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- Abhishek Kumar, Piyush Rai, Hal DaumÃ©
- NIPS
- 2011

In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find aâ€¦ (More)

- Avishek Saha, Piyush Rai, Hal DaumÃ©, Suresh Venkatasubramanian
- AISTATS
- 2011

We propose an Online MultiTask Learning (Omtl) framework which simultaneously learns the task weight vectors as well as the task relatedness adaptively from the data. Our work is in contrast withâ€¦ (More)

- Piyush Rai, Hal DaumÃ©
- NIPS
- 2008

We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparseâ€¦ (More)

- Piyush Rai, Abhishek Kumar, Hal DaumÃ©
- NIPS
- 2012

Multiple-output regression models require estimating multiple parameters, one for each output. Structural regularization is usually employed to improve parameter estimation in such models. In thisâ€¦ (More)

In this work, we show how active learning in some (target) domain can leverage information from a different but related (source) domain. We present an algorithm that harnesses the source domain dataâ€¦ (More)

Multiview clustering algorithms allow leveraging information from multiple views of the data and therefore lead to improved clustering. A number of kernel based multiview clustering algorithms workâ€¦ (More)

- Piyush Rai, Hal DaumÃ©
- AISTATS
- 2010

Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a latent subspace. Moreâ€¦ (More)

- Avishek Saha, Piyush Rai, Hal DaumÃ©, Suresh Venkatasubramanian, Scott L. DuVall
- ECML/PKDD
- 2011

In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. We show how active learning in a target domain can leverage informationâ€¦ (More)

- Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin
- ICML
- 2014

We present a scalable Bayesian framework for low-rank decomposition of multiway tensor data with missing observations. The key issue of pre-specifying the rank of the decomposition is sidestepped inâ€¦ (More)

- Piyush Rai, Hal DaumÃ©, Suresh Venkatasubramanian
- IJCAI
- 2009

We present a streaming model for large-scale classification (in the context of l2-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes theâ€¦ (More)