758 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Microsoft (United States)US53 papers
- Cornell UniversityUS15 papers
- Carnegie Mellon UniversityUS9 papers
- University of Massachusetts AmherstUS8 papers
- Princeton UniversityUS7 papers
- Columbia UniversityUS6 papers
- Microsoft Research (United Kingdom)GB6 papers
- University of PennsylvaniaUS6 papers
- University of Maryland, College ParkUS5 papers
- University of MichiganUS5 papers
- Harvard UniversityUS4 papers
- Stanford UniversityUS4 papers
9 papers · 1 filter
Adversarial Bandits with Knapsacks
Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire +1
We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a we…
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
Shira Mitchell, Eric Potash, Solon Barocas +2
A recent flurry of research activity has attempted to quantitatively define "fairness" for decisions based on statistical and machine learning (ML) predictions. The rapid growth of…
A Flexible Design for Funding Public Goods
Vitalik Buterin, Zoe Hitzig, E. Glen Weyl
We propose a design for philanthropic or publicly-funded seeding to allow (near) optimal provision of a decentralized, self-organizing ecosystem of public goods. The concept extend…
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent respons…
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan +1
Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of cu…
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Sudha Rao, Hal Daumé
Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for th…
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
We use surrogate losses to obtain several new regret bounds and new algorithms for contextual bandit learning. Using the ramp loss, we derive new margin-based regret bounds in term…
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík +2
We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach enco…
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally…