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06/29/2023
Diagnosis Uncertain Models For Medical Risk Prediction
We consider a patient risk models which has access to patient features s...
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03/13/2023
Observational Causal Inference in Novel Diseases: A Case Study of COVID-19
A key issue for all observational causal inference is that it relies on ...
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10/05/2022
Parity in Markets – Methods, Costs, and Consequences
Fisher markets are those where buyers with budgets compete for scarce it...
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06/17/2021
An Attract-Repel Decomposition of Undirected Networks
Dot product latent space embedding is a common form of representation le...
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06/16/2020
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
Can we predict how well a team of individuals will perform together? How...
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09/24/2019
Scalable Fair Division for 'At Most One' Preferences
Allocating multiple scarce items across a set of individuals is an impor...
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06/06/2019
Fair Division Without Disparate Impact
We consider the problem of dividing items between individuals in a way t...
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04/03/2019
Robust Multi-agent Counterfactual Prediction
We consider the problem of using logged data to make predictions about w...
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02/08/2019
Discovering Context Effects from Raw Choice Data
Many applications in preference learning assume that decisions come from...
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01/18/2019
Computing large market equilibria using abstractions
Computing market equilibria is an important practical problem for market...
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11/19/2018
Reinforcement learning and inverse reinforcement learning with system 1 and system 2
Inferring a person's goal from their behavior is an important problem in...
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07/24/2018
Improving pairwise comparison models using Empirical Bayes shrinkage
Comparison data arises in many important contexts, e.g. shopping, web cl...
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06/26/2018
Learning Existing Social Conventions in Markov Games
In order for artificial agents to coordinate effectively with people, th...
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06/26/2018
Learning Social Conventions in Markov Games
Social conventions - arbitrary ways to organize group behavior - are an ...
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10/19/2017
Consequentialist conditional cooperation in social dilemmas with imperfect information
Social dilemmas, where mutual cooperation can lead to high payoffs but p...
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09/08/2017
Prosocial learning agents solve generalized Stag Hunts better than selfish ones
Deep reinforcement learning has become an important paradigm for constru...
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07/04/2017
Maintaining cooperation in complex social dilemmas using deep reinforcement learning
Social dilemmas are situations where individuals face a temptation to in...
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01/04/2017
Learning causal effects from many randomized experiments using regularized instrumental variables
Scientific and business practices are increasingly resulting in large co...
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12/21/2016
Multi-Agent Cooperation and the Emergence of (Natural) Language
The current mainstream approach to train natural language systems is to ...
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11/08/2016