I'm a 4th year PhD student in Prof Joydeep Ghosh's
Intelligent Data Exploration and Analysis Laboratory (IDEAL
) at UT Austin
. My research is on recovering parsimonious structure in the data, specifically sparsity, and exploiting it for prediction.
In the past I have worked on problems involving predicting buying propensity based on marketing touches, large scale recommendation systems, Ad Click prediction, and ranking.
- Sparse Submodular Probabilistic PCA Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi O. Koyejo AISTATS 2015 (Oral)
- On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack NIPS 2014: 676-684
- Parallel matrix factorization for binary response. Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen. IEEE BigData Conference 2013: 430-438
- Estimating rates of rare events with multiple hierarchies through scalable log-linear models. Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota. KDD 2010: 213-222
- Translating relevance scores to probabilities for contextual advertising. Deepak Agarwal, Evgeniy Gabrilovich, Robert Hall, Vanja Josifovski, Rajiv Khanna CIKM 2009: 1899-1902
- Structured learning for non-smooth ranking losses. Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya. KDD 2008: 88-96
- A Deflation Method for Structured Probabilistic PCA (Submitted)
- Pursuits in Structured Non-convex Matrix Factorizations (Submitted)
- A Deflation Method for Probabilistic PCA. Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference
- Towards a Better Understanding of Predict and Count Models. S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna. CoRR abs/1511.02024
- DPM: A State Space Model for Large-Scale Direct Marketing Yubin Park, Rajiv Khanna, Joydeep Ghosh, Daniel Mihalko. CoRR abs/1507.01135
- ETH Zurich (summer 2015): I spent the summer at Data Analytics Lab under Martin Jaggi, and worked on generalized pursuit algorithms.
- Microsoft Research (summer intern 2014): I spent the summer at MSR Silicon Valley working on word embeddings.
- LinkedIn (summer intern 2013): I worked on shortening the feedback loop in the Ad serving platform. The project involved working on large scale implementation of Online Logistic Regression on hadoop.
- Yahoo! Labs (Full time 2008-2012): I was a Research Engineer in Yahoo! Labs (Bangalore) and worked on various projects including large scale recommendation systems, click through rates, and information corroboration.