Fast and Robust Rank Aggregation against Model Misspecification

In rank aggregation, preferences from different users are summarized into a total order under the homogeneous data assumption. Thus, model misspecification arises and rank aggregation methods take some noise models into account. However, they all rely on certain noise model assumptions and cannot handle agnostic noises in the real world. In this paper, we propose CoarsenRank, which rectifies the underlying data distribution directly and aligns it to the homogeneous data assumption without involving any noise model. To this end, we define a neighborhood of the data distribution over which Bayesian inference of CoarsenRank is performed, and therefore the resultant posterior enjoys robustness against model misspecification. Further, we derive a tractable closed-form solution for CoarsenRank making it computationally efficient. Experiments on real-world datasets show that CoarsenRank is fast and robust, achieving consistent improvement over baseline methods

Robust Rank Aggregation and Its Applications

University of Technology Sydney. Faculty of Engineering and Information Technology.Rank aggregation (RA) refers to the task of recovering the total order over a set of items, given a collection of preferences over the items. The flexible collection of preferences enables successful application of RA in various fields, e.g., image rating and bioinformatics. A basic assumption underlying the vanilla RA is that all preferences are provided by homogeneous users. However, this assumption is rarely satisfied in real applications, due to the complex real situation. Therefore, RA usually suffers from model misspecification, namely the inconsistency between the collected preferences and the homogeneity assumption. Another challenge associated with RA is the scalability issue. In particular, RA usually involves ranking over tens of thousands of items, leading to an exponential volume of preferences for aggregation. Therefore, an inappropriate inference method would limit the application of the proposed model. This thesis considered RA under model misspecification in the following three scenarios: • In a crowdsourcing scenario, sufficient annotations from each user are available, which enables exploration of user heterogeneity to account for model misspecification. Therefore, I proposed a reliable CrowdsOUrced Plackett-LucE (COUPLE) model, which introduces an uncertainty vector to make a fine-grained categorization of users. Meanwhile, a general Bayesian Moment Matching (OnlineGBMM) was proposed, to ensure an analytic Bayesian update with an almost twice differentiable likelihood function. • In a general setting, typical model augmentation methods would cause overfitting, because insufficient annotations from each user are available. Inspired by the distributional robust literature, I proposed CoarsenRank, which performs regular RA over a neighborhood of preferences. The resultant inference would enjoy robustness against model misspecification. To this end, I first defined a neighborhood of the rank dataset using relative entropy. Then, I instantiated CoarsenRank with three popular probability ranking models and discussed the optimization strategies. • RA for mental fatigue monitoring. Common practices for mental fatigue monitoring refer to predicting the reaction time (RT) by aggregating the EEG signal from multiple heterogeneous EEG channels. Let us consider the RT as the item score and view each EEG channel as a user. The mental fatigue monitoring task could be formulated as RA under model misspecification, particularly in a crowdsourcing scenario. To address this problem, a Self-Weight Ordinal REgression (SWORE) model with Brain Dynamics table (BDtable) is proposed. The SWORE model could give a reliable evaluation of brain dynamics preferences from multiple channels, while the BDtable is employed to calibrate the SWORE model by utilizing the proposed online generalized Bayesian moment matching (OGMM) algorithm

Learning Robust Node Representations on Graphs

Graph neural networks (GNN), as a popular methodology for node representation learning on graphs, currently mainly focus on preserving the smoothness and identifiability of node representations. A robust node representation on graphs should further hold the stability property which means a node representation is resistant to slight perturbations on the input. In this paper, we introduce the stability of node representations in addition to the smoothness and identifiability, and develop a novel method called contrastive graph neural networks (CGNN) that learns robust node representations in an unsupervised manner. Specifically, CGNN maintains the stability and identifiability by a contrastive learning objective, while preserving the smoothness with existing GNN models. Furthermore, the proposed method is a generic framework that can be equipped with many other backbone models (e.g. GCN, GraphSage and GAT). Extensive experiments on four benchmarks under both transductive and inductive learning setups demonstrate the effectiveness of our method in comparison with recent supervised and unsupervised models.Comment: 16 page

Sanitized Clustering against Confounding Bias

Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysis. Existing methods eliminate the biases by projecting data onto the orthogonal complement of the subspace expanded by the confounding factor before clustering. Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions. These approaches are thus limited in scope as the data in real applications is usually complex and non-linearly correlated with the confounding factor. This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias (SCAB), which removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure. To be specific, we eliminate the bias information in the latent space by minimizing the mutual information between the confounding factor and the latent representation delivered by Variational Auto-Encoder (VAE). Meanwhile, a clustering module is introduced to cluster over the purified latent representations. Extensive experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance by removing the confounding bias. The code is available at \url{https://github.com/EvaFlower/SCAB}.Comment: Machine Learning, in pres

Coarse-to-Fine Contrastive Learning on Graphs

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding perturbations to the graph structure or node attributes. Although impressive results are achieved, it is rather blind to the wealth of prior information assumed: with the increase of the perturbation degree applied on the original graph, 1) the similarity between the original graph and the generated augmented graph gradually decreases; 2) the discrimination between all nodes within each augmented view gradually increases. In this paper, we argue that both such prior information can be incorporated (differently) into the contrastive learning paradigm following our general ranking framework. In particular, we first interpret CL as a special case of learning to rank (L2R), which inspires us to leverage the ranking order among positive augmented views. Meanwhile, we introduce a self-ranking paradigm to ensure that the discriminative information among different nodes can be maintained and also be less altered to the perturbations of different degrees. Experiment results on various benchmark datasets verify the effectiveness of our algorithm compared with the supervised and unsupervised models

Earning Extra Performance from Restrictive Feedbacks

Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named \emph{Earning eXtra PerformancE from restriCTive feEDdbacks} (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for the deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem.Comment: Accepted by IEEE TPAMI in April 202

Experimental Study on HMCVT Adaptive Control of Cotton Pickers

Aiming at the stability of the output speed and the poor adaptability of the transmission system during the operation of a cotton picker, a control strategy of hydro-mechanical continuously variable transmission (HMCVT) for cotton pickers based on gray prediction and fuzzy PID is proposed. Firstly, the hardware and software of the existing hydraulic mechanical coupling transmission test-bed of cotton pickers are designed, and the HMCVT human-computer interaction measurement and control system is built by using LABVIEW 2020 software. Then, combined with the transmission theory, the control strategy and gray prediction model are designed. Finally, the continuity test, transmission efficiency test, and adaptive control verification test are carried out. The results show that as the input speed increases, the peak time of the pump motor output speed is prolonged, while the overall speed regulation process is smoother, and the output speed process of the HMCVT system is continuous. As the displacement ratio of the variable pump increases, the transmission efficiency of the hydraulic system increases accordingly, but the highest efficiency is around 0.8. At a working speed of 10 km/h, the transmission efficiency of the HMCVT system of the cotton picker is more than 80%, and the high efficiency of the mechanical system in the whole system makes up for the low efficiency of the hydraulic system, and the transmission efficiency of the cotton picker is the highest at 15–25 km/h. The speed under the adaptive control strategy is better, with good robustness to sudden torque changes and speed fluctuations in the range of ±0.0125% under external load conditions. This study provides a reference for future adaptive control of transmission output speed for heavy-duty vehicles and construction machinery