GenAI4ELab/papercli-papers huggingface dataset:
Learning Noise-Induced Reward Functions for Surpassing Demonstrations in Imitation Learning
Authors: Liangyu Huo; Zulin Wang; Mai Xu
Venue: AAAI 2023 (main)

Abstract: Imitation learning (IL) has recently shown impressive performance in training a reinforcement learning agent with human demonstrations, eliminating the difficulty of designing elaborate reward functions in complex environments. However, most IL methods work under the assumption of the optimality of the demonstrations and thus cannot learn policies to surpass the demonstrators. Some methods have been investigated to obtain better-than-demonstration (BD) performance with inner human feedback or preference labels. In this paper, we propose a method to learn rewards from suboptimal demonstrations via a weighted preference learning technique (LERP). Specifically, we first formulate the suboptimality of demonstrations as the inaccurate estimation of rewards. The inaccuracy is modeled with a reward noise random variable following the Gumbel distribution. Moreover, we derive an upper bound of the expected return with different noise coefficients and propose a theorem to surpass the demonstrations. Unlike existing literature, our analysis does not depend on the linear reward constraint. Consequently, we develop a BD model with a weighted preference learning technique. Experimental results on continuous control and high-dimensional discrete control tasks show the superiority of our LERP method over other state-of-the-art BD  methods.
XClusters: Explainability-First Clustering
Authors: Hyunseung Hwang; Steven Euijong Whang
Venue: AAAI 2023 (main)

Abstract: We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is a difference of monotonic functions. We then propose an efficient branch-and-bound algorithm for finding the best parameters that lead to a balance of clustering accuracy and decision tree explainability. Our experiments show that our method can improve the explainability of any clustering that fits in our framework.
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
Authors: Taehyun Hwang; Min-hwan Oh
Venue: AAAI 2023 (main)

Abstract: We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with features of state and action. Despite much recent progress in analyzing algorithms in the linear MDP setting, the understanding of more general transition models is very restrictive. In this paper, we propose a provably efficient RL algorithm for the MDP whose state transition is given by a multinomial logistic model. We show that our proposed algorithm based on the upper confidence bounds achieves O(d√(H^3 T)) regret bound where d is the dimension of the transition core, H is the horizon, and T is the total number of steps. To the best of our knowledge, this is the first model-based RL algorithm with multinomial logistic function approximation with provable guarantees. We also comprehensively evaluate our proposed algorithm numerically and show that it consistently outperforms the existing methods, hence achieving both provable efficiency and practical superior performance.
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Authors: Yasutoshi Ida; Sekitoshi Kanai; Kazuki Adachi; Atsutoshi Kumagai; Yasuhiro Fujiwara
Venue: AAAI 2023 (main)

Abstract: Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two different domains. While it has a wide range of applications in machine learning, in some cases the sampled data from only one of the domains will have class labels such as unsupervised domain adaptation. In this kind of problem setting, a group-sparse regularizer is frequently leveraged as a regularization term to handle class labels. In particular, it can preserve the label structure on the data samples by corresponding the data samples with the same class label to one group-sparse regularization term. As a result, we can measure the distance while utilizing label information by solving the regularized optimization problem with gradient-based algorithms. However, the gradient computation is expensive when the number of classes or data samples is large because the number of regularization terms and their respective sizes also turn out to be large. This paper proposes fast discrete OT with group-sparse regularizers. Our method is based on two ideas. The first is to safely skip the computations of the gradients that must be zero. The second is to efficiently extract the gradients that are expected to be nonzero. Our method is guaranteed to return the same value of the objective function as that of the original approach. Experiments demonstrate that our method is up to 8.6 times faster than the original method without degrading accuracy.
Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks
Authors: Michał Jamroż; Marcin Kurdziel
Venue: AAAI 2023 (main)

Abstract: We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for representations learned by deep ResNets. We then use these models to characterize distributions of representations across learned classes. Surprisingly, we find that classes in the investigated models are not fitted in a uniform way. On the contrary: we uncover two groups of classes that are fitted with markedly different distributions of representations. These distinct modes of class-fitting are evident only in the deeper layers of the investigated models, indicating that they are not related to low-level image features. We show that the uncovered structure in neural representations correlate with memorization of training examples and adversarial robustness. Finally, we compare class-conditional distributions of neural representations between memorized and typical examples. This allows us to uncover where in the network structure class labels arise for memorized and standard inputs.
Audio-Visual Contrastive Learning with Temporal Self-Supervision
Authors: Simon Jenni; Alexander Black; John Collomosse
Venue: AAAI 2023 (main)

