CCN estimates full potential outcome distributions without restrictive assumptions.
problem Estimating CATE is insufficient; full potential outcome distributions provide greater insights.
method Collaborating Causal Networks (CCN) learns full potential outcome distributions without restrictive assumptions.
result CCN learns distributions that asymptotically capture true potential outcome distributions.
Proposes a new method to handle data heterogeneity in causal inference.
problem Challenges of collaborating between different data centers due to heterogeneity.
method Collaborative inverse propensity score weighting estimator to adjust for distribution shift.
result Significant improvements over traditional meta-analysis methods when dealing with increased heterogeneity.
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
Propensity score (PS) based estimators are increasingly used for causal inference in observational studies. However, model selection for PS estimation in high-dimensional data has received little attention. In these settings, PS models have traditionally been selected based on the goodness-of-fit for the treatment mech…
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
CausalBench aims to advance causal learning research with a transparent platform.
problem Lack of unified benchmark datasets, algorithms, metrics, and evaluation interfaces for causal learning.
method Introduces CausalBench, a flexible benchmark framework for causal analysis and machine learning.
result Promotes scientific collaboration, reproducibility, and awareness in causal learning research.
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating …
A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.
problem Privacy and efficiency in collaborative machine learning across different data owners.
method Decomposes neural network steps for privacy-preserving training; novel protocol for information leakage.
result Efficient training with stable performance and significant speedup.
Proposes MANE for multi-view network embedding, improving node representations.
problem Learning low-dimensional representations from multiple views of networks.
method MANE combines diversity and collaboration, including second-order collaboration, and attention-based extension MANE+.
result MANE+ outperforms state-of-the-art approaches on real-world multi-view networks.
Shifu2 discovers advisor-advisee relationships in collaboration networks.
problem Discovering hidden advisor-advisee relationships in scientific collaboration networks.
method Network Representation Learning (NRL) model, considering both network structure and node/edge semantics.
result Improved stability and effectiveness compared to state-of-the-art methods.
A novel method for efficient CDRL over wireless networks.
problem Challenges in collaborative deep reinforcement learning over wireless networks.
method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.
result Superior performance compared to state-of-the-art baselines.
Study on collaboration vs. independent data collection in sensor networks.
problem Impact of sensor correlation on data collection strategies.
method Analysis of Fisher information and Cramer-Rao bound.
result Optimal strategy involves transferring non-immediate information for improved estimation.
We introduce collaborative learning in which multiple classifier heads of the same network are simultaneously trained on the same training data to improve generalization and robustness to label noise with no extra inference cost. It acquires the strengths from auxiliary training, multi-task learning and knowledge disti…
Enhances student diversity in collaborative learning.
problem Student homogenization in large groups.
method Random routing, diverse feature sets, and random subgroup imitation.
result Significantly outperforms state-of-the-art approaches.
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of pr…
Optimizes resource allocation for distributed parameter estimation in sensor networks.
problem Maximizing accuracy in parameter estimation with limited resources.
method Formulates a data collection and collaboration policy design problem as a Fisher information maximization problem. Proposes multi-armed bandit algorithms for learning the optimal policy.
result Identifies optimal data collection and collaboration policies that balance resource use and estimation accuracy.
This paper analyzes P2P collaborative insurance products and network structure impact.
problem Analyzing P2P collaborative insurance products and their network structure impact.
method Examined a P2P insurance product with reciprocal risk sharing contracts, studied network structure impact on risk reduction, and discussed optimal reciprocal commitments.
result The network structure, particularly the distribution of degrees, significantly impacts risk reduction in P2P insurance products.
In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since secondary users (SU) usually have no prior knowledge on the environment, they need to learn the availability of each channel through sensing techn…
RGCF improves collaborative filtering by refining graph convolution embeddings.
problem GCN-based recommendation models introduce noise and redundancy, limiting high-order connectivity capture.
method Developed RGCF, a new GCN-based Collaborative Filtering model with redesigned embeddings.
result RGCF significantly outperforms state-of-the-art models on public datasets.
Reinterprets Granger causality with causal Bayesian networks and Reichenbach's principles.
problem Lack of a rigorous causal foundation in Granger causality.
method Reinterpreting Granger causality through Reichenbach's principles and causal Bayesian networks, implementing as c-GC.
result c-GC provides a more principled framework for causal discovery in observational datasets.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the wor…
New method quantifies intrinsic causal contributions in neural networks.
problem Measuring the causal influence of input features in deep neural networks.
method Proposes an identifiable generative post-hoc framework to quantify intrinsic causal contributions (ICC) as structural causal models.
result ICC generates more intuitive and reliable explanations compared to existing global explanation techniques.
