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Found 11 papers, 4 papers with code
CLIMAX: An exploration of Classifier-Based Contrastive Explanations
Our method, which we refer to as CLIMAX which is short for Contrastive Label-aware Influence-based Model Agnostic XAI, is based on local classifiers .
Over-The-Air Clustered Wireless Federated Learning
Privacy and bandwidth constraints have led to the use of federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively without sharing raw data.
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks
Causal structures for observational survival data provide crucial information regarding the relationships between covariates and time-to-event.
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability
Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to the lack of trustworthiness.
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach.
CAMTA: Causal Attention Model for Multi-touch Attribution
Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc.
Hi-CI: Deep Causal Inference in High Dimensions
The proposed architecture comprises of a decorrelation network and an outcome prediction network.
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc.
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc.
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework
We demonstrate that the marriage of KD and the VI techniques inherits compression properties from the KD framework, and enhances levels of sparsity from the VI approach, with minimal compromise in the model accuracy.
Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Comparison Graphs
In particular, we employ the Bayesian BTL model which allows for meaningful prior assumptions and to cope with situations where the number of objects is large and the number of comparisons between some objects is small or even zero.
