497 research outputs found
Power linear keller maps of rank two are linearly triangularizable
AbstractLet R be a field with characteristic zero. In this paper it is proved that power linear Keller maps Rn→Rn with rank at most two are linearly triangularizable
DCC categories of cohomological dimension one
AbstractA characterization of dcc categories of cohomological dimension one is given from which a characterization of dcc posets of cohomological dimension one is deduced
Provable Reset-free Reinforcement Learning by No-Regret Reduction
Reinforcement learning (RL) so far has limited real-world applications. One
key challenge is that typical RL algorithms heavily rely on a reset mechanism
to sample proper initial states; these reset mechanisms, in practice, are
expensive to implement due to the need for human intervention or heavily
engineered environments. To make learning more practical, we propose a generic
no-regret reduction to systematically design reset-free RL algorithms. Our
reduction turns the reset-free RL problem into a two-player game. We show that
achieving sublinear regret in this two-player game would imply learning a
policy that has both sublinear performance regret and sublinear total number of
resets in the original RL problem. This means that the agent eventually learns
to perform optimally and avoid resets. To demonstrate the effectiveness of this
reduction, we design an instantiation for linear Markov decision processes,
which is the first provably correct reset-free RL algorithm.Comment: Full version of the paper accepted to ICML 2023. Also spotlighted in
AAAI 2023 RL4PROD Worksho
Orthogonally Decoupled Variational Gaussian Processes
Gaussian processes (GPs) provide a powerful non-parametric framework for
reasoning over functions. Despite appealing theory, its superlinear
computational and memory complexities have presented a long-standing challenge.
State-of-the-art sparse variational inference methods trade modeling accuracy
against complexity. However, the complexities of these methods still scale
superlinearly in the number of basis functions, implying that that sparse GP
methods are able to learn from large datasets only when a small model is used.
Recently, a decoupled approach was proposed that removes the unnecessary
coupling between the complexities of modeling the mean and the covariance
functions of a GP. It achieves a linear complexity in the number of mean
parameters, so an expressive posterior mean function can be modeled. While
promising, this approach suffers from optimization difficulties due to
ill-conditioning and non-convexity. In this work, we propose an alternative
decoupled parametrization. It adopts an orthogonal basis in the mean function
to model the residues that cannot be learned by the standard coupled approach.
Therefore, our method extends, rather than replaces, the coupled approach to
achieve strictly better performance. This construction admits a straightforward
natural gradient update rule, so the structure of the information manifold that
is lost during decoupling can be leveraged to speed up learning. Empirically,
our algorithm demonstrates significantly faster convergence in multiple
experiments.Comment: Appearing NIPS 201
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