GitHub - kangmintong/COLEP: [ICLR 2024] Codes for COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits

Implementation of COLEP [ICLR 2024]

The repository contains Python codes to implement COLEP. Please use sh run.sh to run scripts for different tasks.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
RSCP		RSCP
arc		arc
archs		archs
dataset		dataset
knowledge_probabilistic_circuit		knowledge_probabilistic_circuit
model		model
models_cifar		models_cifar
randomized_smoothing		randomized_smoothing
used_knowledge		used_knowledge
AwA2_single_NN_conformal.py		AwA2_single_NN_conformal.py
AwA2_single_model_certify.py		AwA2_single_model_certify.py
AwA_conformal_knowledge_PC.py		AwA_conformal_knowledge_PC.py
README.md		README.md
conformal_attack.py		conformal_attack.py
conformal_baseline.py		conformal_baseline.py
conformal_baseline_certified_robustness.py		conformal_baseline_certified_robustness.py
conformal_knowledge.py		conformal_knowledge.py
conformal_knowledge_pc copy.py		conformal_knowledge_pc copy.py
conformal_knowledge_pc.py		conformal_knowledge_pc.py
conformal_knowledge_pc_certified_robustness.py		conformal_knowledge_pc_certified_robustness.py
conformal_knowledge_pc_worst_cov.py		conformal_knowledge_pc_worst_cov.py
main.py		main.py
model_train_cifar.py		model_train_cifar.py
model_train_cifar_sensors.py		model_train_cifar_sensors.py
run.sh		run.sh
test.py		test.py
test_AwA.py		test_AwA.py
train_PC.py		train_PC.py
train_utils_AwA2.py		train_utils_AwA2.py
utils.py		utils.py
worst_case_coverage.py		worst_case_coverage.py