Happy Mittal

About Me

I am a Ph.D scholar in the Department of Computer Science at the Indian Institute of Technology Delhi, working with Dr. Parag Singla. I joined here in July 2013, and planning to submit my thesis soon.

In July 2019, I started working as an Applied Scientist II in the India Machine Learning Team at the Amazon Development Center India, Bengaluru.

Research Interest

My thesis title is "Effective Inference & Learning in Statistical Relational Models". These models can be used to solve various problems in ML and AI like entity resolution, link prediction, information extraction, computer vision etc . To know more about my research please click here.

Curriculum Vitae

Please click here to download my CV.

Bio

I am a Ph.D scholar in the Department of Computer Science at the Indian Institute of Technology Delhi, working with Dr. Parag Singla. I joined here in July 2013, and planning to submit my thesis soon. I have also been the reviewer for the NeurIPS 2018 workshop on Relational Representation Learning.

In July 2019, I joined Amazon Development Center India, Bengaluru as an Applied Scientist II in their India Machine Learning Team

During my Ph.D, I did an internship under Dr. Kristian Kersting at the Technical University of Dortmund from Sep 2016-Dec 2016. There, I worked on learning tractable deep neural networks, and learning more interpretable object embeddings.

In June 2013, I completed my M.Tech from the Department of Computer Science at IIT Delhi, where I worked on user behaviour prediction on an online mobile comparison website smartprix, under the joint supervision of Dr. Parag Singla and Dr.Amitabha Bagchi. Previously, I completed my B.Tech in Information Technology at YMCA University of Science and Technology, Faridabad in the year 2011.

Research Interest

My primary research area is effective inference and learning in Statistical Relational Models. These models combine the complex relational structures among the entities in a data, and the underlying uncertainty of those relations. The relations are specified in a compact form like first order logic, and the uncertainty is captured by various probabilistic models. These models have been successfully used to solve various problems in ML and AI like entity resolution, link prediction, information extraction, computer vision etc .

Publications

[2019]

• Domain-size Aware Markov Logic Networks. Happy Mittal, Ayush Bhardwaj, Vibhav Gogate and Parag Singla. In Proc. of AISTATS 2019, Okinawa, Japan. Supplementary material, Slides, Poster

[2018]

• Lifted Marginal MAP Inference. Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate and Parag Singla. The 34th Conference on Uncertainty in Artificial Intelligence - UAI 2018, Monterey, California, USA.

[2016]

• Fine Grained Weight Learning in Markov Logic Networks. Happy Mittal, Shubhankar Suman Singh, Vibhav Gogate and Parag Singla. Sixth International Workshop on Statistical Relational Learning (StarAI 2016), New York, USA. Slides, Poster


• Analysis and characterisation of comparison shopping behaviour in the mobile handset domain. Mona Gupta, Happy Mittal, Parag Singla and Amitabha Bagchi. Elec. Comm. Res., May 2016.

[2015]

• Lifted Inference Rules With Constraints. Happy Mittal, Anuj Mahajan, Vibhav Gogate and Parag Singla. Twenty Ninth Annual Conference on Neural Information Processing Systems (NIPS), 2015. Montreal, Canada. Supplementary material, Slides, Poster

[2014]

• New Rules for Domain Independent Lifted MAP Inference. Happy Mittal, Prasoon Goyal, Vibhav Gogate and Parag Singla. Twenty Eighth Annual Conference on Neural Information Processing Systems (NIPS), 2014. Montreal, Canada. Supplementary material, Slides, Poster


• Characterizing Comparison Shopping Behavior: A Case Study . Mona Jain, Happy Mittal, Parag Singla and Amitabha Bagchi. Workshop on Big Data Customer Analytics (BDCA), 2014. Co-located with ICDE-14. Chicago, IL, USA.

Tutorial on Markov logic networks (MLNs)

Installing dropbox on ubuntu

Gate Math solutions (2001-2010)

Installing MinGW compiler on windows

Stirling numbers of second kind

Vedic Maths tricks

Installing Realtek wifi driver in ubuntu (any version)

[C Programming]

C FAQs

C Programming Notes

Creating and linking library files

Makefile tutorial

valgrind tutorial

gdb basics

gdb advanced

[C++ Programming]

C++ FAQs

Thinking in C++ (2nd Edition)

[Java Programming]

Thinking in java (3rd Edition)

[Python Programming]

Think Python

Markov Logic(Domingos et al)

Lifted MAP inference for Markov Logic Networks(Sarkhel et al [AISTATS 2014])

Understanding Belief Propagation and its Generalizations(Yedidia [2001])

Lifted First-Order Belief Propagation(Singla and Domingos [2008])

Counting Belief Propagation(Kersting et al [2009])

Approximate Lifted Belief Propagation(Singla et al [2014])

Belief Propagation and Revision in Networks with Loops

A simple approach to Bayesian network computations

Exploting symmetry in Lifted CSPs

First-order probabilistic inference(David Poole [2003])

Lifted First-Order Probabilistic Inference(Braz et al [2005])

Lifted Inference Seen from the Other Side : The Tractable Features(Jha et al [NIPS 2010])

On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs

Maximum Weight Matching via Max-Product Belief Propagation

Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference

Probabilistic Theorem Proving(Gogate and Domingos [2011])

Integration of SAT and CP Techniques

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference

Computing query probability with incidence algebras(Dalvi et al [2010])

Lifted Variable Elimination: Decoupling the Operators from the Constraint Language (Taghipour et al [2013])

Lifted Variable Elimination with Arbitrary Constraints(Taghipour et al [2012])

Generalized Counting for Lifted Variable Elimination(Taghipour and Davis [2012])

Lifted Inference and Learning in Statistical Relational Models(Guy Van Den Broeck PhD Thesis [2013])

Completeness results for Lifted Variable Elimination(Taghipour et al [AISTATS 2013])

Lifted Probabilistic Inference with Counting Formulas(Milch et al [AAAI08])

Contact

Happy Mittal

Office:
16th Floor (South Wing), Amazon India,
World Trade Center (Near Orion Mall),
Dr. Rajkumar Road, Malleshwaram, Bangalore- 560 055,
India.

Phone: +91 8826859252
Email: {firstname}.{lastname}@cse.iitd.ac.in

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