Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Shanmugam, Karthikeyan"'
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally
Externí odkaz:
http://arxiv.org/abs/2406.05937
We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploys a discrete Markov chain called the heat bath dynamics (or the Glaube
Externí odkaz:
http://arxiv.org/abs/2405.17035
Autor:
Artman, Conor M., Mate, Aditya, Nwankwo, Ezinne, Heching, Aliza, Idé, Tsuyoshi, Navrátil, Jiří, Shanmugam, Karthikeyan, Sun, Wei, Varshney, Kush R., Goldkind, Lauri, Kroch, Gidi, Sawyer, Jaclyn, Watson, Ian
We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experienci
Externí odkaz:
http://arxiv.org/abs/2403.10638
This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations
Externí odkaz:
http://arxiv.org/abs/2402.00849
We consider the problem of \emph{blocked} collaborative bandits where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within
Externí odkaz:
http://arxiv.org/abs/2311.03376
This paper focuses on causal representation learning (CRL) under a general nonparametric latent causal model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability resu
Externí odkaz:
http://arxiv.org/abs/2310.15450
Autor:
Havaldar, Shreyas, Sharma, Navodita, Sareen, Shubhi, Shanmugam, Karthikeyan, Raghuveer, Aravindan
Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This
Externí odkaz:
http://arxiv.org/abs/2310.08056
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
Covariate shift in the test data is a common practical phenomena that can significantly downgrade both the accuracy and the fairness performance of the model. Ensuring fairness across different sensitive groups under covariate shift is of paramount i
Externí odkaz:
http://arxiv.org/abs/2310.07535
Autor:
Zhang, Jiaqi, Squires, Chandler, Greenewald, Kristjan, Srivastava, Akash, Shanmugam, Karthikeyan, Uhler, Caroline
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. Such a representation is identifiable if the latent model that explains the data is unique. In this paper, we focus o
Externí odkaz:
http://arxiv.org/abs/2307.06250