Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (see more)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently
Léa Demeule
Mahtab Sandhu
The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning arc… (see more)hitectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.
Revisiting Data Augmentation for Ultrasound Images
Adam Tupper
Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite o… (see more)ften facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
Frequency enrichment of coding variants in a French-Canadian founder population and its implication for inflammatory bowel diseases
C. Bhérer
Jean-Christophe Grenier
Justin Pelletier
Gabrielle Boucher
Geneviève Gagnon
Philippe Goyette
Dariel Ashton-Beaucage
Christine Stevens
R. Battat
Alain Bitton
Philippe M. Campeau
Catherine Laprise
Quebec Ibd Genetics Consortium iGenoMed Consortium Hailia NIDDK IBD Genetics Consortium
Mark J. Daly
Daniel Taliun
Vincent Mooser
John D. Rioux
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao
Tengyu Xu
Xuewei Wang
Zhengxing Chen
Di Jin
Liang Tan
Yen-Ting Lin
Zishun Yu
Zhuokai Zhao
Si-Yuan Wang
Yun He
Sinong Wang
Han Fang
MetaAI
Chen Zhu
Mila - Québec
AI Institute
Polytechnique Montréal
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one.… (see more) One of the approaches for improving math reasoning is self-correction, which designs self-improving loops to let the model correct its own mistakes. However, existing self-correction approaches treat corrections as standalone post-generation refinements, relying on extra prompt and system designs to elicit self-corrections, instead of performing real-time, spontaneous self-corrections in a single pass. To address this, we propose SPOC, a spontaneous self-correction approach that enables LLMs to generate interleaved solutions and verifications in a single inference pass, with generation dynamically terminated based on verification outcomes, thereby effectively scaling inference time compute. SPOC considers a multi-agent perspective by assigning dual roles -- solution proposer and verifier -- to the same model. We adopt a simple yet effective approach to generate synthetic data for fine-tuning, enabling the model to develop capabilities for self-verification and multi-agent collaboration. We further improve its solution proposal and verification accuracy through online reinforcement learning. Experiments on mathematical reasoning benchmarks show that SPOC significantly improves performance. Notably, SPOC boosts the accuracy of Llama-3.1-8B and 70B Instruct models, achieving gains of 8.8% and 11.6% on MATH500, 10.0% and 20.0% on AMC23, and 3.3% and 6.7% on AIME24, respectively.
Discrete Feynman-Kac Correctors
Mohsin Hasan
Marta Skreta
Alan Aspuru-Guzik
The performance of Large Language Models (LLMs) directly depends on the size of the context that the model was trained on. Despite significa… (see more)nt progress in increasing the context size of the current models, some applications remain bottlenecked by the number of processed tokens at inference time. A particular mathematical problem LLMs can be used for is inferring parameters in a statistical model, given data-points as input. Here we make a case demonstrating that discrete diffusion models offer a promising avenue for scaling such parameter prediction tasks, by combining the outputs of the same model evaluated on different parts of the training data. We propose Discrete Fenyman-Kac Correctors --- a framework that allows for controlling the generated distribution of discrete masked diffusion models at inference time. We derive Sequential Monte Carlo (SMC) algorithms that, given a trained discrete diffusion model, sample from its annealed distribution or the product of distributions with different conditions. Notably, our framework does not require any training, finetuning and external reward functions. Finally, we apply our framework to amortized linear regression using LLaDA and demonstrate that it drastically outperforms the standard inference procedure in terms of accuracy and adherence to prompt format.
Instilling Parallel Reasoning into Language Models
Matthew Macfarlane
Minseon Kim
Nebojsa Jojic
Weijia Xu
Lucas Caccia
Xingdi Yuan
Wanru Zhao
Zhengyan Shi
Sequential chain-of-thought reasoning significantly improves the performance of Large language models (LLMs) on complex tasks. However, sequ… (see more)ential reasoning has structural limitations: Long chains are expensive due to attention's quadratic complexity, and multiple diverse strategies cannot be considered simultaneously. To address this we propose a method that instills parallel reasoning capabilities in LLMs by distilling parallel reasoning traces from a teacher model. This approach enables models to decompose problems, explore diverse strategies via concurrent reasoning traces, and aggregate trace outputs for the final answer. Evaluating on a variety of math and puzzle benchmarks such as MATH 500, AIME and Countdown, we show our approach can decompose parallelizable problems, and that the performance scales with the number of parallel traces. The resulting model can dynamically allocate reasoning strategies based on problem complexity, outperforming standard sampling methods.
Learning to Solve Complex Problems via Dataset Decomposition
Wanru Zhao
Lucas Caccia
Zhengyan Shi
Minseon Kim
Xingdi Yuan
Weijia Xu
Marc-Alexandre Côté
Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler… (see more) to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult tasks. We propose a novel scoring system to measure data difficulty based on its structural complexity and conceptual depth, allowing curriculum construction over decomposed data. Experiments on math datasets (MATH and AIME) demonstrate that models trained with curricula generated by our approach exhibit superior performance compared to standard training on original datasets.
