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Kush Varshney

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4 papers

1 author row

AAAI Conference 2020 Conference Paper

Event-Driven Continuous Time Bayesian Networks

Debarun Bhattacharjya
Karthikeyan Shanmugam
Tian Gao
Nicholas Mattei
Kush Varshney
Dharmashankar Subramanian

We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system’s state variables could be inﬂuenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the inﬂuence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual’s life outcome areas such as education, transportation, employment and ﬁnancial education.

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AAAI Conference 2018 Conference Paper

Assessing National Development Plans for Alignment With Sustainable Development Goals via Semantic Search

Jonathan Galsurkar
Moninder Singh
Lingfei Wu
Aditya Vempaty
Mikhail Sushkov
Devika Iyer
Serge Kapto
Kush Varshney

The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the work- ﬂow of policy experts. Speciﬁcally we use paragraph embedding techniques to ﬁnd paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhoodbased supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.

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NeurIPS Conference 2017 Conference Paper

Optimized Pre-Processing for Discrimination Prevention

Flavio Calmon
Dennis Wei
Bhanukiran Vinzamuri
Karthikeyan Natesan Ramamurthy
Kush Varshney

Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distortion in individual data samples, and preserving utility. We characterize the impact of limited sample size in accomplishing this objective. Two instances of the proposed optimization are applied to datasets, including one on real-world criminal recidivism. Results show that discrimination can be greatly reduced at a small cost in classification accuracy.

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NeurIPS Conference 2017 Conference Paper

Scalable Demand-Aware Recommendation

Jinfeng Yi
Cho-Jui Hsieh
Kush Varshney
Lijun Zhang
Yao Li

Recommendation for e-commerce with a mix of durable and nondurable goods has characteristics that distinguish it from the well-studied media recommendation problem. The demand for items is a combined effect of form utility and time utility, i. e. , a product must both be intrinsically appealing to a consumer and the time must be right for purchase. In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession. Moreover, purchase data, in contrast to rating data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware recommendation problem that we pose via joint low-rank tensor completion and product category inter-purchase duration vector estimation. We further relax this problem and propose a highly scalable alternating minimization approach with which we can solve problems with millions of users and millions of items in a single thread. We also show superior prediction accuracies on multiple real-world datasets.

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