38 research outputs found
Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations
A control design approach is developed for a general class of uncertain
strict-feedback-like nonlinear systems with dynamic uncertain input
nonlinearities with time delays. The system structure considered in this paper
includes a nominal uncertain strict-feedback-like subsystem, the input signal
to which is generated by an uncertain nonlinear input unmodeled dynamics that
is driven by the entire system state (including unmeasured state variables) and
is also allowed to depend on time delayed versions of the system state variable
and control input signals. The system also includes additive uncertain
nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
input nonlinearities with time-varying uncertain time delays. The proposed
control design approach provides a globally stabilizing delay-independent
robust adaptive output-feedback dynamic controller based on a dual dynamic
high-gain scaling based structure.Comment: 2017 IEEE International Carpathian Control Conference (ICCC
High-Dimensional Controller Tuning through Latent Representations
In this paper, we propose a method to automatically and efficiently tune
high-dimensional vectors of controller parameters. The proposed method first
learns a mapping from the high-dimensional controller parameter space to a
lower dimensional space using a machine learning-based algorithm. This mapping
is then utilized in an actor-critic framework using Bayesian optimization (BO).
The proposed approach is applicable to complex systems (such as quadruped
robots). In addition, the proposed approach also enables efficient
generalization to different control tasks while also reducing the number of
evaluations required while tuning the controller parameters. We evaluate our
method on a legged locomotion application. We show the efficacy of the
algorithm in tuning the high-dimensional controller parameters and also
reducing the number of evaluations required for the tuning. Moreover, it is
shown that the method is successful in generalizing to new tasks and is also
transferable to other robot dynamics
Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection
This paper proposes a data-efficient detection method for deep neural
networks against backdoor attacks under a black-box scenario. The proposed
approach is motivated by the intuition that features corresponding to triggers
have a higher influence in determining the backdoored network output than any
other benign features. To quantitatively measure the effects of triggers and
benign features on determining the backdoored network output, we introduce five
metrics. To calculate the five-metric values for a given input, we first
generate several synthetic samples by injecting the input's partial contents
into clean validation samples. Then, the five metrics are computed by using the
output labels of the corresponding synthetic samples. One contribution of this
work is the use of a tiny clean validation dataset. Having the computed five
metrics, five novelty detectors are trained from the validation dataset. A meta
novelty detector fuses the output of the five trained novelty detectors to
generate a meta confidence score. During online testing, our method determines
if online samples are poisoned or not via assessing their meta confidence
scores output by the meta novelty detector. We show the efficacy of our
methodology through a broad range of backdoor attacks, including ablation
studies and comparison to existing approaches. Our methodology is promising
since the proposed five metrics quantify the inherent differences between clean
and poisoned samples. Additionally, our detection method can be incrementally
improved by appending more metrics that may be proposed to address future
advanced attacks.Comment: Published in the IEEE Transactions on Information Forensics and
Securit
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs
We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal
portfolio mean-variance preferences in the setting of multivariate generalized
autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on
trading. A numerical solution is obtained using a neural network (NN)
architecture within a recursive RL loop. A fixed-point theorem proves that NN
approximation error has a big-oh bound that we can reduce by increasing the
number of NN parameters. The functional form of the trading penalty has a
parameter that controls the magnitude of transaction costs. When
is small, we can implement an NN algorithm based on the expansion of
the solution in powers of . This expansion has a base term equal to a
myopic solution with an explicit form, and a first-order correction term that
we compute in the RL loop. Our expansion-based algorithm is stable, allows for
fast computation, and outputs a solution that shows positive testing
performance
Inventory strategies for patented and generic products for a pharmaceutical supply chain
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 76-77).This thesis presents a model to determine safety stock considering the distinct planning parameters for a pharmaceutical company. Traditional parameters such as forecast accuracy, service level requirements and average lead-time are combined with a nontraditional upstream uncertainty parameter defined as supply reliability. In this instance, supply reliability measures uncertainty in the supply quantity delivered rather than variability in the lead-time for delivery. We consider the impact of the safety stock using two products: a proprietary product that is patented and a generic product that recently went off patent. Sensitivity analysis is performed to provide insights on the impact of variations in input parameters. The study shows that there is a significant difference in safety stock between the proposed model and the current model used by the company.by Prashanth Krishnamurthy and Amit Prasad.M.Eng.in Logistic
Privacy-Preserving Collaborative Learning through Feature Extraction
We propose a framework in which multiple entities collaborate to build a
machine learning model while preserving privacy of their data. The approach
utilizes feature embeddings from shared/per-entity feature extractors
transforming data into a feature space for cooperation between entities. We
propose two specific methods and compare them with a baseline method. In Shared
Feature Extractor (SFE) Learning, the entities use a shared feature extractor
to compute feature embeddings of samples. In Locally Trained Feature Extractor
(LTFE) Learning, each entity uses a separate feature extractor and models are
trained using concatenated features from all entities. As a baseline, in
Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train
models by sharing raw data. Secure multi-party algorithms are utilized to train
models without revealing data or features in plain text. We investigate the
trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage
(using an off-the-shelf membership inference attack), and computational cost.
LTFE provides the most privacy, followed by SFE, and then CTFE. Computational
cost is lowest for SFE and the relative speed of CTFE and LTFE depends on
network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a
synthetic dataset, and a credit card fraud detection dataset for evaluations
LipSim: A Provably Robust Perceptual Similarity Metric
Recent years have seen growing interest in developing and applying perceptual
similarity metrics. Research has shown the superiority of perceptual metrics
over pixel-wise metrics in aligning with human perception and serving as a
proxy for the human visual system. On the other hand, as perceptual metrics
rely on neural networks, there is a growing concern regarding their resilience,
given the established vulnerability of neural networks to adversarial attacks.
It is indeed logical to infer that perceptual metrics may inherit both the
strengths and shortcomings of neural networks. In this work, we demonstrate the
vulnerability of state-of-the-art perceptual similarity metrics based on an
ensemble of ViT-based feature extractors to adversarial attacks. We then
propose a framework to train a robust perceptual similarity metric called
LipSim (Lipschitz Similarity Metric) with provable guarantees. By leveraging
1-Lipschitz neural networks as the backbone, LipSim provides guarded areas
around each data point and certificates for all perturbations within an
ball. Finally, a comprehensive set of experiments shows the
performance of LipSim in terms of natural and certified scores and on the image
retrieval application. The code is available at
https://github.com/SaraGhazanfari/LipSim