128 citations · 261 across the 12 of their papers we have counts for
15 papers
Generative Adversarial Model-Based Optimization via Source Critic Regularization
Michael S. Yao, Yimeng Zeng, Hamsa Bastani +3
Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonl…
Rethinking Algorithmic Fairness for Human-AI Collaboration
Haosen Ge, Hamsa Bastani, Osbert Bastani
Existing approaches to algorithmic fairness aim to ensure equitable outcomes if human decision-makers comply perfectly with algorithmic decisions. However, perfect compliance with…
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
Xinmeng Huang, Kan Xu, Donghwan Lee +3
Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datase…
Decision-Aware Learning for Optimizing Health Supply Chains
Tsai-Hsuan Chung, Vahid Rostami, Hamsa Bastani +1
We study the problem of allocating limited supply of medical resources in developing countries, in particular, Sierra Leone. We address this problem by combining machine learning (…
Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms
Vashist Avadhanula, Omar Abdul Baki, Hamsa Bastani +17
We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models…
Multitask Learning and Bandits via Robust Statistics
Kan Xu, Hamsa Bastani
Decision-makers often simultaneously face many related but heterogeneous learning problems. For instance, a large retailer may wish to learn product demand at different stores to s…
Uniformly Conservative Exploration in Reinforcement Learning
Wanqiao Xu, Jason Yecheng Ma, Kan Xu +2
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes. We propose a natural constraint on exploration -…
Robust Generalization of Quadratic Neural Networks via Function Identification
Kan Xu, Hamsa Bastani, Osbert Bastani
A key challenge facing deep learning is that neural networks are often not robust to shifts in the underlying data distribution. We study this problem from the perspective of the s…
Improving Human Sequential Decision-Making with Reinforcement Learning
Hamsa Bastani, Osbert Bastani, Wichinpong Park Sinchaisri
Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, can be complicated -- e.g., decision outcomes…
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Kan Xu, Xuanyi Zhao, Hamsa Bastani +1
Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare. To leverage this informat…
Meta Dynamic Pricing: Transfer Learning Across Experiments
Hamsa Bastani, David Simchi-Levi, Ruihao Zhu
We study the problem of learning shared structure \emph{across} a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknow…
Predicting with Proxies: Transfer Learning in High Dimension
Hamsa Bastani
Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true predictive task of interest, a…
Interpretability via Model Extraction
Osbert Bastani, Carolyn Kim, Hamsa Bastani
The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions. We propose an approach calle…
Interpreting Blackbox Models via Model Extraction
Osbert Bastani, Carolyn Kim, Hamsa Bastani
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex,…
Mostly Exploration-Free Algorithms for Contextual Bandits
Hamsa Bastani, Mohsen Bayati, Khashayar Khosravi
The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current e…