2 citations · 3 across the 11 of their papers we have counts for
11 papers
LLMs can construct powerful representations and streamline sample-efficient supervised learning
Ilker Demirel, Lawrence Shi, Zeshan Hussain +1
As real-world datasets become more complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data, such as time-serie…
Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition
Ilker Demirel, Karan Thakkar, Benjamin Elizalde +7
Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show…
Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis +1
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity…
Prediction-Powered Causal Inferences
Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis +4
In many scientific experiments, the data annotating cost constraints the pace for testing novel hypotheses. Yet, modern machine learning pipelines offer a promising solution, provi…
Prediction-powered Generalization of Causal Inferences
Ilker Demirel, Ahmed Alaa, Anthony Philippakis +1
Causal inferences from a randomized controlled trial (RCT) may not pertain to a target population where some effect modifiers have a different distribution. Prior work studies gene…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan Hussain +3
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experim…
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
Zeshan Hussain, Ming-Chieh Shih, Michael Oberst +2
Randomized Controlled Trials (RCT)s are relied upon to assess new treatments, but suffer from limited power to guide personalized treatment decisions. On the other hand, observatio…
Federated Multi-Armed Bandits Under Byzantine Attacks
Artun Saday, İlker Demirel, Yiğit Yıldırım +1
Multi-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward.…
Safe Linear Leveling Bandits
Ilker Demirel, Mehmet Ufuk Ozdemir, Cem Tekin
Multi-armed bandits (MAB) are extensively studied in various settings where the objective is to \textit{maximize} the actions' outcomes (i.e., rewards) over time. Since safety is c…
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin
Finding an optimal individualized treatment regimen is considered one of the most challenging precision medicine problems. Various patient characteristics influence the response to…