A neural network method optimizes power control in multi-user channels.
problem Maximizing sum rate in multi-user interference channels.
method PCNet, a multi-layer neural network, directly maximizes sum rate; ePCNet is an ensemble of multiple PCNets.
result ePCNet outperforms existing power control methods by 1.2%-4.6%.
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised …
Improved multi-user VoiceFilter-Lite model for speech recognition.
problem Limited performance of multi-user VoiceFilter-Lite models.
method Dual learning rate schedule and FiLM for feature conditioning.
result Closed the performance gap between multi-user and single-user VoiceFilter-Lite models.
Algorithm optimizes spectrum access for dynamic multi-user environments.
problem Optimizing spectrum access in uncoordinated multi-user environments with potential collisions.
method Stochastic multi-user bandit framework with estimation and allocation phases.
result Order-optimal system-wide regret of O(logT) for dynamic and static cases. New policy reduces spectrum access regret in uncoordinated systems.
problem Uncoordinated spectrum access with user-dependent rewards.
method Multi-user multi-armed bandit (MAB) model with user-specific rewards.
result Achieves O(logT) regret for spectrum access. Unified framework for multi-user bandits using Laplacian kernels.
problem Multi-user contextual bandits with graph-related users and non-linear rewards.
method Joint penalty combining graph smoothness and individual roughness in a unified RKHS.
result Unified multi-user RKHS and effective dimension for regret bounds.
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
New model for personalized online advertising with multi-user interaction.
problem Realistic online advertising scenarios with multiple users interacting simultaneously.
method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.
result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.
New algorithms for uncoordinated spectrum access with multi-user multi-armed bandits.
problem Uncoordinated spectrum access with unknown number of users and channels.
method Developed algorithms for stochastic and adversarial settings, combining Exp3.P for dynamic scenarios.
result Sub-linear regret guarantees for both stochastic and adversarial cases, even when users outnumber channels.
This paper tackles resource allocation in multi-user communication networks using a coordinated multi-armed bandit approach.
problem Learning unknown stochastic network characteristics and sharing resources efficiently.
method Combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme.
result Ensures convergence to a stable allocation of resources with maximal resource utilization.
This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
New measure of interference helps understand and mitigate learning issues in reinforcement learning.
problem Understanding and mitigating interference in reinforcement learning.
method Defined a new measure of interference, evaluated it, and identified key factors contributing to interference.
result Target network frequency and updates on the last layer are significant factors in interference.
A DL autoencoder tackles interference channels, improving SNR and INR.
problem Improving performance in interference channels with varying interference levels.
method Designing a DL neural network autoencoder for a k-user Gaussian interference channel, classifying interferences as weak to very strong.
result DL autoencoder significantly mitigates interference effects, especially with known α and low offset.
New method removes interference bias in causal models.
problem Interference bias impedes causal effect identification in real-world settings.
method Novel definition of causal models with local interference, semi-parametric assumptions.
result True Average Causal Effect can be identified in certain semi-parametric models with local interference.
Study finds reinforcement learning performance plateaus due to environmental interference.
problem Catastrophic interference hinders sample efficiency in reinforcement learning.
method Empirical study in ALE, controlled experiments, analysis of prediction errors.
result Interference causes performance plateaus and degrades policies used to reach them.
Estimates causal effects in networks with varying interference.
problem Estimating causal effects in settings with network interference.
method Proposes neighborhood adaptive estimators for average direct treatment effect on the treated.
result Establishes rates of convergence and distributional results for proposed estimators.
TD learning reduces interference, leading to better generalization.
problem Understanding and reducing interference in TD learning for better generalization.
method Analyzing the inner product of gradients as interference, comparing TD and supervised learning, and examining the dynamics of interference and bootstrapping.
result TD learning leads to low-interference, under-generalizing parameters, while supervised learning does the opposite.
The paper addresses Qini curve estimation under clustered network interference.
problem Qini curves can be biased when interference is ignored in clustered network settings.
method Proposes three estimation strategies for clustered network interference.
result Identifies the most appropriate approach based on bias-variance trade-offs.
