Warut D. Vijitbenjaronk

TL;DR: This work attempts to fill gaps in existing fairness literature that have primarily focused on discovering and/or algorithmically enforcing fairness constraints on decision making systems by proposing a recourse algorithm that models the underlying data distribution or manifold.

TL;DR: An implementation of OLTP-oriented graph database that supports time-versioning, using a B-tree as the datastructure for the backend storage, to allow fast and scalable support for restoring the arbitrary part of the graph, without slowing down the normal accesses to the current graph.

TL;DR: This paper analyzes the learning-theoretic properties of pAp@k, particularly its benefts in evaluating modern recommender systems, and proposes novel surrogates that are consistent under certain data regularity conditions and provides gradient descent based algorithms to optimize the surrogates directly.

TL;DR: A computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs) revealed that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs.

TL;DR: A novel distributed graph database called System G designed for efficient graph data storage and processing on modern computing architectures is discussed and the efficiency of System G for storing data and processing graph queries on state-of-the-art platforms is experimentally shown.