11 citations · 16 across the 5 of their papers we have counts for
16 papers
Convergence of Uncertainty Sampling for Active Learning
Anant Raj, Francis Bach
Uncertainty sampling in active learning is heavily used in practice to reduce the annotation cost. However, there has been no wide consensus on the function to be used for uncertai…
Non-stationary Online Regression
Anant Raj, Pierre Gaillard, Christophe Saad
Online forecasting under a changing environment has been a problem of increasing importance in many real-world applications. In this paper, we consider the meta-algorithm presented…
Model-specific Data Subsampling with Influence Functions
Anant Raj, Cameron Musco, Lester Mackey +1
Model selection requires repeatedly evaluating models on a given dataset and measuring their relative performances. In modern applications of machine learning, the models being con…
Stochastic Stein Discrepancies
Jackson Gorham, Anant Raj, Lester Mackey
Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable. However, the computation of a Stein…
Explicit Regularization of Stochastic Gradient Methods through Duality
Anant Raj, Francis Bach
We consider stochastic gradient methods under the interpolation regime where a perfect fit can be obtained (minimum loss at each observation). While previous work highlighted the i…
Importance Sampling via Local Sensitivity
Anant Raj, Cameron Musco, Lester Mackey
Given a loss function that can be written as the sum of losses over a large set of inputs , it is often desirable to approximate $…