GenAI4ELab/papercli-papers huggingface dataset:
A Coreset Learning Reality Check
Authors: Fred Lu; Edward Raff; James Holt
Venue: AAAI 2023 (main)

Abstract: Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.
Centerless Multi-View K-means Based on the Adjacency Matrix
Authors: Han Lu; Quanxue Gao; Qianqian Wang; Ming Yang; Wei Xia
Venue: AAAI 2023 (main)

Abstract: Although K-Means clustering has been widely studied due to its simplicity, these methods still have the following fatal drawbacks. Firstly, they need to initialize the cluster centers, which causes unstable clustering performance. Secondly, they have poor performance on non-Gaussian datasets. Inspired by the affinity matrix, we propose a novel multi-view K-Means based on the adjacency matrix. It maps the affinity matrix to the distance matrix according to the principle that every sample has a small distance from the points in its neighborhood and a large distance from the points outside of the neighborhood. Moreover, this method well exploits the complementary information embedded in different views by minimizing the tensor Schatten p-norm regularize on the third-order tensor which consists of cluster assignment matrices of different views. Additionally, this method avoids initializing cluster centroids to obtain stable performance. And there is no need to compute the means of clusters so that our model is not sensitive to outliers. Experiment on a toy dataset shows the excellent performance on non-Gaussian datasets. And other experiments on several benchmark datasets demonstrate the superiority of our proposed method.
PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor
Authors: Shun Lu; Yu Hu; Peihao Wang; Yan Han; Jianchao Tan; Jixiang Li; Sen Yang; Ji Liu
Venue: AAAI 2023 (main)

Abstract: Time-consuming performance evaluation is the bottleneck of traditional Neural Architecture Search (NAS) methods. Predictor-based NAS can speed up performance evaluation by directly predicting performance, rather than training a large number of sub-models and then validating their performance. Most predictor-based NAS approaches use a proxy dataset to train model-based predictors efficiently but suffer from performance degradation and generalization problems. We attribute these problems to the poor abilities of existing predictors to character the sub-models' structure, specifically the topology information extraction and the node feature representation of the input graph data. To address these problems, we propose a Transformer-like NAS predictor PINAT, consisting of a Permutation INvariance Augmentation module serving as both token embedding layer and self-attention head, as well as a Laplacian matrix to be the positional encoding. Our design produces more representative features of the encoded architecture and outperforms state-of-the-art NAS predictors on six search spaces: NAS-Bench-101, NAS-Bench-201, DARTS, ProxylessNAS, PPI, and ModelNet. The code is available at https://github.com/ShunLu91/PINAT.
Multi-View Domain Adaptive Object Detection on Camera Networks
Authors: Yan Lu; Zhun Zhong; Yuanchao Shu
Venue: AAAI 2023 (main)

Abstract: In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods.
Generative Label Enhancement with Gaussian Mixture and Partial Ranking
Authors: Yunan Lu; Liang He; Fan Min; Weiwei Li; Xiuyi Jia
Venue: AAAI 2023 (main)

Abstract: Label distribution learning (LDL) is an effective learning paradigm for dealing with label ambiguity. When applying LDL, the datasets annotated with label distributions (i.e., the real-valued vectors like the probability distribution) are typically required. Unfortunately, most existing datasets only contain the logical labels, and manual annotating with label distributions is costly. To address this problem, we treat the label distribution as a latent vector and infer its posterior by variational Bayes. Specifically, we propose a generative label enhancement model to encode the process of generating feature vectors and logical label vectors from label distributions in a principled way. In terms of features, we assume that the feature vector is generated by a Gaussian mixture dominated by the label distribution, which captures the one-to-many relationship from the label distribution to the feature vector and thus reduces the feature generation error. In terms of logical labels, we design a probability distribution to generate the logical label vector from a label distribution, which captures partial label ranking in the logical label vector and thus provides a more accurate guidance for inferring the label distribution. Besides, to approximate the posterior of the label distribution, we design a inference model, and derive the variational learning objective. Finally, extensive experiments on real-world datasets validate our proposal.
Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning
Authors: Linbo Luo; Yuanjing Li; Haiyan Yin; Shangwei Xie; Ruimin Hu; Wentong Cai
Venue: AAAI 2023 (main)

Abstract: Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets.
MVCINN: Multi-View Diabetic Retinopathy Detection Using a Deep Cross-Interaction Neural Network
Authors: Xiaoling Luo; Chengliang Liu; Waikeung Wong; Jie Wen; Xiaopeng Jin; Yong Xu
Venue: AAAI 2023 (main)