Deep learning predicts frame errors in CIRN using SC2 dataset.
problem Predicting frame errors in Collaborative Intelligent Radio Networks (CIRN).
method Deep learning model trained on SC2 dataset with randomized or fixed bandwidth and channel allocation strategies.
result Deep learning model predicts frame error rates and instances with interesting characteristics over different SNR ranges.
Graph neural networks help infer causal effects from partially observable data.
problem Inferring causal effects from partially observable data.
method Theoretical analysis of GNN and SCM connections.
result Established a new model class for GNN-based causal inference.
A new model combines Gaussian processes with collaborative filtering for uncertainty-aware recommendations.
problem Uncertainty in recommendation systems.
method Combining Gaussian process multi-output models with collaborative filtering.
result Generates uncertainty estimates for predictions.
Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study t…
CRN learns causal models using neural networks, scaling with variables and leveraging prior knowledge.
problem Challenges in learning causal models, especially scalability and leveraging prior knowledge.
method Causal Relational Networks (CRN) using continuous representations and previously learned information.
result CRN achieves high accuracy and quick adaptation to new causal models on synthetic data.
CgNN uses network structure as IVs to estimate causal effects in networks.
problem Hidden confounders complicate causal effect estimation in network data.
method CgNN combines GNNs and attention mechanisms to leverage network structure as IVs.
result CgNN effectively mitigates hidden confounder bias and improves causal effect estimation.
Unified approach compares ERGM, GCN, and Word2Vec+MLP for collaboration network link prediction.
problem Link prediction in collaboration networks.
method ERGM, GCN, and Word2Vec+MLP models.
result Machine learning models outperform traditional ERGM in large networks.
OL4EL optimizes edge learning on resource-constrained servers.
problem Resource constraints on edge servers hinder effective distributed machine learning.
method Online Learning for EL (OL4EL) framework using budget-limited multi-armed bandit model.
result OL4EL significantly improves learning performance while conserving resources.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
Causal deep learning tackles causal inference using tensor factor analysis.
problem Addressing causal questions in data using neural networks.
method Tensor factor analysis and neural network architectures (causal capsules, tensor transformer, multilinear projection algorithm).
result Derives deep neural networks for causal inference with tensor factor analysis.
Drone optimizes collaborative learning for neural networks.
problem Training a shared neural network with heterogeneous data from multiple devices.
method Drone collects and aggregates model parameters from devices, optimizing trajectory for best accuracy.
result Significant improvement in final accuracy and speedup in training time.
Interpretable model for Granger causality using neural networks.
problem Inferring Granger causality in complex dynamical systems.
method Extension of self-explaining neural networks for multivariate Granger causality.
result Framework performs on par with baseline methods and better at inferring interaction signs.
A novel federated learning framework resolves structural misalignment in model fusion.
problem Structural misalignment in model fusion due to chaotic information distribution.
method Feature-oriented regulation method (Ψ-Net) to ensure feature information allocation and dedicated collaboration schemes. result Effective enhancement of federated learning applicability to heterogeneous settings with improved convergence speed, accuracy, and efficiency.
New neural network approach for optimizing latent variable models.
problem Stability issues in marginalizing Gaussian Bayesian networks.
method Developed a new graphical structure and a neural network algorithm.
result Established a duality between parameter optimization and neural network training.
While noise is commonly considered a nuisance in computing systems, a number of studies in neuroscience have shown several benefits of noise in the nervous system from enabling the brain to carry out computations such as probabilistic inference as well as carrying additional information about the stimuli. Similarly, no…
Deep Causal Graphs model complex causal relationships using neural networks.
problem Limited applicability of parametric causal models to real-life datasets with non-linear relationships.
method Deep Causal Graphs, an abstract specification for neural networks to model causal distributions.
result Demonstrates expressive power in modelling complex interactions and provides true causal counterfactuals.
A neural network finds causal relationships among latent variables.
problem Learning causal structure among latent variables in high-dimensional data.
method Redundant Input Neural Network (RINN) with modified architecture and regularized objective function.
result The RINN method successfully recovers latent causal structure between input and output variables.
We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social n…
In this paper we present a review of the existing typologies of Internet service users. We zoom in on social networking services including blogs and crowdsourcing websites. Based on the results of the analysis of the considered typologies obtained by means of FCA we developed a new user typology of a certain class of I…
This work proposes a collaborative multi-head attention layer to reduce model size without sacrificing accuracy.
problem Over-parameterization in transformer models trained with large datasets.
method Proposes a collaborative multi-head attention layer that shares key/query projections.
result Reduction in model size by 4 for same accuracy and speed.
New method uses entropy to generate multiple plausible causal maps.
problem Learning causal relationships from noisy data can lead to artifacts in DAGs.
method Entropy-based inference to generate an ensemble of plausible causal graphs.
result Multiple causal maps consistent with underlying data variability.