Publications

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Andrea Maccio
Yashar Hezaveh
2024-10-30
NeurIPS.cc/2024/Workshop/CRL (poster)
openreview.net
openreview.net
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
Dhanya Sridhar
Gauthier Gidel
Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (voir plus)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.
2024-10-30
NeurIPS.cc/2024/Workshop/CRL (poster)
openreview.net
openreview.net
AugmenToxic: Leveraging Reinforcement Learning to Optimize LLM Instruction Fine-Tuning for Data Augmentation to Enhance Toxicity Detection
Arezo Bodaghi
Benjamin Fung
Ketra A. Schmitt
2024-10-29
ACM Transactions on the Web (publié)
doi.org
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
Shalaleh Rismani
Roel Dobbe
AJung Moon
2024-10-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage
Bertrand Scherrer
Salma Naccache
Christophe B'elanger
Antoine Lesage-Landry
2024-10-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Sum and Tensor of Quantitative Effects
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
2024-10-29
Logical Methods in Computer Science (publié)
doi.org
arxiv.org
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Mercy Nyamewaa Asiedu
Awa Dieng
Iskandar Haykel
Negar Rostamzadeh
Stephen R. Pfohl
Chirag Nagpal
Maria Nagawa
Abigail Oppong
Sanmi Koyejo
Katherine Heller
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand … (voir plus)and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.
2024-10-29
Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (publié)
doi.org
arxiv.org
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
Dhanya Sridhar
Gauthier Gidel
2024-10-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation
Michael Przystupa
Gauthier Gidel
Matthew E. Taylor
Martin Jägersand
Justus Piater
Samuele Tosatto
As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay peop… (voir plus)le be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability.
2024-10-28
ArXiv (prépublication)
arxiv.org
arxiv.org
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Rambod Azimi
Rishav Rishav
Marek Teichmann
Samira Ebrahimi Kahou
2024-10-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Xi Zhang
Yuan Pu
Yuki Kawamura
Andrew Loza
Yoshua Bengio
Dennis Shung
Alexander Tong
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (voir plus)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.
2024-10-28
ArXiv (prépublication)
arxiv.org
arxiv.org
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
Dmytro Humeniuk
Houssem Ben Braiek
Thomas Reid
Foutse Khomh
2024-10-27
Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (publié)
doi.org
arxiv.org