Paper predicts interference for better LA in URLLC.
problem Improving LA for URLLC with strict latency and reliability.
method Exploits time correlation of interference for prediction.
result Predicted interference improves LA for URLLC.
This work addresses causal inference challenges in networked interference and proposes GNN-based estimators for individual treatment effects.
problem Estimating individual treatment effects in randomized experiments with networked interference.
method Uses Graph Neural Networks (GNNs) to capture network dependencies and derive causal effect estimators.
result Provides policy regret bounds and heuristic error bounds for GNN-based causal estimators under network interference and treatment capacity constraints.
DN estimator mitigates network interference in experiments.
problem Network interference biases naive experiment designs.
method Differences-in-Neighbors (DN) estimator designed to mitigate interference.
result DN achieves bias second order in interference effect, with exponentially smaller variance.
Recursive filtering predicts wireless interference levels accurately.
problem Predicting interference in wireless networks.
method Designing a recursive predictor using Kalman filtering and ARMA model.
result Good accuracy of predicted interference values compared to true values.
Study efficient inference for network quantile causal effects with partial interference.
problem Estimating network causal effects on outcome quantiles with partial interference.
method Developed a nonparametric efficiency theory and a nonparametrically efficient estimator using a three-way cross-fitting procedure.
result Proposed estimator is consistent, asymptotically normal, and allows flexible estimation of nuisance functions.
Optimizes data transmission timing to minimize user information age.
problem Minimizing long-term average age of information in multi-user networks.
method Reinforcement learning applied to scheduling decisions without channel state information.
result RL approach effectively reduces AoI compared to traditional ARQ and HARQ.
Combines two graph models to handle interference effects in Gaussian distributions.
problem Handling interference effects in causal models for Gaussian distributions.
method Integrates Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs.
result Proposes a new class of causal models that can represent interference and non-interference relationships.
Spatial Deconfounder tackles interference and confounding in spatial data.
problem Interference and unmeasured spatial factors confound causal inference in spatial domains.
method Two-stage method using CVAE with spatial prior to reconstruct confounder, then estimate causal effects.
result Nonparametric identification of direct and spillover effects under weak assumptions.
Algorithm learns interference network and optimizes treatment allocation for unknown network effects.
problem Adaptive experimentation under unknown network interference.
method Thompson sampling algorithm with Gibbs sampler for joint learning of interference network and treatment allocation.
result Proves a Bayesian regret bound and achieves sublinear regret in real-world applications.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
Machine learning method characterizes network interference in A/B tests.
problem Compromised A/B test reliability due to network interference.
method Causal network motifs and machine learning models.
result Outperforms conventional methods in characterizing network interference.
FRONT optimizes decisions with interference, reducing regret over time.
problem Short-sighted policies in online decision-making due to ignoring interference.
method FRONT considers long-term impacts of decisions, using exploratory and exploitative strategies.
result FRONT achieves sublinear regret in both immediate and consequential impacts.
The paper tackles adaptive targeting in networks with interference effects.
problem Adaptive targeting under network interference in a bandit setting.
method Linear model in a sparse regime, analyzing different levels of knowledge of the interference structure.
result Unified view of how knowledge of the interference structure affects online learning efficiency.
Machine learning reduces wind tunnel testing costs for tall buildings.
problem Limited wind tunnel tests fail to fully reveal interference effects of tall buildings.
method Used machine learning techniques, including GANs, to predict pressure coefficients.
result GANs model based on 30% of dataset accurately predicts pressure coefficients under unseen conditions.
New algorithm optimizes interventions under network interference, scaling to large networks.
problem Optimal policy learning under network interference where one individual's treatment affects others.
method Developed a scalable Thompson sampling algorithm for dynamic networks.
result Proved a Bayesian regret bound that is sublinear in network size and rounds.
