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- Nikolay Laptev, Saeed Amizadeh, Ian Flint
- KDD
- 2015

This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case… (More)

- Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
- UAI
- 2012

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not scale well due to the quadratic complexity. In this paper, a new dual-tree based variational approach for approximating the transition matrix and efficiently performing the random… (More)

- Hadi Firouzi, Majid Nili Ahmadabadi, Babak Nadjar Araabi, Saeed Amizadeh, Maryam S. Mirian, Roland Siegwart
- IEEE Trans. Autonomous Mental Development
- 2012

- S. Amizadeh, M.N. Ahmadabadi, B.N. Araabi, R. Siegwart
- 2007 IEEE/ASME international conference on…
- 2007

Abstraction provides cognition economy and generalization skill in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction which maps the continuous state and action spaces into entities called concepts. Of computational concept learning approaches, action-based… (More)

- Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht
- AISTATS
- 2012

Diffusion maps are among the most powerful Machine Learning tools to analyze and work with complex high-dimensional datasets. Unfortunately, the estimation of these maps from a finite sample is known to suffer from the curse of dimensionality. Motivated by other machine learning models for which the existence of structure in the underlying distribution of… (More)

- Saeed Amizadeh, Shuguang Wang, Milos Hauskrecht
- IJCAI
- 2011

In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for… (More)

- Saeed Amizadeh, Milos Hauskrecht
- AAAI
- 2010

Pairwise Markov Networks (PMN) are an important class of Markov networks which, due to their simplicity, are widely used in many applications such as image analysis, bioinformatics, sensor networks, etc. However, learning of Markov networks from data is a challenging task; there are many possible structures one must consider and each of these structures… (More)

- George D. Montanez, Saeed Amizadeh, Nikolay Laptev
- AAAI
- 2015

Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods… (More)

- Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
- UAI
- 2013

Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly infeasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the… (More)

- Saeed Amizadeh, Mei Chen
- 2010

Graph-based dimensionality reduction techniques assume that each datapoint can be written as a fixed width vector with a well-defined distance measure among datapoints; also, they typically assume that the number of instances is small enough to perform matrix inversion or pseudo-inversion. This paper considers dimensionality reduction on data using… (More)