Reinforcement Learning and Control
Model-based Reinforcement Learning and Planning
Object-centric Self-supervised Reinforcement Learning
Self-exploration of Behavior
Causal Reasoning in RL
Equation Learner for Extrapolation and Control
Intrinsically Motivated Hierarchical Learner
Regularity as Intrinsic Reward for Free Play
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation
Natural and Robust Walking from Generic Rewards
Goal-conditioned Offline Planning
Offline Diversity Under Imitation Constraints
Learning Diverse Skills for Local Navigation
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Combinatorial Optimization as a Layer / Blackbox Differentiation
Object-centric Self-supervised Reinforcement Learning
Symbolic Regression and Equation Learning
Representation Learning
Stepsize adaptation for stochastic optimization
Probabilistic Neural Networks
Learning with 3D rotations: A hitchhiker’s guide to SO(3)
Probabilistic Methods for Linear Algebra
Linear algebra methods form the basis for the majority of numerical computations. Because of this foundational, ``inner-loop'' role, they have to satisfy strict requirements on computational efficiency and numerical robustness.
Our work has added to a growing understanding that many widely used linear solvers can be interpreted as performing probabilistic inference on the elements of a matrix or a vector from observations linear projections of this latent object. In particular, this is true for such foundational algorithms as the method of conjugate gradients and other iterative algorithms in the conjugate directions and projection method classes [
].
Our ongoing research effort focusses on ways to use these insights in the large-scale linear algebra problems encountered in machine learning. There, the most frequent linear algebra task is the least-squares problem of solving
$$ Kx = b $$
where $K$ is a very large symmetric positive definite matrix (e.g. a kernel Gram matrix, or the Hessian of a deep network loss function). A key challenge in the big data regime is that the matrix --- defined as a function of a large data-set --- can only be evaluated with strong stochastic noise caused by data sub-sampling. Classic iterative solvers, particularly those based on the Lanczos process, like conjugate gradients, are known to be unstable to such stochastic disturbances, which is part of the reason why second-order methods are not popular in deep learning. In recent work we have developed and tested extensions to classic solvers that remain stable [] and tractable in this setting by efficiently re-using information across many interations [].
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