We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

We tackle the problem of sampling from intractable high-dimensional density functions, a
fundamental task that often appears in machine learning and statistics. We extend recent …

Active learning (AL) is a specific instance of sequential experimental design and uses
machine learning to intelligently choose the next data point or batch of molecular structures …

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or …

Divergent thinking, the cognitive process of generating diverse solutions, is a hallmark of
human creativity and problem-solving. For machines, sampling diverse solution trajectories …

Current generative models for drug discovery primarily use molecular docking to evaluate
the quality of generated compounds. However, such models are often not useful in practice …

Current generative models for drug discovery primarily use molecular docking as an oracle
to guide the generation of active compounds. However, such models are often not useful in …

Designing biological sequences with desired properties is a significant challenge due to the
combinatorially vast search space and the high cost of evaluating each candidate sequence …

The ability to generate diverse solutions to a given problem is a hallmark of human creativity.
This divergent reasoning is also crucial for machines, enhancing their robustness and …

Without any assumptions about data generation, multiple causal models may explain our
observations equally well. To avoid selecting a single arbitrary model that could result in …