202 research outputs found
Leader-following consensus for lower-triangular nonlinear multi-agent systems with unknown controller and measurement sensitivities
summary:In this paper, a novel consensus algorithm is presented to handle with the leader-following consensus problem for lower-triangular nonlinear MASs (multi-agent systems) with unknown controller and measurement sensitivities under a given undirected topology. As distinguished from the existing results, the proposed consensus algorithm can tolerate to a relative wide range of controller and measurement sensitivities. We present some important matrix inequalities, especially a class of matrix inequalities with multiplicative noises. Based on these results and a dual-domination gain method, the output consensus error with unknown measurement noises can be used to construct the compensator for each follower directly. Then, a new distributed output feedback control is designed to enable the MASs to reach consensus in the presence of large controller perturbations. In view of a Lyapunov function, sufficient conditions are presented to guarantee that the states of the leader and followers can achieve consensus asymptotically. In the end, the proposed consensus algorithm is tested and verified by an illustrative example
Automatic Answerability Evaluation for Question Generation
Conventional automatic evaluation metrics, such as BLEU and ROUGE, developed
for natural language generation (NLG) tasks, are based on measuring the n-gram
overlap between the generated and reference text. These simple metrics may be
insufficient for more complex tasks, such as question generation (QG), which
requires generating questions that are answerable by the reference answers.
Developing a more sophisticated automatic evaluation metric, thus, remains as
an urgent problem in QG research. This work proposes a Prompting-based Metric
on ANswerability (PMAN), a novel automatic evaluation metric to assess whether
the generated questions are answerable by the reference answers for the QG
tasks. Extensive experiments demonstrate that its evaluation results are
reliable and align with human evaluations. We further apply our metric to
evaluate the performance of QG models, which shows our metric complements
conventional metrics. Our implementation of a ChatGPT-based QG model achieves
state-of-the-art (SOTA) performance in generating answerable questions
Scaling in Depth: Unlocking Robustness Certification on ImageNet
Despite the promise of Lipschitz-based methods for provably-robust deep
learning with deterministic guarantees, current state-of-the-art results are
limited to feed-forward Convolutional Networks (ConvNets) on low-dimensional
data, such as CIFAR-10. This paper investigates strategies for expanding
certifiably robust training to larger, deeper models. A key challenge in
certifying deep networks is efficient calculation of the Lipschitz bound for
residual blocks found in ResNet and ViT architectures. We show that fast ways
of bounding the Lipschitz constant for conventional ResNets are loose, and show
how to address this by designing a new residual block, leading to the
\emph{Linear ResNet} (LiResNet) architecture. We then introduce \emph{Efficient
Margin MAximization} (EMMA), a loss function that stabilizes robust training by
simultaneously penalizing worst-case adversarial examples from \emph{all}
classes. Together, these contributions yield new \emph{state-of-the-art} robust
accuracy on CIFAR-10/100 and Tiny-ImageNet under perturbations.
Moreover, for the first time, we are able to scale up fast deterministic
robustness guarantees to ImageNet, demonstrating that this approach to robust
learning can be applied to real-world applications.
We release our code on Github: \url{https://github.com/klasleino/gloro}
Convergence Analysis of the Best Response Algorithm for Time-Varying Games
This paper studies a class of strongly monotone games involving
non-cooperative agents that optimize their own time-varying cost functions. We
assume that the agents can observe other agents' historical actions and choose
actions that best respond to other agents' previous actions; we call this a
best response scheme. We start by analyzing the convergence rate of this best
response scheme for standard time-invariant games. Specifically, we provide a
sufficient condition on the strong monotonicity parameter of the time-invariant
games under which the proposed best response algorithm achieves exponential
convergence to the static Nash equilibrium. We further illustrate that this
best response algorithm may oscillate when the proposed sufficient condition
fails to hold, which indicates that this condition is tight. Next, we analyze
this best response algorithm for time-varying games where the cost functions of
each agent change over time. Under similar conditions as for time-invariant
games, we show that the proposed best response algorithm stays asymptotically
close to the evolving equilibrium. We do so by analyzing both the equilibrium
tracking error and the dynamic regret. Numerical experiments on economic market
problems are presented to validate our analysis
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