39 citations · 107 across the 31 of their papers we have counts for
9 papers · 1 filter
On the Existence of Universal Lottery Tickets
Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee +1
The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Rece…
Finding the Optimal Dynamic Treatment Regime Using Smooth Fisher Consistent Surrogate Loss
Nilanjana Laha, Aaron Sonabend-W, Rajarshi Mukherjee +1
Large health care data repositories such as electronic health records (EHR) open new opportunities to derive individualized treatment strategies for complicated diseases such as se…
Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response
Jue Hou, Rajarshi Mukherjee, Tianxi Cai
A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the…
Sharp Signal Detection Under Ferromagnetic Ising Models
Sohom Bhattacharya, Rajarshi Mukherjee, Gourab Ray
In this paper we study the effect of dependence on detecting a class of structured signals in Ferromagnetic Ising models. Natural examples of our class include Ising Models on latt…
On Statistical Inference with High Dimensional Sparse CCA
Nilanjana Laha, Nathan Huey, Brent Coull +1
We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this r…
Sparse Uniformity Testing
Bhaswar B. Bhattacharya, Rajarshi Mukherjee
In this paper we consider the uniformity testing problem for high-dimensional discrete distributions (multinomials) under sparse alternatives. More precisely, we derive sharp detec…
On Support Recovery with Sparse CCA: Information Theoretic and Computational Limits
Nilanjana Laha, Rajarshi Mukherjee
In this paper we consider asymptotically exact support recovery in the context of high dimensional and sparse Canonical Correlation Analysis (CCA). Our main results describe four r…
On Ensembling vs Merging: Least Squares and Random Forests under Covariate Shift
Maya Ramchandran, Rajarshi Mukherjee
It has been postulated and observed in practice that for prediction problems in which covariate data can be naturally partitioned into clusters, ensembling algorithms based on suit…
Cross-Cluster Weighted Forests
Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani
Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies. We…