publications

2019

1. Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Das, Shubhomoy, Islam, Md Rakibul, Jayakodi, Nitthilan Kannappan, and Doppa, Janardhan Rao In arXiv preprint arXiv:1901.08930 2019 [Abs]

   Anomaly detection (AD) task corresponds to identifying the true anomalies from a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies, which are then analyzed by a human to discover the true anomalies. However, this process can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. We make four main contributions towards this goal. First, we present an important insight that explains the practical successes of AD ensembles and how ensembles are naturally suited for active learning. Second, we present several algorithms for active learning with tree-based AD ensembles. These algorithms help us to improve the diversity of discovered anomalies, generate rule sets for improved interpretability of anomalous instances, and adapt to streaming data settings in a principled manner. Third, we present a novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning with generic AD ensembles. GLAD allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. Fourth, we present extensive experiments to evaluate our insights and algorithms. Our results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, our active learning algorithms under the streaming-data setup are competitive with the batch setup.

2. Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning Ma, Chao, Chowdhury, F A Rezaur Rahman, Deshwal, Aryan, Islam, Rakibul, Doppa, Janardhan Rao, and Roth, Dan IJCAI 2019 [Abs]

   In a structured prediction problem, we need to learn a predictor that can produce a structured output given a structured input (eg, part-of-speech tagging). The key learning and inference challenge is due to the exponential size of the structured output space. This paper makes four contributions towards the goal of a computationally-efficient inference and training approach for structured prediction that allows to employ complex models and to optimize for non-decomposable loss functions. First, we define a simple class of randomized greedy search (RGS) based inference procedures that leverage classification algorithms for simple outputs. Second, we develop a RGS specific learning approach for amortized inference that can quickly produce high-quality outputs for a given set of structured inputs. Third, we plug our amortized RGS inference solver inside the inner loop of parameterlearning algorithms (eg, structured SVM) to improve the speed of training. Fourth, we perform extensive experiments on diverse structured prediction tasks. Results show that our proposed approach is competitive or better than many state-ofthe-art approaches in spite of its simplicity.

3. Planning with actively eliciting preferences Das, Mayukh, Odom, Phillip, Islam, Md. Rakibul, Doppa, Janardhan Rao, Roth, Dan, and Natarajan, Sriraam In Knowledge-Based Systems 2019 [Abs]

   Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. We propose an approach where the planner identifies when and where expert guidance will be most useful and seeks expert’s preferences accordingly to make better decisions. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.

2018

1. Active Anomaly Detection via Ensembles Das, Shubhomoy, Islam, Md Rakibul, Jayakodi, Nitthilan Kannappan, and Doppa, Janardhan Rao In CoRR 2018

2017

1. Active Preference Elicitation for Planning Das, Mayukh, Islam, Md Rakibul, Doppa, Janardhan Rao, Roth, Dan, and Natarajan, Sriraam In AAAI Workshop on Human-Machine Collaborative Planning 2017
2. Towards Problem Solving Agents that Communicate and Learn Narayan-Chen, Anjali, Graber, Colin, Das, Mayukh, Islam, Md Rakibul, Dan, Soham, Natarajan, Sriraam, Doppa, Janardhan Rao, Hockenmaier, Julia, Palmer, Martha, and Roth, Dan In RoboNLP@ACL 2017
3. Learning to Speed Up Query Planning in Graph Databases Namaki, Mohammad Hossein, Chowdhury, F. A. Rezaur Rahman, Islam, Md Rakibul, Doppa, Janardhan Rao, and Wu, Yinghui In Proceedings of 27th International Conference on Automated Planning and Scheduling (ICAPS) 2017
4. Select-and-Evaluate :A Learning Framework for Large-Scale Knowledge Graph Search Chowdhury, F.A. Rezaur Rahman, Ma, Chao, Islam, Md Rakibul, Namaki, Mohammad Hossein, Faruk, Mohammad Omar, and Doppa, Janardhan Rao In Journal of Machine Learning Research (JMLR), Proceedings Track 2017