16,347 research outputs found
Distributed Optimization with Limited Communication in Networks with Adversaries
We all hope for the best but sometimes, one must plan for ways of dealing with the worst-case scenarios, especially in a network with adversaries. This dissertation illustrates a detailed description of distributed optimization algorithms over a network of agents, in which some agents are adversarial. The model considered is such that adversarial agents act to subvert the objective of the network. The algorithms presented in this dissertation are solved via gradient-based distributed optimization algorithm and the effects of the adversarial agents on the convergence of the algorithm to the optimal solution are characterized. The analyses presented establish conditions under which the adversarial agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents. The adversarial agents act by using up network bandwidth, forcing the communication of the non-adversarial agents to be constrained. A distributed gradient-based optimization algorithm is explored in which the non-adversarial agents exchange quantized information with one another using fixed and adaptive quantization scheme. Additionally, convergence of the solution to a neighborhood of the optimal solution is proved in the communication-constrained environment amidst the presence of adversarial agents
A Formalization of Robustness for Deep Neural Networks
Deep neural networks have been shown to lack robustness to small input
perturbations. The process of generating the perturbations that expose the lack
of robustness of neural networks is known as adversarial input generation. This
process depends on the goals and capabilities of the adversary, In this paper,
we propose a unifying formalization of the adversarial input generation process
from a formal methods perspective. We provide a definition of robustness that
is general enough to capture different formulations. The expressiveness of our
formalization is shown by modeling and comparing a variety of adversarial
attack techniques
Cooperative Online Learning: Keeping your Neighbors Updated
We study an asynchronous online learning setting with a network of agents. At
each time step, some of the agents are activated, requested to make a
prediction, and pay the corresponding loss. The loss function is then revealed
to these agents and also to their neighbors in the network. Our results
characterize how much knowing the network structure affects the regret as a
function of the model of agent activations. When activations are stochastic,
the optimal regret (up to constant factors) is shown to be of order
, where is the horizon and is the independence
number of the network. We prove that the upper bound is achieved even when
agents have no information about the network structure. When activations are
adversarial the situation changes dramatically: if agents ignore the network
structure, a lower bound on the regret can be proven, showing that
learning is impossible. However, when agents can choose to ignore some of their
neighbors based on the knowledge of the network structure, we prove a
sublinear regret bound, where is the clique-covering number of the network
Information Structure Design in Team Decision Problems
We consider a problem of information structure design in team decision
problems and team games. We propose simple, scalable greedy algorithms for
adding a set of extra information links to optimize team performance and
resilience to non-cooperative and adversarial agents. We show via a simple
counterexample that the set function mapping additional information links to
team performance is in general not supermodular. Although this implies that the
greedy algorithm is not accompanied by worst-case performance guarantees, we
illustrate through numerical experiments that it can produce effective and
often optimal or near optimal information structure modifications
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Imitation learning has traditionally been applied to learn a single task from
demonstrations thereof. The requirement of structured and isolated
demonstrations limits the scalability of imitation learning approaches as they
are difficult to apply to real-world scenarios, where robots have to be able to
execute a multitude of tasks. In this paper, we propose a multi-modal imitation
learning framework that is able to segment and imitate skills from unlabelled
and unstructured demonstrations by learning skill segmentation and imitation
learning jointly. The extensive simulation results indicate that our method can
efficiently separate the demonstrations into individual skills and learn to
imitate them using a single multi-modal policy. The video of our experiments is
available at http://sites.google.com/view/nips17intentionganComment: Paper accepted to NIPS 201
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