no code implementations • 21 Nov 2023 • Guy Zyskind, Tobin South, Alex Pentland
While the flexible capabilities of large language models (LLMs) allow them to answer a range of queries based on existing learned knowledge, information retrieval to augment generation is an important tool to allow LLMs to answer questions on information not included in pre-training data.
no code implementations • 7 Jun 2023 • Ziv Epstein, Aaron Hertzmann, Laura Herman, Robert Mahari, Morgan R. Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld, Hany Farid, Neil Leach, Alex Pentland, Olga Russakovsky
A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation.
no code implementations • 24 Sep 2022 • Yanni Yang, Alex Pentland, Esteban Moro
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities.
no code implementations • 8 Jul 2022 • Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland
We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties.
no code implementations • 27 May 2022 • Abhimanyu Dubey, Alex Pentland
In this paper, we investigate the stochastic bandit problem under two settings - (a) when the agents wish to make their communication private with respect to the action sequence, and (b) when the agents can be byzantine, i. e., they provide (stochastically) incorrect information.
1 code implementation • 9 Dec 2021 • Xavier Thomas, Dhruv Mahajan, Alex Pentland, Abhimanyu Dubey
In this paper, we propose a domain-adaptive approach to this problem, which operates in two steps: (a) we cluster training data within a carefully chosen feature space to create pseudo-domains, and (b) using these pseudo-domains we learn a domain-adaptive classifier that makes predictions using information about both the input and the pseudo-domain it belongs to.
Ranked #16 on Domain Generalization on PACS
no code implementations • NeurIPS 2021 • Udari Madhushani, Abhimanyu Dubey, Naomi Ehrich Leonard, Alex Pentland
However, most research for this problem focuses exclusively on the setting with perfect communication, whereas in most real-world distributed settings, communication is often over stochastic networks, with arbitrary corruptions and delays.
1 code implementation • 22 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.
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.
no code implementations • 15 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.
no code implementations • 8 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.
1 code implementation • NeurIPS 2020 • Abhimanyu Dubey, Alex Pentland
The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning.
1 code implementation • 20 Sep 2020 • Yan Leng, Rodrigo Ruiz, Xiaowen Dong, Alex Pentland
Recommender systems (RS) are ubiquitous in the digital space.
Ranked #1 on Recommendation Systems on YahooMusic Monti (using extra training data)
no code implementations • 14 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.
no code implementations • 14 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.
no code implementations • 1 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.
1 code implementation • 21 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
no code implementations • 30 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.
no code implementations • 8 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.
no code implementations • 24 Jun 2019 • Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang
Using data on 36 million online red packet gifts on a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.
no code implementations • 16 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.
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.
no code implementations • 28 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.
no code implementations • 15 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
no code implementations • 30 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.
no code implementations • 12 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.
no code implementations • 17 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.
no code implementations • 2 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.
no code implementations • 5 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.
1 code implementation • 11 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.
no code implementations • 10 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