Abstract: We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision.   
In contrast to images that capture the static scene appearance, videos also contain sound and temporal scene dynamics.  
To leverage the temporal and aural dimension inherent to videos, our method extends temporal self-supervision to the audio-visual setting and integrates it with multi-modal contrastive objectives.
As temporal self-supervision, we pose playback speed and direction recognition in both modalities and propose intra- and inter-modal temporal ordering tasks. 
Furthermore, we design a novel contrastive objective in which the usual pairs are supplemented with additional sample-dependent positives and negatives sampled from the evolving feature space. 
In our model, we apply such losses among video clips and between videos and their temporally corresponding audio clips. 
We verify our model design in extensive ablation experiments and evaluate the video and audio representations in transfer experiments to action recognition and retrieval on UCF101 and HMBD51, audio classification on ESC50, and robust video fingerprinting on VGG-Sound, with state-of-the-art results.
Confidence-Aware Training of Smoothed Classifiers for Certified Robustness
Authors: Jongheon Jeong; Seojin Kim; Jinwoo Shin
Venue: AAAI 2023 (main)

Abstract: Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to l2-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions. Code is available at https://github.com/alinlab/smoothing-catrs.
Learnable Path in Neural Controlled Differential Equations
Authors: Sheo Yon Jhin; Minju Jo; Seungji Kook; Noseong Park
Venue: AAAI 2023 (main)

Abstract: Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting.
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations
Authors: Yuanfeng Ji; Lu Zhang; Jiaxiang Wu; Bingzhe Wu; Lanqing Li; Long-Kai Huang; Tingyang Xu; Yu Rong; Jie Ren; Ding Xue; Houtim Lai; Wei Liu; Junzhou Huang; Shuigeng Zhou; Ping Luo; Peilin Zhao; Yatao Bian
Venue: AAAI 2023 (main)

Abstract: AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual screening), little research has been conducted on the out-of-distribution (OOD) learning problem with noise. We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. Particularly, we focus on the drug-target binding affinity prediction problem, which involves both macromolecule (protein target) and small-molecule (drug compound). DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for graph OOD learning problems. Extensive empirical studies have revealed a significant performance gap between in-distribution and out-of-distribution experiments, emphasizing the need for the development of more effective schemes that permit OOD generalization under noise for AIDD.
MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding
Authors: Meihuizi Jia; Lei Shen; Xin Shen; Lejian Liao; Meng Chen; Xiaodong He; Zhendong Chen; Jiaqi Li
Venue: AAAI 2023 (main)

Abstract: Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both text and image. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.
Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features
Authors: Qingrui Jia; Xuhong Li; Lei Yu; Jiang Bian; Penghao Zhao; Shupeng Li; Haoyi Xiong; Dejing Dou
Venue: AAAI 2023 (main)

Abstract: While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power. Training dynamics, i.e., the traces left by iterations of optimization algorithms, have recently been proved to be effective to localize mislabeled samples with hand-crafted features.
In this paper, beyond manually designed features, we introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network, which learns to predict whether a sample was mislabeled using the raw training dynamics as input. 
Specifically, the proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises and can adapt to various datasets (either naturally or synthesized label-noised) without retraining. 
We conduct extensive experiments to evaluate the proposed method.
We train the noise detector based on the synthesized label-noised CIFAR dataset and test such noise detector on Tiny ImageNet, CUB-200, Caltech-256, WebVision and Clothing1M. 
Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation, and outperforms state-of-the-art methods.
Besides, more experiments demonstrate that the mislabel identification can guide a label correction, namely data debugging, providing orthogonal improvements of algorithm-centric state-of-the-art techniques from the data aspect.
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning
Authors: Jiechuan Jiang; Zongqing Lu
Venue: AAAI 2023 (main)

