Publications

A Survey of Diversification Techniques in Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Bowei He
Fernando Diaz
Bhaskar Mitra
Xue (Steve) Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.
2024-01-01
IEEE Transactions on Knowledge and Data Engineering (publié)
doi.org
arxiv.org
Survey on Explainable AI: Techniques, challenges and open issues
Adel Abusitta
Miles Q. Li
Benjamin Fung
2024-01-01
Expert Syst. Appl. (publié)
doi.org
Temporal Graph Analysis with TGX
Razieh Shirzadkhani
Shenyang Huang
Elahe Kooshafar
Reihaneh Rabbany
Farimah Poursafaei
Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely d… (voir plus)esigned for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.
2024-01-01
WSDM (publié)
doi.org
arxiv.org
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning.
Tian Jin
Nolan Clement
Xin Dong
Vaishnavh Nagarajan
Michael Carbin
Jonathan Ragan-Kelley
Gintare Karolina Dziugaite
2024-01-01
International Conference on Learning Representations (publié)
openreview.net
openreview.net
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi
Khalil Virji
Farnoosh Faraji
Lucas Berry
Andrew Holliday
David Meger
Gregory Dudek
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (voir plus)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).
2024-01-01
ICRA (publié)
doi.org
arxiv.org
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
Pablo Pernias
Dominic Rampas
Mats Leon Richter
Chris Pal
Marc Aubreville
2024-01-01
International Conference on Learning Representations (publié)
openreview.net
openreview.net
Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs
Margarida Carvalho
Alison Caulfield
Yi Lin
Adrian Vetta
A kidney exchange program, also called a kidney paired donation program, can be viewed as a repeated, dynamic trading and allocation mechani… (voir plus)sm. This suggests that a dynamic algorithm for transplant exchange selection may have superior performance in comparison to the repeated use of a static algorithm. We confirm this hypothesis using a full scale simulation of the Canadian Kidney Paired Donation Program: learning algorithms, that attempt to learn optimal patient-donor weights in advance via dynamic simulations, do lead to improved outcomes. Specifically, our learning algorithms, designed with the objective of fairness (that is, equity in terms of transplant accessibility across cPRA groups), also lead to an increased number of transplants and shorter average waiting times. Indeed, our highest performing learning algorithm improves egalitarian fairness by 10% whilst also increasing the number of transplants by 6% and decreasing waiting times by 24%. However, our main result is much more surprising. We find that the most critical factor in determining the performance of a kidney exchange program is not the judicious assignment of positive weights (rewards) to patient-donor pairs. Rather, the key factor in increasing the number of transplants, decreasing waiting times and improving group fairness is the judicious assignment of a negative weight (penalty) to the small number of non-directed donors in the kidney exchange program.
2023-12-31
Web and Internet Economics (publié)
doi.org
arxiv.org
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper
Xander Davies
Claudia Shi
Thomas Krendl Gilbert
Jérémy Scheurer
Javier Rando
Rachel Freedman
Tomasz Korbak
David Lindner
Pedro Freire
Tony Tong Wang
Samuel Marks
Charbel-Raphael Segerie
Micah Carroll
Andi Peng
Phillip Christoffersen
Mehul Damani
Stewart Slocum
Usman Anwar
Anand Siththaranjan … (voir 12 de plus)
Max Nadeau
Eric J Michaud
Jacob Pfau
Dmitrii Krasheninnikov
Xin Chen
Lauro Langosco
Peter Hase
Erdem Biyik
Anca Dragan
David Scott Krueger
Dorsa Sadigh
Dylan Hadfield-Menell
2023-12-30
TMLR (accepté)
doi.org
openreview.net
Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol
Sathya Karunananthan
Arya Rahgozar
Ramtin Hakimjavadi
Hui Yan
Kunal A Dalsania
Howard Bergman
Bishwajit Ghose
Jim LaPlante
Tess McCutcheon
Daniel I McIsaac
Samira Abbasgholizadeh-Rahimi
Nadia Sourial
Manpreet Thandi
Sabrina T Wong
Clare Liddy
2023-12-28
BMJ Open (publié)
doi.org
Behavioural pseudometrics for continuous-time diffusions
Linan Chen
Florence Clerc
Prakash Panangaden
2023-12-27
ArXiv (prépublication)
doi.org
arxiv.org
Device-Free Human State Estimation using UWB Multi-Static Radios
Saria Al Laham
Bobak H. Baghi
Pierre-Yves Lajoie
Amal Feriani
Sachini Herath
Steve Liu
Gregory Dudek
We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor envir… (voir plus)onment without the requirement that they carry a specific devices with them. To achieve this"device free"localization we use a small number of low-cost Ultra-Wide Band (UWB) sensors distributed across the environment of interest. To achieve high quality estimation from the UWB signals merely reflected of people in the environment, we exploit a deep network that can learn to make inferences. The hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB modules for sensing, paired with Raspberry PI units for computational processing and data transfer. We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area. Additionally, we can also estimate the number of humans that occupy this region of interest. In our approach, first, we pre-process the CIR data which involves meticulous aggregation of measurements and extraction of key statistics. Afterwards, we leverage a convolutional deep neural network to map the CIRs into precise location estimates with sub-30 cm accuracy. Similarly, we achieve accurate human activity recognition and occupancy counting results. We show that we can quickly fine-tune our model for new out-of-distribution users, a process that requires only a few minutes of data and a few epochs of training. Our results show that UWB is a promising solution for adaptable smart-home localization and activity recognition problems.
2023-12-26
ArXiv (prépublication)
doi.org
arxiv.org
Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed
Goncalo Mordido
Samira Shabanian
Ioana Baldini
Sarath Chandar
2023-12-24
ArXiv (prépublication)
doi.org
arxiv.org