Mihaela van der Schaar

Overlap-weighted orthogonal meta-learner for treatment effect estimation over time

Operator Theory-Driven Autoformulation of MDPs for Control of Queueing Systems

Eliciting Numerical Predictive Distributions of LLMs Without Auto-Regression

MATHMO: Automated Mathematical Modeling Through Adaptive Search

A Study of Posterior Stability in Time-Series Latent Diffusion

Tables2Traces: Distilling Tabular Data to Improve LLM Reasoning in Healthcare

Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents

Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models

Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport

Learning Cellular Dynamics with Cell–Cell Interaction–Aware Optimal Transport

Learning a Dense Reasoning Reward Model from Expert Demonstration via Inverse Reinforcement Learning

Structure Guided Equation Discovery with Influence-Based Feedback for Large Language Models

Strategic Self-Improvement for Competitive Agents in AI Labour Markets

Building Simulation Environments for Computational Organizational Design

Language Bottleneck Models: A Framework for Qualitative Cognitive Diagnosis

Reasoning on auto-verifiable, scalable, multi-step synthetic tasks

Towards Automated Knowledge Integration From Human-Interpretable Representations

Active Task Disambiguation with LLMs

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference

Treatment Effect Estimation for Optimal Decision-Making

Going Beyond Static: Understanding Shifts with Time-Series Attribution

Preference Learning for AI Alignment: a Causal Perspective

Statistical Hypothesis Testing for Auditing Robustness in Language Models

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs

Timely Clinical Diagnosis through Active Test Selection

Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples

Decision Tree Induction Through LLMs via Semantically-Aware Evolution

Continuously Updating Digital Twins using Large Language Models

AutoCATE: End-to-End, Automated Treatment Effect Estimation

Stochastic Encodings for Active Feature Acquisition

The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data

Generation and Evaluation of Synthetic Data Containing Treatments

Cascaded Language Models for Cost-Effective Human–AI Decision-Making

Information Bottleneck for Active Feature Acquisition

Unified Screening for Multiple Diseases

Strategic Planning: A Top-Down Approach to Option Generation

Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data

Autoformulation of Mathematical Optimization Models Using LLMs

A Study of Posterior Stability for Time-Series Latent Diffusion

Large Language Model Alignment via Inverse Reinforcement Learning from Demonstrations

Matchmaker: Schema Matching with self-improving compositional LLM programs

AutoCATE: End-to-End, Automated Treatment Effect Estimation

Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching

Autoformulation of Mathematical Optimization Models Using LLMs

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration

Enhancing Treatment Effect Estimation with Generation-Driven Data Augmentation

LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements

Retrieval Augmented Thought Process for Private Data Handling in Healthcare

LATABLE: TOWARDS LARGE TABULAR MODELS

Watchmaker Functions and Meta Specification of Open-Ended Learning Systems