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Found 8 papers, 0 papers with code
Double Matching Under Complementary Preferences
In this paper, we propose a new algorithm for addressing the problem of matching markets with complementary preferences, where agents' preferences are unknown a priori and must be learned from data.
Graph Federated Learning with Hidden Representation Sharing
On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients.
Debiasing Neural Retrieval via In-batch Balancing Regularization
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
Rate-Optimal Contextual Online Matching Bandit
Existing works focus on multi-armed bandit with static preference, but this is insufficient: the two-sided preference changes as along as one-side's contextual information updates, resulting in non-static matching.
Residual Bootstrap Exploration for Stochastic Linear Bandit
We propose a new bootstrap-based online algorithm for stochastic linear bandit problems.
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms.
Online Forgetting Process for Linear Regression Models
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time.
A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components
Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions.
