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Ali Zarezade

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2021 Conference Paper

Classification Under Human Assistance

Abir De
Nastaran Okati
Ali Zarezade
Manuel Gomez Rodriguez

Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by this empirical observation, our goal is to design classifiers that are optimized to operate under different automation levels. More specifically, we focus on convex margin-based classifiers and first show that the problem is NP-hard. Then, we further show that, for support vector machines, the corresponding objective function can be expressed as the difference of two functions f = g − c, where g is monotone, non-negative and γ-weakly submodular, and c is non-negative and modular. This representation allows us to utilize a recently introduced deterministic greedy algorithm, as well as a more efficient randomized variant of the algorithm, which enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.

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JMLR Journal 2018 Journal Article

Steering Social Activity: A Stochastic Optimal Control Point Of View

Ali Zarezade
Abir De
Utkarsh Upadhyay
Hamid R. Rabiee
Manuel Gomez-Rodriguez

User engagement in online social networking depends critically on the level of social activity in the corresponding platform---the number of online actions, such as posts, shares or replies, taken by their users. Can we design data-driven algorithms to increase social activity? At a user level, such algorithms may increase activity by helping users decide when to take an action to be more likely to be noticed by their peers. At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users. In this paper, we model social activity using the framework of marked temporal point processes, derive an alternate representation of these processes using stochastic differential equations (SDEs) with jumps and, exploiting this alternate representation, develop two efficient online algorithms with provable guarantees to steer social activity both at a user and at a network level. In doing so, we establish a previously unexplored connection between optimal control of jump SDEs and doubly stochastic marked temporal point processes, which is of independent interest. Finally, we experiment both with synthetic and real data gathered from Twitter and show that our algorithms consistently steer social activity more effectively than the state of the art. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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AAAI Conference 2017 Conference Paper

Correlated Cascades: Compete or Cooperate

Ali Zarezade
Ali Khodadadi
Mehrdad Farajtabar
Hamid Rabiee
Hongyuan Zha

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives.

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