (ArXiv preprint version is available at arXiv:1605.08174) [slide]
• [C6] Hyeryung Jang, Se-Young Yun, Jinwoo Shin, and Yung Yi, "Distributed Coordination Maximization over Networks: A Stochastic Approximation Approach," in Proceedings of ACM Mobile Ad Hoc Networking and Computing (MobiHoc), 2016. (Acceptance rate: 18.7%) [slide]
• [C5] Hyeryung Jang, Se-Young Yun, Jinwoo Shin, and Yung Yi, "Distributed Learning for Utility Maximization over CSMA-based Wireless Multihop Networks," in Proceedings of IEEE International Conference on Computer Communications (INFOCOM), 2014. (Acceptance rate: 19.3%) [slide]
• [C4] Hyojung Lee, Hyeryung Jang, Yung Yi, and Jeong-woo Cho, "On the Interaction between Content-Oriented Traffic Scheduling and Revenue Sharing among Providers," in Proceedings of IEEE International Conference on Computer Communications (INFOCOM) Smart Data Pricing workshop, 2013.
• [C3] Jaehoon Jeong, Hyeryung Jang, Yujin Kim, Yung Yi, and Jeonghoon Mo, "Network traffic reduction through smart network," in Proceedings of International Conference on Information Networking (ICOIN), 2013.
• [C2] Hyojung Lee, Hyeryung Jang, and Yung Yi, "On the Stability of ISPs’ Coalition Structure: Shapley Value based Revenue Sharing," in Proceedings of Annual Conference on Information Sciences and Systems (CISS), 2012.
• [C1] Hyeryung Jang, Hyojung Lee, and Yung Yi, "On the Interaction between ISP Revenue Sharing and Network Neutrality," in Proceedings of ACM International Conference on emerging Networking Experiments and Technologies (CoNEXT) Student workshop, 2010. [poster]

Thesis

• [T2] Hyeryung Jang, "Optimization and Learning of Graphical Models: A Stochastic Approximation Approach," Ph.D. Thesis in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), 2017. (Advisor: Prof. Yung Yi, Co-Advisor: Prof. Jinwoo Shin)
  • Abstract: This thesis mainly addresses the problem of optimization and learning in graphical models via stochastic approximation theory. First, in various multi-agent networked environments, the system can benefit from coordinating actions of two interacting agents at some cost of coordination, where a primary goal is to develop distributed algorithms maximizing the coordination effect. Examples include a wireless sensor networks with duty cycling, where a sensor node consumes a certain amount of energy when it is awake, but a coordinated operation of sensors enables some meaningful tasks, e.g., sensed data forwarding. Such pair-wise coordinations and node-wise costs in the network can be captured by graphical model framework, which becomes the problem of finding the optimal graph parameter. In this thesis, I present various distributed algorithms that require only one-hop message passing and locally-observed information, which can be interpreted based on either Lagrange duality theory or game theory framework. The proposed algorithms are motivated by a stochastic approximation method that runs a Markov chain simulation incompletely over time, but provably guarantees the convergence to the optimal solution. Second, I discuss the problem of parameter learning in graphical models having latent variables, where the standard approach, i.e., Expectation Maximization algorithm, is computationally intractable for high dimensional data, in both expectation and maximization steps. Since the substitution of one step to a faster surrogate for combating against intractability can often cause failure in convergence, I propose a new learning algorithm (called Adiabatic Persistent Contrastive Divergence), which runs a few cycles of Markov chains in both steps. Using multi-time-scale stochastic approximation theory, the proposed algorithm provably ensures convergence to a correct optimum, and moreover, I demonstrate the theoretical findings through extensive experiments with synthetic data and/or real-world data sets.
• [T1] Hyeryung Jang, "Economic Benefits of ISP-CDN and ISP-ISP Cooperation," M.S. Thesis in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), 2012. (Advisor: Prof. Yung Yi)

MISC

• 6 Korean conference and journal publications