153 citations · 816 across the 54 of their papers we have counts for
10 papers · 1 filter
Predictive Multiplicity in Probabilistic Classification
Jamelle Watson-Daniels, David C. Parkes, Berk Ustun
Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or…
Decision-Aware Conditional GANs for Time Series Data
He Sun, Zhun Deng, Hui Chen +1
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation. The framework adopts a multi-Wasserstein l…
From Predictions to Decisions: Using Lookahead Regularization
Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath +1
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks. But when deployed, learned models also affect how users act in…
Finding Friend and Foe in Multi-Agent Games
Jack Serrino, Max Kleiman-Weiner, David C. Parkes +1
Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of coope…
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng, David C. Parkes, Haifeng Xu
Motivated by economic applications such as recommender systems, we study the behavior of stochastic bandits algorithms under \emph{strategic behavior} conducted by rational actors,…
Learning Representations by Humans, for Humans
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji +2
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with impro…