Cooperative adaptive cruise control in mixed traffic with selective use of vehicle‐to‐vehicle communication

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166217/1/itr2bf00554.pd

Minimax Optimality In High-Dimensional Classification, Clustering, And Privacy

The age of “Big Data” features large volume of massive and high-dimensional datasets, leading to fast emergence of different algorithms, as well as new concerns such as privacy and fairness. To compare different algorithms with (without) these new constraints, minimax decision theory provides a principled framework to quantify the optimality of algorithms and investigate the fundamental difficulty of statistical problems. Under the framework of minimax theory, this thesis aims to address the following four problems: 1. The first part of this thesis aims to develop an optimality theory for linear discriminant analysis in the high-dimensional setting. In addition, we consider classification with incomplete data under the missing completely at random (MCR) model. 2. In the second part, we study high-dimensional sparse Quadratic Discriminant Analysis (QDA) and aim to establish the optimal convergence rates. 3. In the third part, we study the optimality of high-dimensional clustering on the unsupervised setting under the Gaussian mixtures model. We propose a EM-based procedure with the optimal rate of convergence for the excess mis-clustering error. 4. In the fourth part, we investigate the minimax optimality under the privacy constraint for mean estimation and linear regression models, under both the classical low-dimensional and modern high-dimensional settings

CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201

Consensus and disturbance attenuation in multi‐agent chains with nonlinear control and time delays

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135960/1/rnc3600_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135960/2/rnc3600.pd

Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise

The existence of spurious correlations such as image backgrounds in the training environment can make empirical risk minimization (ERM) perform badly in the test environment. To address this problem, Kirichenko et al. (2022) empirically found that the core features that are related to the outcome can still be learned well even with the presence of spurious correlations. This opens a promising strategy to first train a feature learner rather than a classifier, and then perform linear probing (last layer retraining) in the test environment. However, a theoretical understanding of when and why this approach works is lacking. In this paper, we find that core features are only learned well when their associated non-realizable noise is smaller than that of spurious features, which is not necessarily true in practice. We provide both theories and experiments to support this finding and to illustrate the importance of non-realizable noise. Moreover, we propose an algorithm called Freeze then Train (FTT), that first freezes certain salient features and then trains the rest of the features using ERM. We theoretically show that FTT preserves features that are more beneficial to test time probing. Across two commonly used spurious correlation datasets, FTT outperforms ERM, IRM, JTT and CVaR-DRO, with substantial improvement in accuracy (by 4.5%) when the feature noise is large. FTT also performs better on general distribution shift benchmarks