A new method reduces task interference in model merging.
problem Model merging overlooks structural information and is susceptible to task interference.
method Task Singular Vectors (TSV) and TSV-Compress for compression and interference reduction.
result TSV-Merge significantly outperforms existing methods.
New algorithm minimizes regret in multi-armed bandits with network interference.
problem Minimizing regret in online experiments with network interference.
method Sparse network interference model, discrete Fourier analysis, linear regression-based algorithms.
result Provable low regret algorithms for sparse interference networks.
New method improves causal inference by estimating complex treatment effects with active learning.
problem Traditional causal inference frameworks ignore interference and assume independent treatment effects.
method Active Learning in Causal Inference with Interference (ACI) using Gaussian process and genetic algorithms.
result ACI achieves accurate effects estimation with reduced data requirements in complex interference scenarios.
New framework for estimating treatment effects in experiments with network interference.
problem Network interference biases traditional treatment effect estimations in randomized experiments.
method Causal message-passing framework based on high-dimensional approximate message passing.
result Practical algorithm to estimate total treatment effect in multi-period experiments.
New estimator reduces bias in interference studies on content marketplaces.
problem Interference bias in experiments on content marketplaces like Douyin.
method Developed a Monte-Carlo estimator based on DQ techniques.
result Achieved bias second-order in treatment effect with low variance.
New method HNCI for evaluating treatment effects in network interference.
problem Evaluating the effectiveness of treatments or policies under network interference.
method High-dimensional network causal inference (HNCI) using linear regression with latent homogeneity.
result Valid confidence intervals and sets for average direct treatment effect and neighborhood size.
New model tackles interference in online experiments.
problem Understanding cumulative performance in interference experiments.
method Introduces Multi-Armed Bandits with Interference (MABI) model.
result Cluster randomization policy achieves optimal expected regret and high probability bound.
Paper investigates robustness to interference as a new training signal for meta-learning.
problem Improving incremental learning through robust representations.
method Directly minimizing catastrophic interference as a training signal.
result Representations learned to minimize interference lead to better incremental learning.
Method learns gestures from touch devices, outperforming state of the art.
problem Automatic gesture recognition on touch devices with multi-user variability.
method Dynamic sampling, convolutional model, recurrent vs. convolutional features.
result Outperforms state of the art on MMG dataset.
Estimates treatment effects in bipartite systems with partial eligibility and interference.
problem Randomized experiments in bipartite systems with partial treatment eligibility and interference.
method Formalizes eligibility-constrained bipartite experiments, defines PTTE and STTE, identifies conditions, develops ensemble estimators, introduces projection.
result Proposed estimators recover PTTE and STTE with low bias and variance, corrects interference bias in field experiments.
New method estimates treatment effects in complex interference settings.
problem Challenges in estimating treatment effects due to unknown interference.
method Higher-order causal message passing for non-linear feature learning.
result Effective estimation of treatment effect dynamics in complex interference.
Paper uses SC to estimate hidden interference for WSRM.
problem Maximizing sum-rate with hidden interfering sources.
method Synthetic control (SC) for estimating counterfactual interference in WMMSE.
result SC-WMMSE outperforms original WMMSE in convergence and objective.
The paper proposes an evolution-based approach to estimate causal effects in interference networks without fully observing the network structure.
problem Estimating causal effects in complex systems with unobserved interaction pathways.
method An evolution-based approach that characterizes how outcomes change across observation rounds in response to interventions, using an exposure-mapping perspective.
result The approach identifies minimal structural conditions under which evolution mappings exist, enabling consistent learning about heterogeneous spillover effects.
U-Net trained to recover acoustic interference striations from distorted data.
problem Recovering acoustic interference striations from distorted signals.
method Training a U-Net using a random mode-coupling matrix model to generate training data.
result U-Net successfully recovers AISs under various conditions.
New framework minimizes interference and selection bias in network A/B testing.
problem Interference and selection bias in network A/B testing.
method Proposes a principled framework that jointly minimizes interference and selection bias using edge spillover probability and cluster matching.
result Significantly lower error in causal effect estimation compared to existing solutions.