Probably Approximately Correct (PAC) learning has attempted to
analyse the generalisation of learning systems within the
statistical learning framework. It has been referred to as a ‘worst
case’ analysis, but the tools have been extended to analyse...
In handling wide range of experiences ranging from data
instances, knowledge, constraints, to rewards, adversaries, and
lifelong interplay in an ever-growing spectrum of tasks,
contemporary ML/AI research has resulted in thousands of
models...
Unsupervised learning, in particular learning general nonlinear
representations, is one of the deepest problems in machine
learning. Estimating latent quantities in a generative model
provides a principled framework, and has been successfully
used...
For autonomous robots to operate in the open, dynamically
changing world, they will need to be able to learn a robust set of
skills from relatively little experience. This talk begins by
introducing Grounded Simulation Learning as a way to bridge...
I will introduce Generalized Energy Based Models (GEBM) for
generative modelling. These models combine two trained components:
a base distribution (generally an implicit model), which can learn
the support of data with low intrinsic dimension in a...
Some of the aspects of the world around us are captured in
natural language and refer to semantic high-level variables, which
often have a causal role (referring to agents, objects, and actions
or intentions). These high-level variables also seem to...
In this talk I will introduce our next generation of graph
neural networks. GNNs have the property that they are invariant to
permutations of the nodes in the graph and to rotations of the
graph as a whole. We claim this is unnecessarily restrictive...
We consider sequential prediction with expert advice when the
data are generated stochastically, but the distributions generating
the data may vary arbitrarily among some constraint set. We
quantify relaxations of the classical I.I.D. assumption in...
Competitive optimization is needed for many ML problems such as
training GANs, robust reinforcement learning, and adversarial
learning. Standard approaches to competitive optimization involve
each agent independently optimizing their objective...
Data-driven design is making headway into a number of
application areas, including protein, small-molecule, and materials
engineering. The design goal is to construct an object with desired
properties, such as a protein that binds to a target more...