Abstract: Offline reinforcement learning could learn effective policies from a fixed dataset, which is promising for real-world applications. However, in offline decentralized multi-agent reinforcement learning, due to the discrepancy between the behavior policy and learned policy, the transition dynamics in offline experiences do not accord with the transition dynamics in online execution, which creates severe errors in value estimates, leading to uncoordinated low-performing policies. One way to overcome this problem is to bridge offline training and online tuning. However, considering both deployment efficiency and sample efficiency, we could only collect very limited online experiences, making it insufficient to use merely online data for updating the agent policy. To utilize both offline and online experiences to tune the policies of agents, we introduce online transition correction (OTC) to implicitly correct the offline transition dynamics by modifying sampling probabilities. We design two types of distances, i.e., embedding-based and value-based distance, to measure the similarity between transitions, and further propose an adaptive rank-based prioritization to sample transitions according to the transition similarity. OTC is simple yet effective to increase data efficiency and improve agent policies in online tuning. Empirically, OTC outperforms baselines in a variety of tasks.
Robust Domain Adaptation for Machine Reading Comprehension
Authors: Liang Jiang; Zhenyu Huang; Jia Liu; Zujie Wen; Xi Peng
Venue: AAAI 2023 (main)

Abstract: Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., Noisy Correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain. Undoubtedly, the noisy correspondence will degenerate the performance of MRC, which however is neglected by existing works. To solve such an untouched problem, we propose to construct QA pairs by additionally using the dialogue related to the documents, as well as a new domain adaptation method for MRC. Specifically, we propose Robust Domain Adaptation for Machine Reading Comprehension (RMRC) method which consists of an answer extractor (AE), a question selector (QS), and an MRC model. Specifically, RMRC filters out the irrelevant answers by estimating the correlation to the document via the AE, and extracts the questions by fusing the candidate questions in multiple rounds of dialogue chats via the QS. With the extracted QA pairs, MRC is fine-tuned and provides the feedback to optimize the QS through a novel reinforced self-training method. Thanks to the optimization of the QS, our method will greatly alleviate the noisy correspondence problem caused by the domain shift. To the best of our knowledge, this could be the first study to reveal the influence of noisy correspondence in domain adaptation MRC models and show a feasible solution to achieve the robustness against the mismatched pairs. Extensive experiments on three datasets demonstrate the effectiveness of our method.
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning
Authors: Lu Jiang; Yibin Wang; Jianan Wang; Pengyang Wang; Minghao Yin
Venue: AAAI 2023 (main)

Abstract: In this paper, we study the problem of MOOC quality evaluation that is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. 
While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. 
To tackle the challenges, we formulate the problem as a course representation learning task based, and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. 
Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. 
And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict multi-view semantics of courses. 
The course representation learning can be further converted to a multi-view graph representation task. 
Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: 
(1) the agreement on expressiveness between the raw course portfolio and the learned course representations; 
(2) the consistency between the representations in each view and the unified representations; 
(3) the alignment between the course and MOOC platform representations. 
Therefore, we propose to exploit mutual information for preserving the validity of course representations. 
We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Authors: Renhe Jiang; Zhaonan Wang; Jiawei Yong; Puneet Jeph; Quanjun Chen; Yasumasa Kobayashi; Xuan Song; Shintaro Fukushima; Toyotaro Suzumura
Venue: AAAI 2023 (main)

Abstract: Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning
Authors: Xiaopeng Jiang; Cristian Borcea
Venue: AAAI 2023 (main)

Abstract: Federated Learning (FL) is a privacy-preserving distributed deep learning paradigm that involves substantial communication and computation effort, which is a problem for resource-constrained mobile and IoT devices. Model pruning/sparsification develops sparse models that could solve this problem, but existing sparsification solutions cannot satisfy at the same time the requirements for low bidirectional communication overhead between the server and the clients, low computation overhead at the clients, and good model accuracy, under the FL assumption that the server does not have access to raw data to fine-tune the pruned models. We propose Complement Sparsification (CS), a pruning mechanism that satisfies all these requirements through a complementary and collaborative pruning done at the server and the clients.  At each round, CS creates a global sparse model that contains the weights that capture the general data distribution of all clients, while the clients create local sparse models with the weights pruned from the global model to capture the local trends. For improved model performance, these two types of complementary sparse models are aggregated into a dense model in each round, which is subsequently pruned in an iterative process. CS requires little computation overhead on the top of vanilla FL for both the server and the clients. We demonstrate that CS is an approximation of vanilla FL and, thus, its models perform well. We evaluate CS experimentally with two popular FL benchmark datasets. CS achieves substantial reduction in bidirectional communication, while achieving performance comparable with vanilla FL. In addition, CS outperforms baseline pruning mechanisms for FL.