Search Results for author:
Found 15 papers, 7 papers with code
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search
To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference
In this paper, we consider the problem of estimating the effects of crowd movement guidance from past data.
Inter-domain Multi-relational Link Prediction
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities.
Multiview Representation Learning from Crowdsourced Triplet Comparisons
Crowdsourcing has been used to collect data at scale in numerous fields.
Few-shot learning of neural networks from scratch by pseudo example optimization
In this paper, we propose a simple but effective method for training neural networks with a limited amount of training data.
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
This is because coordinate descent iteratively updates all the parameters in the objective until convergence.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
Crowdsourcing Evaluation of Saliency-based XAI Methods
In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Evaluation of intervention in a multiagent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
Mitigating Observation Biases in Crowdsourced Label Aggregation
Crowdsourcing has been widely used to efficiently obtain labeled datasets for supervised learning from large numbers of human resources at low cost.
Mitigating Voter Attribute Bias for Fair Opinion Aggregation
The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data.
Estimating Treatment Effects Under Heterogeneous Interference
One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).
Causal Effect Estimation on Hierarchical Spatial Graph Data
To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome.
