10 citations · 49 across the 18 of their papers we have counts for
12 papers · 1 filter
Resolution-Aware Retrieval Augmented Zero-Shot Forecasting
Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods.…
Challenges in Understanding Modality Conflict in Vision-Language Models
Trang Nguyen, Jackson Michaels, Madalina Fiterau +1
This paper highlights the challenge of decomposing conflict detection from conflict resolution in Vision-Language Models (VLMs) and presents potential approaches, including using a…
Leveraging Foundation Language Models (FLMs) for Automated Cohort Extraction from Large EHR Databases
Purity Mugambi, Alexandra Meliou, Madalina Fiterau
A crucial step in cohort studies is to extract the required cohort from one or more study datasets. This step is time-consuming, especially when a researcher is presented with a da…
A/B testing under Interference with Partial Network Information
Shiv Shankar, Ritwik Sinha, Yash Chandak +2
A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control t…
Zero-shot Microclimate Prediction with Deep Learning
Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions oft…
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
Iman Deznabi, Madalina Fiterau
The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard d…
Progressive Fusion for Multimodal Integration
Shiv Shankar, Laure Thompson, Madalina Fiterau
Integration of multimodal information from various sources has been shown to boost the performance of machine learning models and thus has received increased attention in recent ye…
Structure Mapping for Transferability of Causal Models
Purva Pruthi, Javier González, Xiaoyu Lu +1
Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using obj…
Personalized Student Stress Prediction with Deep Multitask Network
Abhinav Shaw, Natcha Simsiri, Iman Deznaby +2
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress…
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)
Bhanu Pratap Singh, Iman Deznabi, Bharath Narasimhan +4
Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are…
FLARe: Forecasting by Learning Anticipated Representations
Surya Teja Devarakonda, Joie Yeahuay Wu, Yi Ren Fung +1
Computational models that forecast the progression of Alzheimer's disease at the patient level are extremely useful tools for identifying high risk cohorts for early intervention a…
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…