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
Kusha Sareen
Morgane M Moss
Arian Hosseini
Celo: Training Versatile Learned Optimizers on a Compute Diet
Abhinav Moudgil
Boris Knyazev
Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned upda… (see more)te rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after meta-training, is strong meta-generalization: the ability to apply the optimizers to new tasks. Recent state-of-the-art work in learned optimizers, VeLO (Metz et al., 2022), requires a large number of highly diverse meta-training tasks along with massive computational resources, 4000 TPU months, to achieve meta-generalization. This makes further improvements to such learned optimizers impractical. In this work, we identify several key elements in learned optimizer architectures and meta-training procedures that can lead to strong meta-generalization. We also propose evaluation metrics to reliably assess quantitative performance of an optimizer at scale on a set of evaluation tasks. Our proposed approach, Celo, makes a significant leap in improving the meta-generalization performance of learned optimizers and also outperforms tuned state-of-the-art optimizers on a diverse set of out-of-distribution tasks, despite being meta-trained for just 24 GPU hours.
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Maximilian Zenk
Ujjwal Baid
Sarthak Pati
Akis Linardos
Brandon Edwards
Micah Sheller
Patrick Foley
Alejandro Aristizabal
David Zimmerer
Alexey Gruzdev
Jason Martin
Russell T. Shinohara
Annika Reinke
Fabian Isensee
Santhosh Parampottupadam
Kaushal Parekh
Ralf Floca
Hasan Kassem
Bhakti Baheti
Siddhesh Thakur … (see 332 more)
Verena Chung
Kaisar Kushibar
Karim Lekadir
Meirui Jiang
Youtan Yin
Hongzheng Yang
Quande Liu
Cheng Chen
Qi Dou
Pheng-Ann Heng
Xiaofan Zhang
Shaoting Zhang
Muhammad Irfan Khan
Mohammad Ayyaz Azeem
Mojtaba Jafaritadi
Esa Alhoniemi
Elina Kontio
Suleiman A. Khan
Leon Mächler
Ivan Ezhov
Florian Kofler
Suprosanna Shit
Johannes C. Paetzold
Timo Loehr
Benedikt Wiestler
Himashi Peiris
Kamlesh Pawar
Shenjun Zhong
Zhaolin Chen
Munawar Hayat
Gary Egan
Mehrtash Harandi
Ece Isik Polat
Gorkem Polat
Altan Kocyigit
Alptekin Temizel
Anup Tuladhar
Lakshay Tyagi
Raissa Souza
Nils D. Forkert
Pauline Mouches
Matthias Wilms
Vishruth Shambhat
Akansh Maurya
Shubham Subhas Danannavar
Rohit Kalla
Vikas Kumar Anand
Ganapathy Krishnamurthi
Sahil Nalawade
Chandan Ganesh
Ben Wagner
Divya Reddy
Yudhajit Das
Fang F. Yu
Baowei Fei
B. Fei
Ananth J. Madhuranthakam
Joseph Maldjian
Gaurav Singh
Jianxun Ren
Wei Zhang
Ning An
Qingyu Hu
Youjia Zhang
Ying Zhou
Vasilis Siomos
Giacomo Tarroni
Jonathan Passerrat-Palmbach
Ambrish Rawat
Giulio Zizzo
Swanand Ravindra Kadhe
Jonathan P. Epperlein
Stefano Braghin
Yuan Wang
Renuga Kanagavelu
Qingsong Wei
Yechao Yang
Yong Liu
Krzysztof Kotowski
Szymon Adamski
Bartosz Machura
Wojciech Malara
Lukasz Zarudzki
Jakub Nalepa
Yaying Shi
Hongjian Gao
Salman Avestimehr
Yonghong Yan
Agus S. Akbar
Ekaterina Kondrateva
Hua Yang
Zhaopei Li
Hung-Yu Wu
Johannes Roth
Camillo Saueressig
Alexandre Milesi
Quoc D. Nguyen
Nathan J. Gruenhagen
Tsung-Ming Huang
Jun Ma
Har Shwinder H. Singh
Nai-Yu Pan
Dingwen Zhang
Ramy A. Zeineldin
Michal Futrega
Yading Yuan
Gian Marco Conte
GM Conte
Xue Feng
Quan D. Pham
Yong Xia
Zhifan Jiang
Huan Minh Luu
Mariia Dobko
Alexandre Carré
Bair Tuchinov
Hassan Mohy-ud-Din
Saruar Alam
Anup Singh
Nameeta Shah