Abstract: Diabetic retinopathy (DR) is the main cause of irreversible blindness for working-age adults. The previous models for DR detection have difficulties in clinical application. The main reason is that most of the previous methods only use single-view data, and the single field of view (FOV) only accounts for about 13% of the FOV of the retina, resulting in the loss of most lesion features. To alleviate this problem, we propose a multi-view model for DR detection, which takes full advantage of multi-view images covering almost all of the retinal field. To be specific, we design a Cross-Interaction Self-Attention based Module (CISAM) that interfuses local features extracted from convolutional blocks with long-range global features learned from transformer blocks. Furthermore, considering the pathological association in different views, we use the feature jigsaw to assemble and learn the features of multiple views. Extensive experiments on the latest public multi-view MFIDDR dataset with 34,452 images demonstrate the superiority of our method, which performs favorably against state-of-the-art models. To the best of our knowledge, this work is the first study on the public large-scale multi-view fundus images dataset for DR detection.
Local Explanations for Reinforcement Learning
Authors: Ronny Luss; Amit Dhurandhar; Miao Liu
Venue: AAAI 2023 (main)

Abstract: Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this paper, we propose a novel perspective to understanding RL policies based on identifying important states from automatically learned meta-states. The key conceptual difference between our approach and many previous ones is that we form meta-states based on locality governed by the expert policy dynamics rather than based on similarity of actions, and that we do not assume any particular knowledge of the underlying topology of the state space. Theoretically, we show that our algorithm to find meta-states converges and the objective that selects important states from each meta-state is submodular leading to efficient high quality greedy selection. Experiments on four domains (four rooms, door-key, minipacman, and pong) and a carefully conducted user study illustrate that our perspective leads to better understanding of the policy. We conjecture that this is a result of our meta-states being more intuitive in that the corresponding important states are strong indicators of tractable intermediate goals that are easier for humans to interpret and follow.
Compositional Prototypical Networks for Few-Shot Classification
Authors: Qiang Lyu; Weiqiang Wang
Venue: AAAI 2023 (main)

Abstract: It is assumed that pre-training provides the feature extractor with strong class transferability and that high novel class generalization can be achieved by simply reusing the transferable feature extractor. In this work, our motivation is to explicitly learn some fine-grained and transferable meta-knowledge so that feature reusability can be further improved. Concretely, inspired by the fact that humans can use learned concepts or components to help them recognize novel classes, we propose Compositional Prototypical Networks (CPN) to learn a transferable prototype for each human-annotated attribute, which we call a component prototype. We empirically demonstrate that the learned component prototypes have good class transferability and can be reused to construct compositional prototypes for novel classes. Then a learnable weight generator is utilized to adaptively fuse the compositional and visual prototypes. Extensive experiments demonstrate that our method can achieve state-of-the-art results on different datasets and settings. The performance gains are especially remarkable in the 5-way 1-shot setting. The code is available at https://github.com/fikry102/CPN.
Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning
Authors: Xiaoting Lyu; Yufei Han; Wei Wang; Jingkai Liu; Bin Wang; Jiqiang Liu; Xiangliang Zhang
Venue: AAAI 2023 (main)

Abstract: Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? This is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful backdoor attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.
OMPQ: Orthogonal Mixed Precision Quantization
Authors: Yuexiao Ma; Taisong Jin; Xiawu Zheng; Yan Wang; Huixia Li; Yongjian Wu; Guannan Jiang; Wei Zhang; Rongrong Ji
Venue: AAAI 2023 (main)

Abstract: To bridge the ever-increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest trend of mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. However, existing approaches rely heavily on an extremely time-consuming search process and various relaxations when seeking the optimal bit configuration. To address this issue, we propose to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width. Our approach significantly reduces the search time and the required data amount by orders of magnitude, but without a compromise on quantization accuracy. Specifically, we achieve 72.08% Top-1 accuracy on ResNet-18 with 6.7Mb parameters, which does not require any searching iterations. Given the high efficiency and low data dependency of our algorithm, we use it for the post-training quantization, which achieves 71.27% Top-1 accuracy on MobileNetV2 with only 1.5Mb parameters.
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
Authors: Sérgio Machado; Anirudh Sridhar; Paulo Gil; Jorge Henriques; José M. F. Moura; Augusto Santos
Venue: AAAI 2023 (main)

