3,976 research outputs found
Fast projections onto mixed-norm balls with applications
Joint sparsity offers powerful structural cues for feature selection,
especially for variables that are expected to demonstrate a "grouped" behavior.
Such behavior is commonly modeled via group-lasso, multitask lasso, and related
methods where feature selection is effected via mixed-norms. Several mixed-norm
based sparse models have received substantial attention, and for some cases
efficient algorithms are also available. Surprisingly, several constrained
sparse models seem to be lacking scalable algorithms. We address this
deficiency by presenting batch and online (stochastic-gradient) optimization
methods, both of which rely on efficient projections onto mixed-norm balls. We
illustrate our methods by applying them to the multitask lasso. We conclude by
mentioning some open problems.Comment: Preprint of paper under revie
Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints
Regularization of ill-posed linear inverse problems via penalization
has been proposed for cases where the solution is known to be (almost) sparse.
One way to obtain the minimizer of such an penalized functional is via
an iterative soft-thresholding algorithm. We propose an alternative
implementation to -constraints, using a gradient method, with
projection on -balls. The corresponding algorithm uses again iterative
soft-thresholding, now with a variable thresholding parameter. We also propose
accelerated versions of this iterative method, using ingredients of the
(linear) steepest descent method. We prove convergence in norm for one of these
projected gradient methods, without and with acceleration.Comment: 24 pages, 5 figures. v2: added reference, some amendments, 27 page
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Robustness of Deep Reinforcement Learning (DRL) algorithms towards
adversarial attacks in real world applications such as those deployed in
cyber-physical systems (CPS) are of increasing concern. Numerous studies have
investigated the mechanisms of attacks on the RL agent's state space.
Nonetheless, attacks on the RL agent's action space (AS) (corresponding to
actuators in engineering systems) are equally perverse; such attacks are
relatively less studied in the ML literature. In this work, we first frame the
problem as an optimization problem of minimizing the cumulative reward of an RL
agent with decoupled constraints as the budget of attack. We propose a
white-box Myopic Action Space (MAS) attack algorithm that distributes the
attacks across the action space dimensions. Next, we reformulate the
optimization problem above with the same objective function, but with a
temporally coupled constraint on the attack budget to take into account the
approximated dynamics of the agent. This leads to the white-box Look-ahead
Action Space (LAS) attack algorithm that distributes the attacks across the
action and temporal dimensions. Our results shows that using the same amount of
resources, the LAS attack deteriorates the agent's performance significantly
more than the MAS attack. This reveals the possibility that with limited
resource, an adversary can utilize the agent's dynamics to malevolently craft
attacks that causes the agent to fail. Additionally, we leverage these attack
strategies as a possible tool to gain insights on the potential vulnerabilities
of DRL agents.Comment: Version 2 with supplementary material
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