This paper studies the effect of exploiting temporal dependency of successive weight updates on compressing communications in Federated Learning (FL). For this, we propose residual coding for FL, which utilizes temporal dependencies by communicating …
We present a human-in-the-loop approach to generate counterfactual (CF) explanations that preserve global and local feasibility constraints. Global feasibility constraints refer to the causal constraints that are necessary for generating actionable …
A stochastic binary-ternary (SBT) quantization approach is introduced for communication efficient federated computation; form of collaborative computing where locally trained models are exchanged between institutes. Communication of deep neural …
Deep neural networks have huge number of parameters and require large number of bits for representation. This hinders their adoption in decentralized environments where model transfer among different parties is a characteristic of the environment …
In this work, we propose an interpretability utility, which explicates the trade-off between explanation fidelity and interpretability in the Bayesian framework.
In this work, we propose an interpretability utility, which explicates the trade-off between explanation fidelity and interpretability in the Bayesian framework.
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is laborious, as the emphasis has been on accuracy and not on efficiency of the process. Another line of work queries about importance of features one at a time, assuming them to be independent and hence missing covariance information. In contrast, we propose eliciting expert knowledge about pairwise feature similarities, to borrow statistical strength in the predictions, and using sequential decision making techniques to minimize the effort of the expert.
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. In many real-world applications, samples may not deterministically be assigned to a single class; they come naturally with their associated uncertainties. Also, samples may not be equally importantand their importance degrees affect the classification. Despite its efficiency, LST-SVM still lacks the ability to deal with these situations. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to cope with these difficulties.
Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design.
Two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters.