Publications

QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations
Suyuchen Wang
Ruihui Zhao
Yefeng Zheng
Bang Liu
Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a … (see more)taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1
2022-04-25
The Web Conference (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Leon Qi Rong Ooi
Christopher L. Asplund
Scott Marek
Nico Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Christopher L Asplund
Scott A. Marek
N. Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (published)
doi.org
Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar Anbil Parthipan
We empirically show that classic ideas from two-time scale stochastic approximation \citep{borkar1997stochastic} can be combined with sequen… (see more)tial iterative best response (SIBR) to solve complex cooperative multi-agent reinforcement learning (MARL) problems. We first start with giving a multi-agent estimation problem as a motivating example where SIBR converges while parallel iterative best response (PIBR) does not. Then we present a general implementation of staged multi-agent RL algorithms based on SIBR and multi-time scale stochastic approximation, and show that our new methods which we call Staged Independent Proximal Policy Optimization (SIPPO) and Staged Independent Q-learning (SIQL) outperform state-of-the-art independent learning on almost all the tasks in the epymarl \citep{papoudakis2020benchmarking} benchmark. This can be seen as a first step towards more decentralized MARL methods based on SIBR and multi-time scale learning.
2022-04-25
ICLR.cc/2022/Workshop/GMS (published)
openreview.net
openreview.net
VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
Pratheeksha Nair
Yifei Li
Catalina Vajiac
Andreas Olligschlaeger
Meng-Chieh Lee
Namyong Park
Duen Horng Chau
Christos Faloutsos
Reihaneh Rabbany
Chieh Lee
Human trafficking analysts investigate groups of related online escort advertisements (called micro-clusters) to detect suspicious activitie… (see more)s and identify various modus operandi. This task is complex as it requires finding patterns and linked meta-data across micro-clusters such as the geographical spread of ads, cluster sizes, etc. Additionally, drawing insights from the data is challenging without visualizing these micro-clusters. To address this, in close-collaboration with domain experts, we built VisPaD, a novel interactive way for characterizing and visualizing micro-clusters and their associated meta-data, all in one place. VisPaD helps discover underlying patterns in the data by projecting micro-clusters in a lower dimensional space. It also allows the user to select micro-clusters involved in suspicious patterns and interactively examine them leading to faster detection and identification of trends in the data. A demo of VisPaD is also released1.
2022-04-25
The Web Conference (published)
doi.org
VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
Pratheeksha Nair
Yifei Li
Catalina Vajiac
Andreas Olligschlaeger
Meng-Chieh Lee
Namyong Park
Duen Horng Chau
Christos Faloutsos
Reihaneh Rabbany
Chieh Lee
2022-04-25
The Web Conference (published)
doi.org
Local Learning with Neuron Groups
Adeetya Patel
Michael Eickenberg
Eugene Belilovsky
2022-04-21
ICLR.cc/2022/Workshop/Cells2Societies (poster)
doi.org
openreview.net
Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning
Amin Memarian
Maximilian Puelma Touzel
Matthew D Riemer
Rupali Bhati
Irina Rish
Agents cannot make sense of many-agent societies through direct consideration of small-scale, low-level agent identities, but instead must r… (see more)ecognize emergent collective identities. Here, we take a first step towards a framework for recognizing this structure in large groups of low-level agents so that they can be modeled as a much smaller number of high-level agents—a process that we call agent abstraction. We illustrate this process by extending bisimulation metrics for state abstraction in reinforcement learning to the setting of multi-agent reinforcement learning and analyze a straightforward, if crude, abstraction based on experienced joint actions. It addresses non-stationarity due to other learning agents by improving minimax regret by a intuitive factor. To test if this compression factor provides signal for higher-level agency, we applied it to a large dataset of human play of the popular social dilemma game Diplomacy. We find that it correlates strongly with the degree of ground-truth abstraction of low-level units into the human players.
2022-04-21
ICLR.cc/2022/Workshop/Cells2Societies (poster)
openreview.net
openreview.net
A Strong Node Classification Baseline for Temporal Graphs
Farimah Poursafaei
Željko Žilić
Reihaneh Rabbany
2022-04-20
Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (published)
doi.org
Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
Lee Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
Paule-Joanne Toussaint
Ali R. Khan
Gustav Nilsonne
Filippo Maria Castelli
Stefan Ross Eric Franklin Anthony Rémi Christopher J. Taylor Appelhoff
Julien Cohen-Adad
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (see more)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
2022-04-19
Frontiers in Neuroscience (published)
doi.org
Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
L. Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
P. Toussaint
Ali Raza Khan
Gustav Nilsonne
Filippo Maria Castelli
Julien Cohen-Adad
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (see more)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
2022-04-19
Frontiers in Neuroscience (published)
doi.org
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Nouha Dziri
Sivan Milton
Mo Yu
Osmar R Zaiane
Siva Reddy
Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallu… (see more)cination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models? We conduct a comprehensive human study on both existing knowledge-grounded conversational benchmarks and several state-of-the-art models. Our study reveals that the standard benchmarks consist of > 60% hallucinated responses, leading to models that not only hallucinate but even amplify hallucinations. Our findings raise important questions on the quality of existing datasets and models trained using them. We make our annotations publicly available for future research.
2022-04-17
ArXiv (preprint)
doi.org
arxiv.org