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Swaroop Vattam

I am a technical staff member at Lincoln Lab at MIT. Previously, I was a research scientist at Georgia Tech and a NAS fellow at NRL. I got my PhD in Computer Science from Georgia Tech in 2012. My research is broadly focused on applications of machine reasoning and machine learning to natural language processing problems. I am currently investigating data-driven model discovery systems in an effort to tackle the problem of automated machine learning.



cs.AI updates on arXiv.org: Computer Science -- Artificial Intelligence (cs.AI) updates on the arXiv.org e-print archive
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. (arXiv:1901.04562v1 [cs.LG]):

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road.

In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product

Interpretable machine learning: definitions, methods, and applications. (arXiv:1901.04592v1 [stat.ML]):

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them.

We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game. (arXiv:1901.04626v1 [cs.AI]):

We compare a novel Knowledge-based Reinforcement Learning (KB-RL) approach with the traditional Neural Network (NN) method in solving a classical task of the Artificial Intelligence (AI) field. Neural networks became very prominent in recent years and, combined with Reinforcement Learning, proved to be very effective for one of the frontier challenges in AI - playing the game of Go. Our experiment shows that a KB-RL system is able to outperform a NN in a task typical for NN, such as optimizing a regression problem. Furthermore, KB-RL offers a range of advantages in comparison to the traditional Machine Learning methods. Particularly, there is no need for a large dataset to start and succeed with this approach, its learning process takes considerably less effort, and its decisions are fully controllable, explicit and predictable.

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. (arXiv:1901.04670v1 [cs.LG]):

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.

Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning. (arXiv:1901.04693v1 [cs.SY]):

Heating, Ventilation, and Air Conditioning (HVAC) is extremely energy-consuming, accounting for 40% of total building energy consumption. Therefore, it is crucial to design some energy-efficient building thermal control policies which can reduce the energy consumption of HVAC while maintaining the comfort of the occupants. However, implementing such a policy is challenging, because it involves various influencing factors in a building environment, which are usually hard to model and may be different from case to case. To address this challenge, we propose a deep reinforcement learning based framework for energy optimization and thermal comfort control in smart buildings. We formulate the building thermal control as a cost-minimization problem which jointly considers the energy consumption of HVAC and the thermal comfort of the occupants. To solve the problem, we first adopt a deep neural network based approach for predicting the occupants' thermal comfort, and then adopt Deep Deterministic Policy Gradients (DDPG) for learning the thermal control policy. To evaluate the performance, we implement a building thermal control simulation system and evaluate the performance under various settings. The experiment results show that our method can improve the thermal comfort prediction accuracy, and reduce the energy consumption of HVAC while improving the occupants' thermal comfort.



Mathematical Moments from the American Mathematical Society: The American Mathematical Societys Mathematical Moments program promotes appreciation and understanding of the role mathematics plays in science, nature, technology, and human culture. Listen to researchers talk about how they use math: from presenting realistic animation to beating cancer.
Screening for Autism: Researcher: Jordan Hashemi, Duke University Moment: http://www.ams.org/samplings/mathmoments/mm142-autism.pdf Moment Title: Screening for Autism Description: Jordan Hashemi talks about an easy-to-use app to screen for autism. Podcast page: http://www.ams.org/samplings/mathmoments/mm142-autism-podcast Audio file: podcast-mom-autism.mp3
Unbunching Buses: Researchers: Vikash V. Gayah and S. Ilgin Guler, Pennsylvania State University Moment: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching.pdf Moment Title: Unbunching Buses Description: Gayah and Guler talk about mitigating the clustering of buses on a route. Podcast page: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching-podcast Audio file: podcast-mom-bus-bunching.mp3
Winning the Race: Researcher: Christine Darden, NASA (retired) Moment: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures.pdf Moment Title: Winning the Race Description: Christine Darden on working at NASA. Podcast page: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures-podcast
Revolutionizing and Industry: Researchers: Christopher Brinton, Zoomi, Inc. and Princeton University, and Mung Chiang, Purdue University Moment: http://www.ams.org/samplings/mathmoments/mm139-netflix.pdf Description: Christopher Brinton and Mung Chiang talk about the Netflix Prize competition.
Going Into a Shell: Researcher: Derek Moulton, University of Oxford Moment: http://www.ams.org/samplings/mathmoments/mm138-shells.pdf Description: Derek Moulton explains the math behind the shapes of seashells.


AMS Feature Column: AMS Feature Column - RSS Feed
Branko Grunbaum Remembered--A Great Geometer!:
Upgrading Slums Using Topology:
Topology and Elementary Electric Circuit Theory, I:
Recognition:
Getting in Sync:
Reading the Bakhshali Manuscript:
Crochet Topology:
Mathematical Economics for Mathematics and Statistics Awareness Month:
Neural Nets and How They Learn:
Jakob Bernoulli's Zoo:






Last modified 18 November 2018 at 5:47 pm by svattam