A snapshot of the frontiers of fairness in machine learning
A snapshot of the frontiers of fairness in machine learning Page 1 82 COMMUNICATIONS OF
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …
A tutorial on thompson sampling
Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …
sequentially in a manner that must balance between exploiting what is known to maximize …
The frontiers of fairness in machine learning
The last few years have seen an explosion of academic and popular interest in algorithmic
fairness. Despite this interest and the volume and velocity of work that has been produced …
fairness. Despite this interest and the volume and velocity of work that has been produced …
Introduction to multi-armed bandits
Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …
decisions over time under uncertainty. An enormous body of work has accumulated over the …
Adaptive treatment assignment in experiments for policy choice
Standard experimental designs are geared toward point estimation and hypothesis testing,
while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider …
while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider …
AI and personalization
This chapter reviews the recent developments at the intersection of personalization and AI in
marketing and related fields. We provide a formal definition of personalized policy and …
marketing and related fields. We provide a formal definition of personalized policy and …
Prompt optimization with ease? efficient ordering-aware automated selection of exemplars
Large language models (LLMs) have shown impressive capabilities in real-world
applications. The capability of* in-context learning*(ICL) allows us to adapt an LLM to …
applications. The capability of* in-context learning*(ICL) allows us to adapt an LLM to …
Meta dynamic pricing: Transfer learning across experiments
We study the problem of learning shared structure across a sequence of dynamic pricing
experiments for related products. We consider a practical formulation in which the unknown …
experiments for related products. We consider a practical formulation in which the unknown …
Practical contextual bandits with regression oracles
A major challenge in contextual bandits is to design general-purpose algorithms that are
both practically useful and theoretically well-founded. We present a new technique that has …
both practically useful and theoretically well-founded. We present a new technique that has …
A contextual bandit bake-off
Contextual bandit algorithms are essential for solving many real-world interactive machine
learning problems. Despite multiple recent successes on statistically optimal and …
learning problems. Despite multiple recent successes on statistically optimal and …