Publications

Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
Prakhar Ganesh
Afaf Taïk
Golnoosh Farnadi
Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbit… (voir plus)rariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., those likely to be deployed in practice. In this work, we systemize the literature on multiplicity by: (a) formalizing the terminology around model design choices and their contribution to arbitrariness, (b) expanding the definition of multiplicity to incorporate underrepresented forms beyond just predictions and explanations, (c) clarifying the distinction between multiplicity and other lenses of arbitrariness, i.e., uncertainty and variance, and (d) distilling the benefits and potential risks of multiplicity into overarching trends, situating it within the broader landscape of responsible AI. We conclude by identifying open research questions and highlighting emerging trends in this young but rapidly growing area of research.
2025-10-14
AAAI/ACM Conference on AI Ethics and Society (publié)
doi.org
arxiv.org
The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates
Dhvani Doshi
Nicolas B. Cowan
E. Artigau
René Doyon
André M. Silva
Khaled Al Moulla
Yashar Hezaveh
Precise radial velocity (pRV) measurements of M-dwarfs in the near-infrared (NIR) rely on empirical templates due to the lack of accurate st… (voir plus)ellar spectral models in this regime. Templates are assumed to approximate the true spectrum when constructed from many observations or in the high signal-to-noise limit. We develop a numerical simulation that generates SPIRou-like pRV observations from PHOENIX spectra, constructs empirical templates, and estimates radial velocities. This simulation solely considers photon noise and evaluates when empirical templates remain reliable for pRV analysis. Our results reveal a previously unrecognized noise source in templates, establishing a fundamental floor for template-based pRV measurements. We find that templates inherently include distortions in stellar line shapes due to imperfect interpolation at the detector's sampling resolution. The magnitude of this interpolation error depends on sampling resolution and RV content. Consequently, while stars with a higher RV content, such as cooler M-dwarfs are expected to yield lower RV uncertainties, their dense spectral features can amplify interpolation errors, potentially biasing RV estimates. For a typical M4V star, SPIRou's spectral and sampling resolution imposes an RV uncertainty floor of 0.5-0.8 m/s, independent of the star's magnitude or the telescope's aperture. These findings reveal a limitation of template-based pRV methods, underscoring the need for improved spectral modeling and better-than-Nyquist detector sampling to reach the next level of RV precision.
2025-10-14
The Astronomical Journal (publié)
doi.org
arxiv.org
Joint Satellite Power Consumption and Handover Optimization for LEO Constellations
Yassine Afif
Mohammed Almekhlafi
Antoine Lesage-Landry
Gunes Karabulut Kurt
In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology indu… (voir plus)ced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.
2025-10-13
International Conference on Wireless for Space and Extreme Environments (publié)
doi.org
arxiv.org
Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
Adam Tupper
Christian Gagné
2025-10-13
ArXiv (prépublication)
doi.org
arxiv.org
RNAGenScape: Property-guided Optimization and Interpolation of mRNA Sequences with Manifold Langevin Dynamics
Danqi Liao
Chen Liu
Xingzhi Sun
Di'e Tang
Haochen Wang
Scott E. Youlten
Srikar Krishna Gopinath
Haejeong Lee
Ethan C. Strayer
Antonio J. Giraldez
Smita Krishnaswamy
2025-10-13
ArXiv (prépublication)
doi.org
arxiv.org
Sensory multi-brain stimulation enhances dyadic cooperative behavior
Ivo Leiva-Cisterna
Paulo Barraza
Eugenio Rodríguez
Guillaume Dumas
Hyperscanning research suggests that interbrain synchronization supports the regulation of social behavior. However, the evidence is predomi… (voir plus)nantly correlational, leaving a gap for epiphenomenal accounts, where synchrony merely represents concurrent stimulus processing rather than a mechanism relevant to interpersonal interactions. Here, we demonstrate that interbrain synchrony causally drives cooperative success, as evidenced by non-invasive stimulation enhancing coupling and subsequently improving performance in a concurrent interdependent cooperation task. We applied dual-sensory entrainment at 16 Hz and 40 Hz to dyads and compared their performance with non-entrained control dyads performing the same cooperation task. We found that dual stimulation improved interbrain synchrony at the targeted frequencies relative to controls, with 16 Hz entrainment producing the most prominent effect. Strikingly, sensory entrainment facilitated sustained behavioral coupling, allowing partners to maintain coordination over extended periods. Notably, these effects are contingent on improved response coordination, indicating the importance of interbrain coupling for facilitating coordination and demonstrating causally that partner neural attunement is necessary to produce effective joint behavior. Thus, our study supports the concept that interbrain synchrony represents a neural mechanism with functional specificity in social interactions.
