no code implementations • ICML 2020 • Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Individual fairness was proposed to address some of the shortcomings of group fairness.
no code implementations • 25 May 2024 • Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Ya'acov Ritov, Mikhail Yurochkin, Yuekai Sun
In particular, it is unclear whether it is possible to align (stronger) LLMs with superhuman capabilities with (weaker) human feedback without degrading their capabilities.
no code implementations • 24 May 2024 • Daniele Bracale, Subha Maity, Moulinath Banerjee, Yuekai Sun
In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a screening systems.
no code implementations • 7 Dec 2023 • Felipe Maia Polo, Mikhail Yurochkin, Moulinath Banerjee, Subha Maity, Yuekai Sun
We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional) distribution classes in which some variables are continuous-valued.
1 code implementation • NeurIPS 2023 • Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning.
1 code implementation • 26 May 2022 • Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task.
1 code implementation • 26 May 2022 • Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun
Time-varying stochastic optimization problems frequently arise in machine learning practice (e. g. gradual domain shift, object tracking, strategic classification).
no code implementations • 22 Feb 2021 • Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
In this paper, we present a new model coined SCENTS: Score Explained Non-Randomized Treatment Systems, and a corresponding method that allows estimation of the treatment effect at $\sqrt{n}$ rate in the presence of fairly general forms of confoundedness, when the `score' variable on whose basis treatment is assigned can be explained via certain feature measurements of the individuals under study.
Methodology Statistics Theory Statistics Theory
no code implementations • 3 Dec 2020 • Rohit K. Patra, Moulinath Banerjee, George Michailidis
In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target.
Disaster Response Methodology
no code implementations • 19 Jun 2020 • Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
no code implementations • 23 Mar 2020 • Subha Maity, Yuekai Sun, Moulinath Banerjee
We study the minimax rates of the label shift problem in non-parametric classification.
1 code implementation • 26 Dec 2019 • Subha Maity, Yuekai Sun, Moulinath Banerjee
We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical.
1 code implementation • 8 Sep 2019 • Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov
The problem of statistical inference for regression coefficients in a high-dimensional single-index model is considered.
Statistics Theory Other Statistics Statistics Theory
no code implementations • 7 Dec 2018 • Monika Bhattacharjee, Moulinath Banerjee, George Michailidis
Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters.