44 citations · 44 across the 3 of their papers we have counts for
7 papers
Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu +1
We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume th…
Surprisingly Popular Voting Recovers Rankings, Surprisingly!
Hadi Hosseini, Debmalya Mandal, Nisarg Shah +1
The wisdom of the crowd has long become the de facto approach for eliciting information from individuals or experts in order to predict the ground truth. However, classical democra…
Meta-Learning with Graph Neural Networks: Methods and Applications
Debmalya Mandal, Sourav Medya, Brian Uzzi +1
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. H…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
Weighted Tensor Completion for Time-Series Causal Inference
Debmalya Mandal, David Parkes
Marginal Structural Models (MSM) are the most popular models for causal inference from time-series observational data. However, they have two main drawbacks: (a) they do not captur…
Calibrated Fairness in Bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis +2
We study fairness within the stochastic, \emph{multi-armed bandit} (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" to t…