Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Dwivedi, Raaz"'
The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size significantly smaller than the input points, KT is known to speed up unsupervi
Externí odkaz:
http://arxiv.org/abs/2410.13749
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate distribution for each unit-outcome entry, such as the distribution of a
Externí odkaz:
http://arxiv.org/abs/2410.13381
We introduce the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both observed and unobserved matrix entries. This is a generalization
Externí odkaz:
http://arxiv.org/abs/2410.13112
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