Abstract: We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state of linear stochastic networked dynamical systems. We assume partial observability, where the state evolution of only a subset of nodes comprising the network is observed. We propose a new feature-based paradigm: to each pair of nodes, we compute a feature vector from the observed time series. We prove that these features are linearly separable, i.e., there exists a hyperplane that separates the cluster of features associated with connected pairs of nodes from those of disconnected pairs. This renders the features amenable to train a variety of classifiers to perform causal inference. In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level profiles. Remarkably, they also generalize well to real-world networks while trained over a synthetic network -- namely, a particular realization of a random graph.
LIMIP: Lifelong Learning to Solve Mixed Integer Programs
Authors: Sahil Manchanda; Sayan Ranu
Venue: AAAI 2023 (main)

Abstract: Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has gained attention due to its success in reducing the running time for solving MIPs. However, existing learning-to-branch methods assume that the entire training data is available in a single session of training. This assumption is often not true, and if the training data is supplied in continual fashion over time, existing techniques suffer from catastrophic forgetting. In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs. To mitigate catastrophic forgetting, we propose LIMIP, which is powered by the idea of modeling an MIP instance in the form of a bipartite graph, which we map to an embedding space using a bipartite Graph Attention Network. This rich embedding space avoids catastrophic forgetting through the application of knowledge distillation and elastic weight consolidation, wherein we learn the parameters key towards retaining efficacy and are therefore protected from significant drift. We evaluate LIMIP on a series of NP-hard problems and establish that in comparison to existing baselines, LIMIP is up to 50% better when confronted with lifelong learning
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
Authors: Gabriel Mancino-Ball; Shengnan Miao; Yangyang Xu; Jie Chen
Venue: AAAI 2023 (main)

Abstract: Consider a network of N decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called DEEPSTORM, that achieves optimal sample complexity for this setting. Unlike double-loop algorithms that require a large batch size to compute the (stochastic) gradient once in a while, DEEPSTORM uses a small batch size, creating advantages in occasions such as streaming data and online learning. This is the first method achieving optimal sample complexity for decentralized nonconvex stochastic composite problems, requiring O(1) batch size. We conduct convergence analysis for DEEPSTORM with both constant and diminishing step sizes. Additionally, under proper initialization and a small enough desired solution error, we show that DEEPSTORM with a constant step size achieves a network-independent sample complexity, with an additional linear speed-up with respect to N over centralized methods. All codes are made available at https://github.com/gmancino/DEEPSTORM.
Online Reinforcement Learning with Uncertain Episode Lengths
Authors: Debmalya Mandal; Goran Radanovic; Jiarui Gan; Adish Singla; Rupak Majumdar
Venue: AAAI 2023 (main)

Abstract: Existing episodic reinforcement algorithms assume that the  length of an episode is fixed across time and known a priori. In this paper, we consider a general framework of  episodic reinforcement learning when the length of each episode is drawn from a distribution. We first establish that this problem is equivalent to online reinforcement learning with general discounting where the learner is trying to optimize the expected discounted sum of rewards over an infinite horizon, but where the discounting function is not necessarily geometric. We show that minimizing regret with this new general discounting is equivalent to minimizing regret with uncertain episode lengths. We then design a reinforcement learning algorithm that minimizes regret with general discounting but acts for the setting with uncertain episode lengths. We instantiate  our general bound for different types of discounting, including geometric and polynomial discounting. We also show that we can obtain similar regret bounds even when the uncertainty over the episode lengths is unknown, by estimating the unknown distribution over time. Finally, we compare our learning algorithms with existing value-iteration based episodic RL algorithms on a grid-world environment.
Tight Performance Guarantees of Imitator Policies with Continuous Actions
Authors: Davide Maran; Alberto Maria Metelli; Marcello Restelli
Venue: AAAI 2023 (main)

Abstract: Behavioral Cloning (BC) aims at learning a policy that mimics the behavior demonstrated by an expert. The current theoretical understanding of BC is limited to the case of finite actions. In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions. We start by deriving a novel bound on the performance gap based on Wasserstein distance, applicable for continuous-action experts, holding under the assumption that the value function is Lipschitz continuous. Since this latter condition is hardy fulfilled in practice, even for Lipschitz Markov Decision Processes and policies, we propose a relaxed setting, proving that value function is always H\"older continuous. This result is of independent interest and allows obtaining in BC a general bound for the performance of the imitator policy. Finally, we analyze noise injection, a common practice in which the expert's action is executed in the environment after the application of a noise kernel. We show that this practice allows deriving stronger performance guarantees, at the price of a bias due to the noise addition.
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
Authors: Andrei Margeloiu; Nikola Simidjievski; Pietro Liò; Mateja Jamnik
Venue: AAAI 2023 (main)

Abstract: Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task.