Q-learning with cSMART data assesses cAI tailoring variables.
problem Evaluating moderators in cAI construction.
method Clustered Q-learning with M-out-of-N Cluster Bootstrap.
result Constructs confidence intervals for causal effect moderation.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “clustered adaptive intervention”
New algorithm reduces interventional strategy complexity for causal graph discovery.
problem Designing efficient interventional strategies for causal graph discovery.
method Developed an r-adaptive algorithm for causal graph discovery that minimizes the number of interventions.
result Achieved an approximation of O(min{r, log n} * n^{1/min{r, log n}}) for the verification number.
New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.
problem Discover causal relationships from data with adaptive interventions and node-dependent costs.
method Define new benchmarks and provide adaptive search algorithms for causal graph discovery.
result Logarithmic approximations achieved under various settings: atomic, bounded size interventions and generalized cost objectives.
Algorithm detects causal change points quickly with adaptive interventions.
problem Detecting changes in causal models with interventions.
method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.
result Theoretical first-order optimality and validation through simulations and real-world studies.
Framework achieves fairness in predictions using partially known causal graph over clusters of variables.
problem Achieving fairness in algorithmic decisions when causal graph knowledge is limited.
method Leverages a causal graph over clusters of variables to train a prediction model, reducing interventional distribution discrepancies.
result Framework strikes a better balance between fairness and accuracy than existing approaches under limited causal graph knowledge.
From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Designcs.LG
Adaptive IP approach optimizes intervention design for causal graph recovery.
problem Designing efficient interventions to recover causal relationships from data.
method Iterative integer programming approach for optimizing information gain.
result Adaptive IP approach achieves full causal graph recovery with fewer interventions.
Paper models treatment effects by clustering patients with distinct survival characteristics.
problem Estimating treatment efficacy in clinical settings with censored outcomes.
method Latent variable approach to model heterogeneous treatment effects.
result The latent structure can mediate base survival rates and reveal actionable phenotypes.
New analysis shows surprising results on adaptation speed of causal models.
problem Investigate the adaptation speed of causal models under interventions.
method Use convergence rates from stochastic optimization to measure adaptation speed.
result Surprising findings: anticausal model can be faster than causal model under certain conditions.
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
Approach to detect and adapt to concept drift in unlabeled streaming data.
problem Detect and adapt to concept drift in high-dimensional, noisy, low-context data.
method Density-based clustering for virtual drift and weak supervision for real drift.
result 90% precision in detecting and adapting to concept drift for 4 years after initial deployment.
Unified RL survey for healthcare AI interventions.
problem Limited real-life application of RL in healthcare.
method Unified technical survey and case studies.
result Bridge between dynamic treatment regimes and mobile health.
We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network on a graph with discrete variables and bounded in-degree and bounded `confounded components', we show that interventions on an unknown causal Bayesian ne…
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
MetaCaDI learns causal graphs and unknown interventions from few data instances.
problem Discovering causal mechanisms in systems with high data costs and unknown interventions.
method MetaCaDI is a Bayesian meta-learning framework that optimizes for rapid adaptation to new intervention targets.
result MetaCaDI significantly outperforms state-of-the-art methods in causal graph recovery and intervention target prediction.
GAMBITTS uses GenAI for adaptive interventions, improving decision-making.
problem Adaptive interventions with GenAI-generated content.
method Generator-mediated bandit-Thompson sampling (GAMBITTS).
result GAMBITTS outperforms standard bandit methods in mobile health interventions.
Adaptive PCR improves panel data analysis with uniform guarantees.
problem Adaptive data collection in panel data settings.
method Adapting PCR to online settings using martingale concentration.
result Time-uniform guarantees for adaptive PCR in panel data.
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
problem Discovering the true causal graph from observational data with limited interventions.
method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.
result Provides approximation algorithms with competitive ratios for verification and search problems.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
problem Improper feature transferability due to spurious correlations in domain adaptation.
method Intervention strategy using unlabeled target data to generate counterfactual features and train discriminability invariance.
result Consistent performance improvements over state-of-the-art approaches in various domain adaptation tasks.
This paper tackles causal interactions in mixtures of DAGs using interventions.
problem Learning causal interactions among variables governed by a mixture of causal systems.
method Establishes necessary and sufficient conditions for intervention size, designs an adaptive algorithm.
result Identifies true edges in a mixture of DAGs using optimal or near-optimal interventions.
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
HiPart offers an efficient, interactive tool for hierarchical clustering.
problem Efficient and interpretable hierarchical clustering for Big Data.
method Divisive hierarchical clustering algorithms with interactive visualizations.
result High computational efficiency and interpretability in Big Data applications.
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in whic…
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
An adaptive clustering algorithm learns from evolving data without manual tuning.
problem Clustering in dynamic data environments where distributions change over time.
method ART-based topological clustering with self-adjusting vigilance parameter.
result The algorithm outperforms state-of-the-art methods in clustering performance and continual learning.
AI improves precision health through adaptive interventions.
problem Improving healthcare through personalized and dynamic treatments.
method Reinforcement learning (RL) for adaptive interventions in digital health.
result RL shows promise in dynamic healthcare problems.
