Alexander Peysakhovich | DeepAI

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Christian Kroer
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Adam Lerer
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Arjun Seshadri
6 publications

∙ 06/29/2023

Diagnosis Uncertain Models For Medical Risk Prediction

We consider a patient risk models which has access to patient features s...

0 Alexander Peysakhovich, et al. ∙

∙ 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 ...

0 Alexander Peysakhovich, et al. ∙

∙ 10/05/2022

Parity in Markets – Methods, Costs, and Consequences

Fisher markets are those where buyers with budgets compete for scarce it...

0 Alexander Peysakhovich, et al. ∙

∙ 06/17/2021

An Attract-Repel Decomposition of Undirected Networks

Dot product latent space embedding is a common form of representation le...

0 Alexander Peysakhovich, et al. ∙

∙ 06/16/2020

Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions

Can we predict how well a team of individuals will perform together? How...

0 Tom Yan, et al. ∙

∙ 09/24/2019

Scalable Fair Division for 'At Most One' Preferences

Allocating multiple scarce items across a set of individuals is an impor...

0 Christian Kroer, et al. ∙

∙ 06/06/2019

Fair Division Without Disparate Impact

We consider the problem of dividing items between individuals in a way t...

0 Alexander Peysakhovich, et al. ∙

∙ 04/03/2019

Robust Multi-agent Counterfactual Prediction

We consider the problem of using logged data to make predictions about w...

0 Alexander Peysakhovich, et al. ∙

∙ 02/08/2019

Discovering Context Effects from Raw Choice Data

Many applications in preference learning assume that decisions come from...

0 Arjun Seshadri, et al. ∙

∙ 01/18/2019

Computing large market equilibria using abstractions

Computing market equilibria is an important practical problem for market...

4 Christian Kroer, et al. ∙

∙ 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...

0 Alexander Peysakhovich, et al. ∙

∙ 07/24/2018

Improving pairwise comparison models using Empirical Bayes shrinkage

Comparison data arises in many important contexts, e.g. shopping, web cl...

0 Stephen Ragain, et al. ∙

∙ 06/26/2018

Learning Existing Social Conventions in Markov Games

In order for artificial agents to coordinate effectively with people, th...

0 Adam Lerer, et al. ∙

∙ 06/26/2018

Learning Social Conventions in Markov Games

Social conventions - arbitrary ways to organize group behavior - are an ...

0 Adam Lerer, et al. ∙

∙ 10/19/2017

Consequentialist conditional cooperation in social dilemmas with imperfect information

Social dilemmas, where mutual cooperation can lead to high payoffs but p...

0 Alexander Peysakhovich, et al. ∙

∙ 09/08/2017

Prosocial learning agents solve generalized Stag Hunts better than selfish ones

Deep reinforcement learning has become an important paradigm for constru...

0 Alexander Peysakhovich, et al. ∙

∙ 07/04/2017

Maintaining cooperation in complex social dilemmas using deep reinforcement learning

Social dilemmas are situations where individuals face a temptation to in...

0 Adam Lerer, et al. ∙

∙ 01/04/2017

Learning causal effects from many randomized experiments using regularized instrumental variables

Scientific and business practices are increasingly resulting in large co...

0 Alexander Peysakhovich, et al. ∙

∙ 12/21/2016

Multi-Agent Cooperation and the Emergence of (Natural) Language

The current mainstream approach to train natural language systems is to ...

0 Angeliki Lazaridou, et al. ∙

∙ 11/08/2016

Combining observational and experimental data to find heterogeneous treatment effects

Every design choice will have different effects on different units. Howe...

0 Alexander Peysakhovich, et al. ∙

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