Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Dwivedi, Raaz"'
Modern compression methods can summarize a target distribution $\mathbb{P}$ more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chain converging quickly to $\mathbb{P}$. We introduce a new suite of compr
Externí odkaz:
http://arxiv.org/abs/2404.12290
The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have the properties of being robust, extendable to new groups or attribute
Externí odkaz:
http://arxiv.org/abs/2404.06619
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, i
Externí odkaz:
http://arxiv.org/abs/2402.11652
Autor:
Ghosh, Susobhan, Kim, Raphael, Chhabria, Prasidh, Dwivedi, Raaz, Klasnja, Predrag, Liao, Peng, Zhang, Kelly, Murphy, Susan
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat
Externí odkaz:
http://arxiv.org/abs/2304.05365
Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve runtime. To address these sho
Externí odkaz:
http://arxiv.org/abs/2301.05974
Autor:
Dwivedi, Raaz, Tian, Katherine, Tomkins, Sabina, Klasnja, Predrag, Murphy, Susan, Shah, Devavrat
We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a matrix completion problem with missing data, where the $(i, t)$-th entry, when observed, is given by its me
Externí odkaz:
http://arxiv.org/abs/2211.14297
Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes. Specifica
Externí odkaz:
http://arxiv.org/abs/2211.08209
Autor:
Dwivedi, Raaz, Tian, Katherine, Tomkins, Sabina, Klasnja, Predrag, Murphy, Susan, Shah, Devavrat
We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies that adapt over time. Our goal is to provide inference guarantees for
Externí odkaz:
http://arxiv.org/abs/2202.06891
In distribution compression, one aims to accurately summarize a probability distribution $\mathbb{P}$ using a small number of representative points. Near-optimal thinning procedures achieve this goal by sampling $n$ points from a Markov chain and ide
Externí odkaz:
http://arxiv.org/abs/2111.07941
Autor:
Dwivedi, Raaz, Mackey, Lester
The kernel thinning (KT) algorithm of Dwivedi and Mackey (2021) compresses a probability distribution more effectively than independent sampling by targeting a reproducing kernel Hilbert space (RKHS) and leveraging a less smooth square-root kernel. H
Externí odkaz:
http://arxiv.org/abs/2110.01593