129 citations · 527 across the 38 of their papers we have counts for
40 papers
Parameter Estimation and Inference in a Continuous Piecewise Linear Regression Model
Georg Hahn, Moulinath Banerjee, Bodhisattva Sen
The estimation of regression parameters in one dimensional broken stick models is a research area of statistics with an extensive literature. We are interested in extending such mo…
Inference for change-plane regression
Chaeryon Kang, Hunyong Cho, Rui Song +3
A key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. Two estimators are proposed to deal with this non-uniqu…
Understanding new tasks through the lens of training data via exponential tilting
Subha Maity, Mikhail Yurochkin, Moulinath Banerjee +1
Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be…
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Subha Maity, Debarghya Mukherjee, Moulinath Banerjee +1
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most pr…
A linear adjustment based approach to posterior drift in transfer learning
Subha Maity, Diptavo Dutta, Jonathan Terhorst +2
We present a new model and methods for the posterior drift problem where the regression function in the target domain is modeled as a linear adjustment (on an appropriate scale) of…
On robust learning in the canonical change point problem under heavy tailed errors in finite and growing dimensions
Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
This paper presents a number of new findings about the canonical change point estimation problem. The first part studies the estimation of a change point on the real line in a simp…