Weichung Wang
Chiharu Sako
Michel Bilello
Satyam Ghodasara
Suyash Mohan
Christos Davatzikos
Evan Calabrese
Jeffrey Rudie
Javier Villanueva-Meyer
S. Cha
Soonmee Cha
Christopher Hess
John Mongan
Madhura Ingalhalikar
Manali Jadhav
Umang Pandey
Jitender Saini
Raymond Y. Huang
Ken Chang
Minh-Son To
Sargam Bhardwaj
Chee Chong
Marc Agzarian
Michal Kozubek
Filip Lux
Jan Michálek
Petr Matula
Miloš Ker^kovský
Tereza Kopr^ivová
Marek Dostál
Václav Vybíhal
Marco C. Pinho
James Holcomb
Marie Metz
Rajan Jain
Matthew D. Lee
Yvonne W. Lui
Pallavi Tiwari
Ruchika Verma
Rohan Bareja
Ipsa Yadav
Jonathan Chen
Neeraj Kumar
Yuriy Gusev
Krithika Bhuvaneshwar
Anousheh Sayah
Camelia Bencheqroun
Anas Belouali
Subha Madhavan
Rivka R. Colen
Aikaterini Kotrotsou
Philipp Vollmuth
Gianluca Brugnara
Chandrakanth J. Preetha
Felix Sahm
Martin Bendszus
Wolfgang Wick
Abhishek Mahajan
Carmen Balaña
Jaume Capellades
Josep Puig
Yoon Seong Choi
Seung-Koo Lee
Jong Hee Chang
Sung Soo Ahn
Hassan F. Shaykh
Alejandro Herrera-Trujillo
Maria Trujillo
William Escobar
Ana Abello
Jose Bernal
Jhon Gómez
Pamela LaMontagne
Daniel S. Marcus
Mikhail Milchenko
Arash Nazeri
BENNETT A. LANDMAN
Karthik Ramadass
Kaiwen Xu
Silky Chotai
Lola B. Chambless
Akshitkumar Mistry
Reid C. Thompson
Ashok Srinivasan
Jayapalli R. Bapuraj
J. Rajiv Bapuraj
Arvind Rao
Nicholas Wang
Ota Yoshiaki
Toshio Moritani
Sevcan Turk
Joonsang Lee
Snehal Prabhudesai
John Garrett
Matthew Larson
Robert Jeraj
Hongwei Li
H. Li
Tobias Weiss
Michael Weller
Andrea Bink
Bertrand Pouymayou
Sonam Sharma
Tzu-Chi Tseng
Saba Adabi
Alexandre Xavier Falcão
Samuel B. Martins
Bernardo C. A. Teixeira
Flávia Sprenger
David Menotti
Diego R. Lucio
Simone P. Niclou
Olivier Keunen
Ann-Christin Hau
Enrique Pelaez
Heydy Franco-Maldonado
Francis Loayza
Sebastian Quevedo
Richard McKinley
Johannes Slotboom
Piotr Radojewski
Raphael Meier
Roland Wiest
Johannes Trenkler
Josef Pichler
Georg Necker
Andreas Haunschmidt
Stephan Meckel
Pamela Guevara
Esteban Torche
Cristobal Mendoza
Franco Vera
Elvis Ríos
Eduardo López
Sergio A. Velastin
Joseph Choi
Stephen Baek
Yusung Kim
Heba Ismael
Bryan Allen
John M. Buatti
Peter Zampakis
Vasileios Panagiotopoulos
Panagiotis Tsiganos
Sotiris Alexiou
Ilias Haliassos
Evangelia I. Zacharaki
Konstantinos Moustakas
Christina Kalogeropoulou
Dimitrios M. Kardamakis
Bing Luo
Laila M. Poisson
Ning Wen
Mahdi A. L. Loutfi
David Fortin
Martin Lepage
Fanny Morón
Jacob Mandel
Gaurav Shukla
Spencer Liem
Gregory S. Alexandre
Joseph Lombardo
Joshua D. Palmer
Adam E. Flanders
Adam P. Dicker
Godwin Ogbole
Dotun Oyekunle
Olubunmi Odafe-Oyibotha
Babatunde Osobu
Mustapha Shu’aibu Hikima
Mayowa Soneye
Farouk Dako
Adeleye Dorcas
Derrick Murcia
Eric Fu
Rourke Haas
John A. Thompson
David Ryan Ormond
Stuart Currie
Kavi Fatania
Russell Frood
Amber L. Simpson
Jacob J. Peoples
Ricky Hu
Danielle Cutler
Fabio Y. Moraes
Anh Tran
Mohammad Hamghalam
Michael A. Boss
James Gimpel
Deepak Kattil Veettil
Kendall Schmidt
Lisa Cimino
Cynthia Price
Brian Bialecki
Sailaja Marella
Charles Apgar
Andras Jakab
Marc-André Weber
Errol Colak
Jens Kleesiek
John Freymann
Justin Kirby
Lena Maier-Hein
Jake Albrecht
Peter Mattson
Alexandros Karargyris
Prashant Shah
Bjoern Menze
Klaus Maier-Hein
Spyridon Bakas
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test da… (see more)tasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads
Kailu Song
Yumin Zheng
Bowen Zhao
David H. Eidelman