Publications

GEANT4-DNA simulation of temperature-dependent and pH-dependent yields of chemical radiolytic species
Jingyi Bian
Juan Duran
Wook-Geun Shin
Jose Ramos-Méndez
Jack C Sankey
Lilian Childress
Jan Seuntjens
Shirin A. Enger
2023-06-15
Physics in Medicine & Biology (published)
doi.org
A solution algorithm for chance-constrained problems with integer second-stage recourse decisions
Andrea Lodi
Enrico Malaguti
Michele Monaci
Giacomo Nannicini
Paolo
Paronuzzi
2023-06-15
Mathematical programming (published)
doi.org
A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning
Adel Nabli
Eugene Belilovsky
Edouard Oyallon
2023-06-14
ArXiv (preprint)
doi.org
arxiv.org
Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision
Fernando Diaz
Across a variety of ranking tasks, researchers use reciprocal rank to measure the effectiveness for users interested in exactly one relevant… (see more) item. Despite its widespread use, evidence suggests that reciprocal rank is brittle when discriminating between systems. This brittleness, in turn, is compounded in modern evaluation settings where current, high-precision systems may be difficult to distinguish. We address the lack of sensitivity of reciprocal rank by introducing and connecting it to the concept of best-case retrieval, an evaluation method focusing on assessing the quality of a ranking for the most satisfied possible user across possible recall requirements. This perspective allows us to generalize reciprocal rank and define a new preference-based evaluation we call lexicographic precision or lexiprecision. By mathematical construction, we ensure that lexiprecision preserves differences detected by reciprocal rank, while empirically improving sensitivity and robustness across a broad set of retrieval and recommendation tasks.
2023-06-13
ArXiv (preprint)
doi.org
arxiv.org
Who Controlled the Evidence? Question Answering for Disclosure Information Extraction
Hardy Hardy
Derek Ruths
Nicholas B King
Conflict of interest (COI) disclosure statements provide rich information to support trans-parency and reduce bias in research. We introduce… (see more) a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.
2023-06-13
Proceedings of the Conference on Health, Inference, and Learning (published)
proceedings.mlr.press
Benchmarking Neural Network Training Algorithms
George Edward Dahl
Frank Schneider
Zachary Nado
Naman Agarwal
Chandramouli Shama Sastry
Philipp Hennig
Sourabh Medapati
Runa Eschenhagen
Priya Kasimbeg
Daniel Suo
Juhan Bae
Justin M. Gilmer
A. L. Peirson
Bilal Muhammad Khan
Rohan Anil
Michael Rabbat
Shankar Krishnan
Daniel Snider
Ehsan Amid
Kongtao Chen … (see 5 more)
Chris J. Maddison
R. Vasudev
Michal Badura
Ankush Garg
Peter Mattson
2023-06-12
ArXiv (preprint)
doi.org
arxiv.org
Harms from Increasingly Agentic Algorithmic Systems
Alan Chan
Rebecca Salganik
Alva Markelius
Chris Pang
Nitarshan Rajkumar
Dmitrii Krasheninnikov
Lauro Langosco
Zhonghao He
Yawen Duan
Micah Carroll
Michelle Lin
Alex Mayhew
Katherine Collins
Maryam Molamohammadi
John Burden
Wanru Zhao
Shalaleh Rismani
Konstantinos Voudouris
Umang Bhatt
Adrian Weller … (see 2 more)
David Scott Krueger
Tegan Maharaj
Research in Fairness, Accountability, Transparency, and Ethics (FATE)1 has established many sources and forms of algorithmic harm, in domain… (see more)s as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed, typically without strong regulatory barriers, threatening the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms, rather than just responding to them. Anticipation of harms is especially important given the rapid pace of developments in machine learning (ML). Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency – notably, these include systemic and/or long-range impacts, often on marginalized or unconsidered stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
2023-06-12
2023 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Tiago Salvador
Kilian FATRAS
Ioannis Mitliagkas
Adam M. Oberman
Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In the case of an extreme l… (see more)abel shift scenario between the source and target domains, where we have extra source classes not present in the target domain, the UDA problem becomes a harder problem called Partial Domain Adaptation (PDA). While different methods have been developed to solve the PDA problem, most successful algorithms use model selection strategies that rely on target labels to find the best hyper-parameters and/or models along training. These strategies violate the main assumption in PDA: only unlabeled target domain samples are available. In addition, there are also experimental inconsistencies between developed methods - different architectures, hyper-parameter tuning, number of runs - yielding unfair comparisons. The main goal of this work is to provide a realistic evaluation of PDA methods under different model selection strategies and a consistent evaluation protocol. We evaluate 6 state-of-the-art PDA algorithms on 2 different real-world datasets using 7 different model selection strategies. Our two main findings are: (i) without target labels for model selection, the accuracy of the methods decreases up to 30 percentage points; (ii) only one method and model selection pair performs well on both datasets. Experiments were performed with our PyTorch framework, BenchmarkPDA, which we open source.
2023-06-11
TMLR (accepted)
doi.org
openreview.net
Conditions for indexability of restless bandits and an algorithm to compute whittle index – CORRIGENDUM
Nima Akbarzadeh
Aditya Mahajan
2023-06-09
Advances in Applied Probability (published)
doi.org
Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions
Veronika Pak
Quadri Adewale
Danilo Bzdok
Mahsa Dadar
Yashar Zeighami
Yasser Iturria-Medina
For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuro… (see more)nal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types’ contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in thirteen neurodegenerative conditions, including early-and late-onset Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and three clinical variants of frontotemporal lobar degeneration (behavioural variant, semantic and non-fluent primary progressive aphasia) along with associated 3-repeat and 4-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.
2023-06-09
bioRxiv (preprint)
doi.org
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani
Erick Delage
Angelos Georghiou
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information t… (see more)o provide more anticipative decisions. The wide range of methods and contexts of application have motivated the design of a universal unitless measure of performance known as the coefficient of prescriptiveness. This coefficient was designed to quantify both the quality of contextual decisions compared to a reference one and the prescriptive power of side information. To identify policies that maximize the former in a data-driven context, this paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk minimization objective. We present a bisection algorithm to solve this model, which relies on solving a series of linear programs when the distributional ambiguity set has an appropriate nested form and polyhedral structure. Studying a contextual shortest path problem, we evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift.
2023-06-09
ArXiv (preprint)
doi.org
arxiv.org
Value function estimation using conditional diffusion models for control
Bogdan Mazoure
Walter Talbott
Miguel Ángel Bautista
(Rex) Devon Hjelm
Alexander T Toshev
Joshua M. Susskind
2023-06-09
ArXiv (preprint)
doi.org
openreview.net