We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previou…
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses real-time data collection and communication capabilities of modern mobile devices to deliver…
Paper shows fairness and domain adaptation can work together.
problem Algorithmic bias and distributional shifts in ML models.
method Leveraging fairness and distribution shifts, the paper shows how domain adaptation methods can mitigate bias.
result Enforcing individual fairness can improve out-of-distribution accuracy under covariate shift.
We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster ad…
Proposes SAG-DBSCAN for clustering with self-adaptation.
problem Clustering analysis in data mining.
method Uses grey relational matrix and DBSCAN for clustering.
result Demonstrates superior performance compared to other methods.
A new experimental design method for combinatorial interventions reduces complexity and improves accuracy.
problem Efficiently conducting all possible combinatorial interventions with multiple treatments and potential interactions.
method Probabilistic factorial experimental design, applying random combinations of treatments and adapting over multiple rounds.
result Optimal dosage of 1/2 for each treatment yields near-optimal design for estimating any k-way interaction model.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
DADC algorithm improves clustering for data with varying density.
problem Sparse cluster loss and cluster fragmentation in density peak clustering.
method Domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble.
result DADC achieves more reasonable clustering results on data with varying density.
Design interventions to learn causal structure from observational data.
problem Identify causal structure from observational data, especially when direction of edges is unknown.
method Design non-adaptive interventions to maximize the number of identifiable edges, formulate as optimization problems.
result Proposed algorithms can efficiently identify causal structure from interventions, even for general causal structures.
Graph-coupled causal Bayesian optimization transfers information across related interventions.
problem Optimizing expensive systems where interventions are costly and causal effects are confounded.
method Ties intervention effects together through shared causal parameters, improving estimation.
result Information-gain and regret bounds show improved performance with shared mechanisms.
A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning anglecs.LG
A new clustering algorithm fuses heat diffusion and turning angle for robustness.
problem Cluster similar elements in various fields.
method Combines heat diffusion and maximal turning angle for robust fission clustering.
result The SARFC algorithm outperforms other methods in clustering performance.
Adaptive graph auto-encoder improves general data clustering.
problem Extending graph convolution networks to general clustering tasks.
method Adaptive graph construction based on generative perspective, novel decoder design.
result Model performs well in weighted graph scenarios.
Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineationstat.ML
Locally adaptive clustering for tree delineation.
problem Tree delineation from distance data.
method Locally adaptive hierarchical cluster termination.
result Multi-scale alternative to conventional termination criteria.
Study efficient algorithms for identifying minimum interventional sets to learn causal relationships.
problem Identify the smallest set of interventions to learn causal relationships between a subset of edges.
method Develop algorithms for subset verification and search problems under assumptions of faithfulness, causal sufficiency, and ideal interventions.
result For subset verification, an efficient algorithm is provided to compute a minimum sized interventional set.
SCTL scales causal domain adaptation without prior knowledge.
problem Domain adaptation with covariate shift and invariances across domains.
method SCTL: scalable causal discovery algorithm based on Markov blanket.
result SCTL achieves scalable and robust domain adaptation.
Paper develops methods for evaluating mHealth interventions using historical data.
problem Evaluating the long-term effectiveness of mHealth interventions designed for near-term outcomes.
method Develops off-policy estimation techniques to infer long-term average outcomes from historical data.
result Provides estimators and confidence intervals for evaluating mHealth policies.
New algorithms verify and search causal graphs with minimal interventions.
problem Recovering causal graphs from interventional data.
method Characterization of minimal intervention sets for verification, and adaptive graph separator algorithm for search.
result First provable algorithms for efficient verification and search of causal graphs.
Optimizes experiment design for causal structure learning in linear models with cycles.
problem Causal structure learning from combined observational and interventional data in linear non-Gaussian cyclic models.
method Combinatorial characterization of equivalence classes, adaptive stochastic optimization, greedy policy with near-optimal performance guarantee, sampling-based estimator for reward function.
result Optimal experiment design reduces the equivalence class of causal graphs to a single true graph with a small number of interventions.
Adapts causal inference for high-dimensional treatments like text strings.
problem Predicting effects of interventions with many possible variations.
method Adapts classical causal estimators to high-dimensional treatment spaces, balancing moment errors.
result Shows high-dimensional treatment spaces can be addressed with a single model.
Adaptive algorithm reduces regret in causal bandits.
problem Minimize regret in causal bandits with unknown d-separators.
method Adaptive algorithm exploiting d-separators without prior knowledge.
result Significantly smaller regret than previous methods.
IMPACC improves consensus clustering for bioinformatics data.
problem Consensus clustering's inefficiency and lack of interpretability for large-scale data.
method Ensemble minipatch co-occurrences, adaptive sampling of observations and features.
result Significantly improved accuracy and interpretability with substantial computational savings.
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced per…
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data. Our approach, Adaptive Nonparametric Variational Autoencoder (AdapVAE), learns the cluster membership through a Bayesian Nonparametric (BN…