2025-10-13
Social Cognitive and Affective Neuroscience (publié)
doi.org
TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
Ziyang Song
Qincheng Lu
Hao Xu
He Zhu
David L. Buckeridge
Yue Li
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing and Computer … (voir plus)Vision domains. However, the development of PTMs on healthcare time-series data is lagging behind.This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale time series and ability to capture long-term temporal dependencies. In this study, we present Timely Generative Pre-trained Transformer (TimelyGPT). TimelyGPT employs an extrapolatable position (xPos) embedding to encode trend and periodic patterns into time-series representations. It also integrates recurrent attention and temporal convolution modules to effectively capture global-local temporal dependencies. We evaluated TimelyGPT on two large-scale healthcare time series datasets corresponding to continuous biosignals and irregularly-sampled time series, respectively. Our experiments show that during pre-training, TimelyGPT excels in learning time-series representations from continuously monitored biosignals and irregularly-sampled time series data commonly observed in longitudinal electronic health records (EHRs). In forecasting continuous biosignals, TimelyGPT achieves accurate extrapolation up to 6,000 timesteps of body temperature during the sleep stage transition, given a short look-up window (i.e., prompt) containing only 2,000 timesteps. For irregularly-sampled time series, TimelyGPT with a proposed time-specific inference demonstrates high top recall scores in predicting future diagnoses using early diagnostic records, effectively handling irregular intervals between clinical records. Together, we envision TimelyGPT to be useful in a broad spectrum of health domains, including long-term patient health state forecasting and patient risk trajectory prediction.
2025-10-13
Health Information Science and Systems (publié)
doi.org
arxiv.org
Disease-Specific Prediction of Missense Variant Pathogenicity with DNA Language Models and Graph Neural Networks
Mohamed Ghadie
Sameer Sardaar
Yannis Trakadis
2025-10-12
Bioengineering (publié)
doi.org
Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers
Sarthak Mittal
Divyat Mahajan
Guillaume Lajoie
Mohammad Pezeshki
2025-10-12
ArXiv (prépublication)
doi.org
arxiv.org
Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance
Mohamed Mohamed
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however the impressive pe… (voir plus)rformance of these models in 2D is largely enabled by extensive, readily available pretrained foundation models. Critically, comparable pretrained foundation models do not exist for 3D, significantly limiting progress in this domain. As a result, the potential of vision-language models to produce high-resolution 3D counterfactual medical images conditioned solely on natural language descriptions remains completely unexplored. Addressing this gap would enable powerful clinical and research applications, such as personalized counterfactual explanations, simulation of disease progression scenarios, and enhanced medical training by visualizing hypothetical medical conditions in realistic detail. Our work takes a meaningful step toward addressing this challenge by introducing a framework capable of generating high-resolution 3D counterfactual medical images of synthesized patients guided by free-form language prompts. We adapt state-of-the-art 3D diffusion models with enhancements from Simple Diffusion and incorporate augmented conditioning to improve text alignment and image quality. To our knowledge, this represents the first demonstration of a language-guided native-3D diffusion model applied specifically to neurological imaging data, where faithful three-dimensional modeling is essential to represent the brain's three-dimensional structure. Through results on two distinct neurological MRI datasets, our framework successfully simulates varying counterfactual lesion loads in Multiple Sclerosis (MS), and cognitive states in Alzheimer's disease, generating high-quality images while preserving subject fidelity in synthetically generated medical images. Our results lay the groundwork for prompt-driven disease progression analysis within 3D medical imaging.
2025-10-11
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Zahra Tehrani Nasab
Amar Kumar
Tal Arbel
Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resoluti… (voir plus)on settings. As high-resolution image synthesis becomes increasingly essential for various applications, particularly in medical imaging domains, fine-tuning emerges as a crucial mechanism for adapting these powerful pre-trained models to task-specific requirements and data distributions. In this work, we present a systematic study, examining the impact of various fine-tuning techniques on image generation quality when scaling to high resolution 512x512 pixels. We benchmark a diverse set of fine-tuning methods, including full fine-tuning strategies and parameter-efficient fine-tuning (PEFT). We dissect how different fine-tuning methods influence key quality metrics, including Fr\'echet Inception Distance (FID), Vendi score, and prompt-image alignment. We also evaluate the utility of generated images in a downstream classification task under data-scarce conditions, demonstrating that specific fine-tuning strategies improve both generation fidelity and downstream performance when synthetic images are used for classifier training and evaluation on real images. Our code is accessible through the project website - https://tehraninasab.github.io/PixelUPressure/.
2025-10-11
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
CTR-LoRA: Curvature-Aware and Trust-Region Guided Low-Rank Adaptation for Large Language Models
Zhuxuanzi Wang
Mingqiao Mo
Xi Xiao
Chen Liu
Chenrui Ma
Yunbei Zhang
Xiao Wang
Smita Krishnaswamy
Tianyang Wang
Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory … (voir plus)budgets. Although previous methods improve efficiency through low-rank updates, quantization, or heuristic budget reallocation, they often decouple the allocation of capacity from the way updates evolve during training. In this work, we introduce CTR-LoRA, a framework guided by curvature trust region that integrates rank scheduling with stability-aware optimization. CTR-LoRA allocates parameters based on marginal utility derived from lightweight second-order proxies and constrains updates using a Fisher/Hessian-metric trust region. Experiments on multiple open-source backbones (7B-13B), evaluated on both in-distribution and out-of-distribution benchmarks, show consistent improvements over strong PEFT baselines. In addition to increased accuracy, CTR-LoRA enhances training stability, reduces memory requirements, and achieves higher throughput, positioning it on the Pareto frontier of performance and efficiency. These results highlight a principled path toward more robust and deployable PEFT.
2025-10-10
ArXiv (prépublication)
doi.org
arxiv.org