Zobrazeno 1 - 10
of 255
pro vyhledávání: '"BANERJEE, Moulinath"'
Autor:
Bracale, Daniele, Maity, Subha, Polo, Felipe Maia, Somerstep, Seamus, Banerjee, Moulinath, Sun, Yuekai
Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested inte
Externí odkaz:
http://arxiv.org/abs/2411.08998
Autor:
Somerstep, Seamus, Polo, Felipe Maia, Banerjee, Moulinath, Ritov, Ya'acov, Yurochkin, Mikhail, Sun, Yuekai
Modern large language model (LLM) alignment techniques rely on human feedback, but it is unclear whether these techniques fundamentally limit the capabilities of aligned LLMs. In particular, it is unknown if it is possible to align (stronger) LLMs wi
Externí odkaz:
http://arxiv.org/abs/2405.16236
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. Such shifts in distribution are particularly prevale
Externí odkaz:
http://arxiv.org/abs/2405.15172
Programmatic Weak Supervision (PWS) enables supervised model training without direct access to ground truth labels, utilizing weak labels from heuristics, crowdsourcing, or pre-trained models. However, the absence of ground truth complicates model ev
Externí odkaz:
http://arxiv.org/abs/2312.04601
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression functions or Bayes predictors a
Externí odkaz:
http://arxiv.org/abs/2307.02520
Autor:
Kang, Chaeryon, Cho, Hunyong, Song, Rui, Banerjee, Moulinath, Laber, Eric B., Kosorok, Michael R.
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-uniqueness. For each estimator, an n-rate of convergence is established, an
Externí odkaz:
http://arxiv.org/abs/2206.06140
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 reweighted to be more representative of the new (target) task. We con
Externí odkaz:
http://arxiv.org/abs/2205.13577
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete time, the underlying process is often co
Externí odkaz:
http://arxiv.org/abs/2205.13575
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 that in the source domain, an idea that inherits the simplicity and t
Externí odkaz:
http://arxiv.org/abs/2111.10841
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 simple stump model using the robust Huber estimating function which interp
Externí odkaz:
http://arxiv.org/abs/2105.11591