Search Results for author:
Found 5 papers, 0 papers with code
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
Our research demonstrates that to achieve $\epsilon$-optimal policies for all $M$ tasks, a single agent using DistMT-LSVI needs to run a total number of episodes that is at most $\tilde{\mathcal{O}}({d^3H^6(\epsilon^{-2}+c_{\rm sep}^{-2})}\cdot M/N)$, where $c_{\rm sep}>0$ is a constant representing task separability, $H$ is the horizon of each episode, and $d$ is the feature dimension of the dynamics and rewards.
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering
To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering.
Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases
Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs). Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0. 80 and 0. 62 respectively.
WiseR: An end-to-end structure learning and deployment framework for causal graphical models
Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data.
