Publications (20)
Target-Embedding Autoencoders for Supervised Representation Learning
Daniel Jarrett, Mihaela van der Schaar
Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings. This paper analyzes a framework for improving gene…
Language Agents as Digital Representatives in Collective Decision-Making
Daniel Jarrett, Miruna Pîslar, Michiel A. Bakker +6
Consider the process of collective decision-making, in which a group of individuals interactively select a preferred outcome from among a universe of alternatives. In this context,…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Daniel Jarrett, Jinsung Yoon, Ioana Bica +3
Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthc…
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar
Decision analysis deals with modeling and enhancing decision processes. A principal challenge in improving behavior is in obtaining a transparent description of existing behavior i…
Time-series Generation by Contrastive Imitation
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Consider learning a generative model for time-series data. The sequential setting poses a unique challenge: Not only should the generator capture the conditional dynamics of (stepw…
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar
Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare. Though convent…
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar
Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeli…
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often const…
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Hao Sun, Alihan Hüyük, Daniel Jarrett +1
Learning controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. Ho…
Hide-and-Seek Privacy Challenge
James Jordon, Daniel Jarrett, Jinsung Yoon +7
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identifi…
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan Cebere, Tennison Liu +2
Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability o…
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Alex J. Chan, Ioana Bica, Alihan Huyuk +2
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes. Sever…
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
Daniel Jarrett, Corentin Tallec, Florent Altché +3
Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how mu…
Learning "What-if" Explanations for Sequential Decision-Making
Ioana Bica, Daniel Jarrett, Alihan Hüyük +1
Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i.e. trajectories of observations and actions made by an expert maxi…
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
Yao Zhang, Daniel Jarrett, Mihaela van der Schaar
An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may v…
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett, Mihaela van der Schaar
Evidence-based decision-making entails collecting (costly) observations about an underlying phenomenon of interest, and subsequently committing to an (informed) decision on the bas…
Online Decision Mediation
Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an…
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further int…
Invariant Causal Imitation Learning for Generalizable Policies
Ioana Bica, Daniel Jarrett, Mihaela van der Schaar
Consider learning an imitation policy on the basis of demonstrated behavior from multiple environments, with an eye towards deployment in an unseen environment. Since the observabl…