We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents' reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An age…
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48 results for “community discovery”
The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery…
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, w…
The false discovery rate (FDR)---the expected fraction of spurious discoveries among all the discoveries---provides a popular statistical assessment of the reproducibility of scientific studies in various disciplines. In this work, we introduce a new method for controlling the FDR in meta-analysis of many decentralized…
A communication-efficient method controls FDR in network settings.
problem Controlling FDR in networks with limited communication.
method Sample-and-Forward: a flexible procedure for multihop networks.
result Nodes can control FDR without sharing p-values, achieving power and FDR control.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top polit…
Paper presents a workflow for reliable unsupervised learning in science.
problem Lack of standardization in unsupervised learning workflows for reproducible scientific discoveries.
method Structured workflow including data preparation, modeling, validation, and communication.
result Illustrates the importance of validation in unsupervised learning.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
The analysis of temporal networks has a wide area of applications in a world of technological advances. An important aspect of temporal network analysis is the discovery of community structures. Real data networks are often very large and the communities are observed to have a hierarchical structure referred to as mult…
AI+MPS workshop aims to strengthen AI's role in science.
problem AI's potential to enhance scientific discovery and education.
method Proposes activities and strategic priorities to strengthen AI-MPS link.
result AI and science are becoming increasingly intertwined.
Proposes VILMAP for finding motifs and segmenting words in time series.
problem Finding motifs and segmenting words in time series data.
method Self-Organizing Maps with variable input length.
result VILMAP identifies motifs of varying lengths and avoids catastrophic forgetting.
Aggregation distorts causal discovery results but recovery is possible with partial linearity or prior.
problem Understanding how temporal aggregation affects causal discovery in aggregated data.
method Functional consistency and conditional independence consistency methods.
result Causal discovery results may be distorted by aggregation, but recovery is possible with certain conditions.
EDL discovers state-covering skills without relying on task rewards.
problem Discovering skills in reinforcement learning without a task-oriented reward function.
method EDL optimizes information-theoretic objective using different machinery to address coverage problem.
result EDL discovers state-covering skills more effectively than existing methods.
TimeGraph creates synthetic datasets for robust time-series causal discovery.
problem Lack of reliable synthetic benchmark datasets for robust time-series causal discovery.
method Developed comprehensive synthetic datasets with temporal properties, including trends, seasonality, and noise.
result Demonstrated significant variations in algorithm performance under realistic temporal conditions.
Machine learning enhances cosmology through new tools and data analysis.
problem Leveraging machine learning in cosmology for better understanding of the universe.
method Development of new computational tools, data analysis techniques, and community building.
result Substantial potential in cosmology remains untapped with machine learning.
Combines SBMs and graph neural nets for graph embeddings.
problem Discovering community structure and link prediction on graphs.
method Sparse variational autoencoder integrating SBMs and graph neural nets.
result Encouraging link prediction results with interpretable latent structure.
Develops a framework for causal structure learning using both interventional and observational data.
problem Lack of identifiability of causal structures with only observational data.
method Bilevel polynomial optimization (Bloom) framework for causal structure discovery from interventional and observational data.
result Bloom framework provides convergence and optimality guarantees, surpassing other learning algorithms in experiments.
Improved error feedback method reduces communication complexity in distributed training.
problem Improving convergence in distributed training methods with compression techniques.
method Introduced and improved a modern form of error feedback (EF21) by reducing its theoretical communication complexity.
result Communication complexity depends on the arithmetic mean of smoothness parameters, leading to significant improvements.
Bayesian model detects communities in networks with covariates.
problem Extracting meaningful community clusters from network data with covariates.
method Proposes a Bayesian stochastic block model with a covariate-dependent random partition prior.
result Can learn the number of communities via posterior inference.
Study uses LLMs to automate data insights discovery.
problem Extracting relevant insights from large data sets.
method Capture the Flag principle, LLMs, reasoning, code generation.
result LLMs can recognize meaningful data insights.
New benchmarks show LLMs struggle with causal discovery.
problem Leveraging LLMs for causal discovery is unreliable due to dataset leakage.
method Developing science-grounded benchmarks and hybrid methods combining LLM predictions with statistical analysis.
result LLMs perform poorly on novel, real-world scientific studies compared to classical methods.
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as …
The waggle dance that honeybees perform is an astonishing way of communicating the location of food source. After over 60 years of its discovery, researchers still use manual labeling by watching hours of dance videos to detect different transitions between dance components thus extracting information regarding the dis…
Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still d…
Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used …
Paper proposes a method to monitor research topic evolution.
problem Difficulty in tracking research topic diffusion and evolution.
method Deep Non-negative Autoencoder with information divergence measurement.
result Identifies evolution of research topics and discovers topic diffusions.
