6 citations · 19 across the 9 of their papers we have counts for
11 papers
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
Mohit Kumar, Bernhard A. Moser, Lukas Fischer
Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach t…
Membership-Mappings for Practical Secure Distributed Deep Learning
Mohit Kumar, Weiping Zhang, Lukas Fischer +1
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The im…
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Mohit Kumar, Samuel Kolb, Stefano Teso +1
Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor…
Learning Mixed-Integer Linear Programs from Contextual Examples
Mohit Kumar, Samuel Kolb, Luc De Raedt +1
Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research to model complex decision problems like scheduling and routing. Designing s…
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
Mohit Kumar, Bernhard A. Moser, Lukas Fischer +1
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI f…
Differentially Private Transferrable Deep Learning with Membership-Mappings
Mohit Kumar
This paper considers the problem of differentially private semi-supervised transfer and multi-task learning. The notion of \emph{membership-mapping} has been developed using measur…