Differentiable Adversarial Attacks for Marked Temporal Point Processes

Authors: Pritish Chakraborty, Vinayak Gupta, Rahul R, Srikanta J. Bedathur, Abir De

AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experiments on four real-world datasets demonstrate the offensive and defensive capabilities, and lower inference times of PERMTPP. 4 Experiments Here, we evaluate PERMTPP by its ability to reduce the performance of learner s MTPP M b w. We present the results in Table 2.
Researcher Affiliation Academia 1Indian Institute of Technology Bombay 2University of Washington Seattle 3Indian Institute of Technology Delhi EMAIL, EMAIL, EMAIL
Pseudocode No The paper describes the method PERMTPP in detail but does not present it in a structured pseudocode or algorithm block.
Open Source Code Yes All codes for PERMTPP are available at: https://github.com/data-iitd/advtpp.
Open Datasets Yes Datasets: We use Taobao (Chen et al. 2019), Twitter (Mei and Eisner 2017), Electricity (Murray, Stankovic, and Stankovic 2017), and Health (Gupta, Bedathur, and De 2022; Baim et al. 1986) datasets.
Dataset Splits Yes Given a dataset of CTESs D = {H}, we split D into 70% training, 10% validation and 20% test set.
Hardware Specification No The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments.
Software Dependencies No The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment.
Experiment Setup No The paper describes the optimization problem with hyperparameters ρD and ρA,B and mentions a temperature parameter τ for the Gumbel-Sinkhorn network, but it does not explicitly provide their specific values or other experimental setup details such as learning rate, batch size, or optimizer settings in the main text.