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Found 11 papers, 2 papers with code
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms.
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food
We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that rescues food from farms and elsewhere to feed the hungry.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
We then propose a novel framework for probabilistic anomaly attribution that allows us to not only compute attribution scores as the predictive mean but also quantify the uncertainty of those scores.
Black-Box Anomaly Attribution
When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation?
Diagnostic Spatio-temporal Transformer with Faithful Encoding
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency.
Decentralized Collaborative Learning with Probabilistic Data Protection
We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others.
Anomaly Attribution with Likelihood Compensation
In either case, one would ideally want to compute a ``responsibility score'' indicative of the extent to which an input variable is responsible for the anomalous output.
Cardinality-Regularized Hawkes-Granger Model
In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches.
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner.
Directed Graph Auto-Encoders
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels.
