Search Results for author: Alex Pentland

Found 24 papers, 4 papers with code

Investigating and Modeling the Dynamics of Long Ties

no code implementations22 Sep 2021 Ding Lyu, Yuan Yuan, Lin Wang, Xiaofan Wang, Alex Pentland

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks.

Adaptive Methods for Real-World Domain Generalization

no code implementations CVPR 2021 Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, Dhruv Mahajan

We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains.

Domain Generalization

What are the key components of an entrepreneurial ecosystem in a developing economy? A longitudinal empirical study on technology business incubators in China

no code implementations15 Mar 2021 Xiangfei Yuan, Haijing Hao, Chenghua Guan, Alex Pentland

Since the 1980s, technology business incubators (TBIs), which focus on accelerating businesses through resource sharing, knowledge agglomeration, and technology innovation, have become a booming industry.

Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation

no code implementations8 Mar 2021 Abhimanyu Dubey, Alex Pentland

Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic treatment regimes, distributed control, and federated learning.

Federated Learning Multi-agent Reinforcement Learning

Differentially-Private Federated Linear Bandits

1 code implementation NeurIPS 2020 Abhimanyu Dubey, Alex Pentland

The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning.

Federated Learning

Cooperative Multi-Agent Bandits with Heavy Tails

no code implementations14 Aug 2020 Abhimanyu Dubey, Alex Pentland

We study the heavy-tailed stochastic bandit problem in the cooperative multi-agent setting, where a group of agents interact with a common bandit problem, while communicating on a network with delays.

Kernel Methods for Cooperative Multi-Agent Contextual Bandits

no code implementations14 Aug 2020 Abhimanyu Dubey, Alex Pentland

For this problem, we propose \textsc{Coop-KernelUCB}, an algorithm that provides near-optimal bounds on the per-agent regret, and is both computationally and communicatively efficient.

Decision Making Multi-Armed Bandits

Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities

no code implementations1 Jun 2020 Yan Leng, Tara Sowrirajan, Alex Pentland

While homophily drives the formation of communities with similar characteristics, social influence occurs both within and between communities.

Decision Making

Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements

1 code implementation21 May 2020 Yan Leng, Yujia Zhai, Shaojing Sun, Yifei Wu, Jordan Selzer, Sharon Strover, Julia Fensel, Alex Pentland, Ying Ding

COVID-19 resulted in an infodemic, which could erode public trust, impede virus containment, and outlive the pandemic itself.

Social and Information Networks Computers and Society

DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning

no code implementations30 Oct 2019 Michiel A. Bakker, Duy Patrick Tu, Humberto Riverón Valdés, Krishna P. Gummadi, Kush R. Varshney, Adrian Weller, Alex Pentland

We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown objectives.

Fairness Representation Learning

Thompson Sampling on Symmetric $α$-Stable Bandits

no code implementations8 Jul 2019 Abhimanyu Dubey, Alex Pentland

Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance.

Bayesian Inference Decision Making +1

Gift Contagion in Online Groups: Evidence From WeChat Red Packets

no code implementations24 Jun 2019 Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang

Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.

Experimental Design

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning

no code implementations16 Feb 2019 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, Peter Krafft, Esteban Moro, Alex Pentland

A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.

Learning Quadratic Games on Networks

no code implementations ICML 2020 Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland

Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations.

Active Fairness in Algorithmic Decision Making

no code implementations28 Sep 2018 Alejandro Noriega-Campero, Michiel A. Bakker, Bernardo Garcia-Bulle, Alex Pentland

Recent work has proposed optimal post-processing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration.

Classification Decision Making +2

Towards a Design Philosophy for Interoperable Blockchain Systems

no code implementations15 May 2018 Thomas Hardjono, Alexander Lipton, Alex Pentland

In this paper we discuss a design philosophy for interoperable blockchain systems, using the design philosophy of the Internet architecture as the basis to identify key design principles.

Cryptography and Security

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

no code implementations30 Nov 2017 Dhaval Adjodah, Dan Calacci, Yan Leng, Peter Krafft, Esteban Moro, Alex Pentland

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning.

Stigmergy-based modeling to discover urban activity patterns from positioning data

no code implementations12 Apr 2017 Antonio L. Alfeo, Mario G. C. A. Cimino, Sara Egidi, Bruno Lepri, Alex Pentland, Gigliola Vaglini

However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling.

Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs

no code implementations17 Nov 2016 Yaniv Altshuler, Alex Pentland, Shlomo Bekhor, Yoram Shiftan, Alfred Bruckstein

Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones' flying routes.

Bots as Virtual Confederates: Design and Ethics

no code implementations2 Nov 2016 Peter M. Krafft, Michael Macy, Alex Pentland

In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation.

Human collective intelligence as distributed Bayesian inference

no code implementations5 Aug 2016 Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland

To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.

Bayesian Inference Decision Making

Modeling Human Ad Hoc Coordination

1 code implementation11 Feb 2016 Peter M. Krafft, Chris L. Baker, Alex Pentland, Joshua B. Tenenbaum

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action.

Enigma: Decentralized Computation Platform with Guaranteed Privacy

no code implementations10 Jun 2015 Guy Zyskind, Oz Nathan, Alex Pentland

A peer-to-peer network, enabling different parties to jointly store and run computations on data while keeping the data completely private.

Cryptography and Security Distributed, Parallel, and Cluster Computing

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