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Found 15 papers, 3 papers with code
Generative Adversarial Bayesian Optimization for Surrogate Objectives
To address this limitation, we propose generative adversarial Bayesian optimization (GABO) using adaptive source critic regularization, a task-agnostic framework for Bayesian optimization that employs a Lipschitz-bounded source critic model to constrain the optimization trajectory to regions where the surrogate function is reliable.
Rethinking Fairness for Human-AI Collaboration
However, we show that it may be infeasible to design algorithmic recommendations that are simultaneously fair in isolation, compliance-robustly fair, and more accurate than the human policy; thus, if our goal is to improve the equity and accuracy of human-AI collaboration, it may not be desirable to enforce traditional fairness constraints.
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits
MOLAR improves the dependence of the estimation error on the data dimension, compared to independent least squares estimates.
Decision-Aware Learning for Optimizing Health Supply Chains
We apply our framework to optimize the distribution of essential medicines in collaboration with policymakers in Sierra Leone; highly uncertain demand and limited budgets currently result in excessive unmet demand.
Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms
We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms.
Multitask Learning and Bandits via Robust Statistics
Decision-makers often simultaneously face many related but heterogeneous learning problems.
Uniformly Conservative Exploration in Reinforcement Learning
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes.
Robust Generalization of Quadratic Neural Networks via Function Identification
We study this problem from the perspective of the statistical concept of parameter identification.
Improving Human Sequential Decision-Making with Reinforcement Learning
Workers spend a significant amount of time learning how to make good decisions.
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
However, learning word embeddings from new domains with limited training data can be challenging, because the meaning/usage may be different in the new domain, e. g., the word ``positive'' typically has positive sentiment, but often has negative sentiment in medical notes since it may imply that a patient tested positive for a disease.
Meta Dynamic Pricing: Transfer Learning Across Experiments
We study the problem of learning shared structure \emph{across} a sequence of dynamic pricing experiments for related products.
Predicting with Proxies: Transfer Learning in High Dimension
Predictive analytics is increasingly used to guide decision-making in many applications.
Interpretability via Model Extraction
The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions.
Interpreting Blackbox Models via Model Extraction
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
Mostly Exploration-Free Algorithms for Contextual Bandits
We prove that this algorithm is rate optimal without any additional assumptions on the context distribution or the number of arms.
