Search Results for author:
Found 31 papers, 6 papers with code
Enigma: Decentralized Computation Platform with Guaranteed Privacy
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
Modeling Human Ad Hoc Coordination
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.
Human collective intelligence as distributed Bayesian inference
To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.
Bots as Virtual Confederates: Design and Ethics
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.
Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs
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.
Stigmergy-based modeling to discover urban activity patterns from positioning data
However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling.
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies
We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning.
Towards a Design Philosophy for Interoperable Blockchain Systems
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
Active Fairness in Algorithmic Decision Making
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.
Learning Quadratic Games on Networks
Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations.
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
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.
Gift Contagion in Online Groups: Evidence From Virtual Red Packets
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.
Thompson Sampling on Symmetric $α$-Stable Bandits
Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance.
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning
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.
Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements
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
Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities
While homophily drives the formation of communities with similar characteristics, social influence occurs both within and between communities.
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
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.
Cooperative Multi-Agent Bandits with Heavy Tails
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.
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective
Recommender systems (RS) are ubiquitous in the digital space.
Ranked #1 on
Recommendation Systems
on YahooMusic Monti
(using extra training data)
Differentially-Private Federated Linear Bandits
The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning.
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation
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.
What are the key components of an entrepreneurial ecosystem in a developing economy? A longitudinal empirical study on technology business incubators in China
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.
Adaptive Methods for Real-World Domain Generalization
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.
Investigating and Modeling the Dynamics of Long Ties
Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks.
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
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.
Adaptive Methods for Aggregated Domain Generalization
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
Private and Byzantine-Proof Cooperative Decision-Making
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.
Private independence testing across two parties
We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties.
Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities.
Art and the science of generative AI: A deeper dive
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.
Don't forget private retrieval: distributed private similarity search for large language models
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.