A Constrained Coupled Matrix-Tensor Factorization for Learning Time-evolving and Emerging Topicscs.IR
Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving t…
Paper proposes a new dataset for group anomaly detection in physics.
problem Group anomaly detection in physics.
method Development of a synthetic benchmark dataset and comparison of anomaly detection techniques.
result Demonstrated performance of anomaly detection techniques on the new dataset.
AutoML struggles with climate change data, but offers potential improvements.
problem Improving machine learning for climate change applications.
method Benchmarked AutoML libraries on climate modeling, wind power, and catalyst discovery.
result Current AutoML techniques fail to surpass human-designed models in climate change applications.
Causal Autoregressive Flowsstat.ML
Causal autoregressive flows enable accurate causal inference and prediction.
problem Causal discovery and interventional predictions in machine learning.
method Autoregressive normalizing flows with fixed variable orderings.
result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.
Despite the overwhelming success of the existing Social Networking Services (SNS), their centralized ownership and control have led to serious concerns in user privacy, censorship vulnerability and operational robustness of these services. To overcome these limitations, Distributed Social Networks (DSN) have recently b…
We present a probabilistic framework for overlapping community discovery and link prediction for relational data, given as a graph. The proposed framework has: (1) a deep architecture which enables us to infer multiple layers of latent features/communities for each node, providing superior link prediction performance o…
XIMP improves molecular property prediction by integrating multiple graph representations.
problem Graph neural networks struggle in data-scarce regimes and fail to surpass traditional methods.
method Cross-graph inter-message passing with multiple graph abstractions.
result XIMP outperforms state-of-the-art baselines across diverse molecular property tasks.
Survey of deep learning models for scientific discovery.
problem Identifying which scientific problems are most suitable for deep learning.
method Overview of deep learning models, tasks, training methods, and techniques.
result Helps accelerate deep learning use in scientific domains.
Survey on robust data representation learning from a knowledge flow perspective.
problem Learning robust visual representation from noisy, incomplete or mismatched data.
method Low-rank modeling for robust knowledge recovery, transfer, and fusion.
result Unified formulation for robust knowledge discovery and transfer/fusion.
With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples. We show how learning cluster structure using edge features naturally and simultaneously determines the most likely number of clusters and addresses data scale issues. These results are parti…
The paper evaluates methods for explaining deep learning in security.
problem Understanding the predictions of deep learning models in security applications.
method Developed criteria to compare and evaluate six explanation methods.
result Significant differences exist between the methods, leading to recommendations.
Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learningcs.LG
Entropy regularization improves sparse model discovery in federated learning.
problem Sparse model discovery in federated learning with limited data.
method Entropy regularization of gate distributions for probabilistic sparse model exploration.
result Entropy regularization leads to better sparse model recovery and performance.
Economic returns of research: the Pareto law and its implicationscond-mat.stat-mech
At what level should government or companies support research? This complex multi-faceted question encompasses such qualitative bonus as satisfying natural human curiosity, the quest for knowledge and the impact on education and culture, but one of its most scrutinized component reduces to the assessment of economic pe…
Introduces Q-structures for mechanics using advanced geometry.
problem Challenges in classical differential geometric constructions in mechanics.
method Explains the use of Q-structures and differential graded manifolds.
result Q-structure preserving integrators can be useful in mechanics.
The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow collective motions governing the long-time conformational dynamics of a system. It is challenging to intuit these slow CVs for all but the simpl…
EUREKA builds classifiers that use surprising features.
problem Building classifiers that are interesting, not just accurate.
method Uses large language models to rank features by interestingness and builds interpretable classifiers using only selected features.
result EUREKA discovers non-obvious yet predictive features, improving accuracy and offering insights.
Differentiable causal discovery methods perform robustly under model violations.
problem Causal discovery algorithms struggle with real-world data due to unverifiable causal assumptions.
method Benchmarked differentiable causal discovery methods under eight model assumption violations.
result Differentiable causal discovery methods exhibit robust performance under Structural Hamming Distance and Structural Intervention Distance metrics.
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
Paper proposes efficient sample collection strategy for RL.
problem Balancing exploration and exploitation in reinforcement learning.
method Decoupled approach with objective-specific and objective-agnostic strategies.
result Improved or novel sample complexity guarantees for various RL settings.
Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a…