pwc-archive/papers-with-abstracts huggingface dataset:
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA
Authors: Mohamed Karim Keraani 1, Khalil Mansour 1, Bilel Khlaifia 2, Nesrine Chehata 3, *
Date: 2022-05-30 00:00:00
Proceeding: XXIV ISPRS Congress 2022 5
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification, Transfer Learning

In this paper, we present an approach to land cover mapping from Sentinel-2 (S-2) satellite image time series using deep learning methods in the context of few shots in agricultural areas which aims to learn a classifier to recognize unseen classes during training with limited labelled examples. In many countries, there is a lack of Land Parcel Information Systems (LPIS) and thus of agricultural crop type annotations. Annotations are still based on fastidious digitization of parcels and in-field observations that are available in few numbers. Our idea is to transfer learning from pre-trained models on existing LPIS in France and apply them to a different geographical area in Kairouan in Central Tunisia. We build on work employing multi-headed self-attention mechanisms that have contributed to results that outperform other deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) in agricultural context using S-2 Time series. We used two transformer-based deep learning models PSE-TAE (Pixel-Set Encoders + Temporal Self-Attention) and PSE-LTAE (Pixel-Set Encoders + Lightweight Temporal Self-Attention). We first studied their generalisation capacity in a few shot context and on different geographical study site. Then, by transferring the knowledge of these models and adapting them to the Tunisian context with the transfer learning techniques we have demonstrated experimentally that the adaptation of these methods is efficient for land cover mapping in agricultural areas with few in-field
observations in terms of accuracy with an overall accuracy for with 17 classes.
Mixed integer linear optimization formulations for learning optimal binary classification trees
Authors: Brandon Alston, Hamidreza Validi, Illya V. Hicks
Date: 2022-06-10 00:00:00
Tasks: Binary Classification, Classification

Decision trees are powerful tools for classification and regression that attract many researchers working in the burgeoning area of machine learning. One advantage of decision trees over other methods is their interpretability, which is often preferred over other higher accuracy methods that are relatively uninterpretable. A binary classification tree has two types of vertices: (i) branching vertices which have exactly two children and where datapoints are assessed on a set of discrete features; and (ii) leaf vertices at which datapoints are given a discrete prediction. An optimal binary classification tree can be obtained by solving a biobjective optimization problem that seeks to (i) maximize the number of correctly classified datapoints and (ii) minimize the number of branching vertices. In this paper, we propose four mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees: two flow-based formulations and two-cut based formulations. We provide theoretical comparisons between our proposed formulations and the strongest flow-based MILO formulation of Aghaei et al. (2021). We conduct experiments on 13 publicly available datasets to show the models' ability to scale and the strength of a biobjective approach using Pareto frontiers. Our code and data are available on GitHub.
Comprehensive Fair Meta-learned Recommender System
Authors: Tianxin Wei, Jingrui He
Date: 2022-06-09 00:00:00
Tasks: counterfactual, Fairness, General Knowledge, Meta-Learning, Recommendation Systems

In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation scenarios, i.e. learning to learn the user preference by only a few past interaction items. The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively. They aim at deriving general knowledge across preference learning of various users, so as to rapidly adapt to the future new user with the learned prior and a small amount of training data. However, previous works have shown that recommender systems are generally vulnerable to bias and unfairness. Despite the success of meta-learning at improving the recommendation performance with cold-start, the fairness issues are largely overlooked. In this paper, we propose a comprehensive fair meta-learning framework, named CLOVER, for ensuring the fairness of meta-learned recommendation models. We systematically study three kinds of fairness - individual fairness, counterfactual fairness, and group fairness in the recommender systems, and propose to satisfy all three kinds via a multi-task adversarial learning scheme. Our framework offers a generic training paradigm that is applicable to different meta-learned recommender systems. We demonstrate the effectiveness of CLOVER on the representative meta-learned user preference estimator on three real-world data sets. Empirical results show that CLOVER achieves comprehensive fairness without deteriorating the overall cold-start recommendation performance.
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors
Authors: Renyu Zhu, Lei Yuan, Xiang Li, Ming Gao, Wenyuan Cai
Date: 2022-05-10 00:00:00
Proceeding: ACL 2022 5
Tasks: Clone Detection, Code Summarization, Graph Neural Network

Program understanding is a fundamental task in program language processing. Despite the success, existing works fail to take human behaviors as reference in understanding programs. In this paper, we consider human behaviors and propose the PGNN-EK model that consists of two main components. On the one hand, inspired by the "divide-and-conquer" reading behaviors of humans, we present a partitioning-based graph neural network model PGNN on the upgraded AST of codes. On the other hand, to characterize human behaviors of resorting to other resources to help code comprehension, we transform raw codes with external knowledge and apply pre-training techniques for information extraction. Finally, we combine the two embeddings generated from the two components to output code embeddings. We conduct extensive experiments to show the superior performance of PGNN-EK on the code summarization and code clone detection tasks. In particular, to show the generalization ability of our model, we release a new dataset that is more challenging for code clone detection and could advance the development of the community. Our codes and data are publicly available at https://github.com/RecklessRonan/PGNN-EK.
Rethinking Spatial Invariance of Convolutional Networks for Object Counting
Authors: Zhi-Qi Cheng, Qi Dai, Hong Li, Jingkuan Song, Xiao Wu, Alexander G. Hauptmann
Date: 2022-06-10 00:00:00
Proceeding: CVPR 2022 1
Tasks: Crowd Counting, Object, Object Counting, Position

Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map generation. In this paper, we try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. The purpose of this is to allow the feature extraction process to potentially stimulate the density map generation process to overcome the annotation noise. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our work points a new direction for follow-up research, which should investigate how to properly relax the overly strict pixel-level spatial invariance for object counting. We evaluate our methods on 4 mainstream object counting networks (i.e., MCNN, CSRNet, SANet, and ResNet-50). Extensive experiments were conducted on 7 popular benchmarks for 3 applications (i.e., crowd, vehicle, and plant counting). Experimental results show that our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects.
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
Authors: Sheng Zhang, Patrick Ng, Zhiguo Wang, Bing Xiang
Date: 2022-06-10 00:00:00
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction

Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extraction methods have achieved good performance on various benchmarks. However, we observe two shortcomings of previous methods: first, there is no unified framework that works well under various relation extraction settings; second, effectively utilizing external knowledge as background information is absent. In this work, we propose a knowledge-enhanced generative model to mitigate these two issues. Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities. Our model achieves superior performance on multiple benchmarks and settings, including WebNLG, NYT10, and TACRED.
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Authors: Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, Ji-Rong Wen
Date: 2022-06-10 00:00:00
Tasks: Disentanglement, Diversity, Re-Ranking

Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides considering the relevance between recommendations and trigger item, the recommendations should also be diversified to avoid information cocoons. However, existing diversified recommendation methods mainly focus on item-level diversity which is insufficient when the recommended items are all relevant to the target item. Moreover, redundant or noisy item features might affect the performance of simple feature-aware recommendation approaches. Faced with these issues, we propose a Feature Disentanglement Self-Balancing Re-ranking framework (FDSB) to capture feature-aware diversity. The framework consists of two major modules, namely disentangled attention encoder (DAE) and self-balanced multi-aspect ranker. In DAE, we use multi-head attention to learn disentangled aspects from rich item features. In the ranker, we develop an aspect-specific ranking mechanism that is able to adaptively balance the relevance and diversity for each aspect. In experiments, we conduct offline evaluation on the collected dataset and deploy FDSB on KuaiShou app for online A/B test on the function of relevant recommendation. The significant improvements on both recommendation quality and user experience verify the effectiveness of our approach.
On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks
Authors: Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar
Date: 2022-06-09 00:00:00
Tasks: Attribute, Movie Recommendation

We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set. LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIMB) that is superior to LIME and much faster than SHAP.
ReCo: A Dataset for Residential Community Layout Planning
Authors: Xi Chen, Yun Xiong, Siqi Wang, Haofen Wang, Tao Sheng, Yao Zhang, Yu Ye
Date: 2022-06-08 00:00:00
Tasks: Generative Adversarial Network, Layout Design, Layout Generation

Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particular attention since the advent of deep learning that facilitates the automated layout generation and spatial pattern recognition. However, the research circles generally suffer from the insufficiency of residential community layout benchmark or high-quality datasets, which hampers the future exploration of data-driven methods for residential community layout planning. The lack of datasets is largely due to the difficulties of large-scale real-world residential data acquisition and long-term expert screening. In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date. ReCo Dataset is presented in multiple data formats with 37,646 residential community layout plans, covering 598,728 residential buildings with height information. ReCo can be conveniently adapted for residential community layout related urban design tasks, e.g., generative layout design, morphological pattern recognition and spatial evaluation. To validate the utility of ReCo in automated residential community layout planning, two Generative Adversarial Network (GAN) based generative models are further applied to the dataset. We expect ReCo Dataset to inspire more creative and practical work in intelligent design and beyond. The ReCo Dataset is published at: https://www.kaggle.com/fdudsde/reco-dataset.
Causal Balancing for Domain Generalization
Authors: Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang
Date: 2022-06-10 00:00:00
Tasks: Domain Generalization

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark.
Meta Optimal Transport
Authors: Brandon Amos, samuel cohen, Giulia Luise, Ievgen Redko
Date: 2022-06-10 00:00:00

We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to rapidly predict and solve new problems. Otherwise, standard methods ignore the knowledge of the past solutions and suboptimally re-solve each problem from scratch. We instantiate Meta OT models in discrete and continuous settings between grayscale images, spherical data, classification labels, and color palettes and use them to improve the computational time of standard OT solvers. Our source code is available at http://github.com/facebookresearch/meta-ot
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Authors: Kamran Alipour, Aditya Lahiri, Ehsan Adeli, Babak Salimi, Michael Pazzani
Date: 2022-06-10 00:00:00
Tasks: Attribute, counterfactual, Decision Making

Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these black-box algorithms. Nevertheless, generating counterfactuals that can have a consistent impact on classifier outputs and yet expose interpretable feature changes is a very challenging task. We introduce a novel method to generate causal and yet interpretable counterfactual explanations for image classifiers using pretrained generative models without any re-training or conditioning. The generative models in this technique are not bound to be trained on the same data as the target classifier. We use this framework to obtain contrastive and causal sufficiency and necessity scores as global explanations for black-box classifiers. On the task of face attribute classification, we show how different attributes influence the classifier output by providing both causal and contrastive feature attributions, and the corresponding counterfactual images.
Interactively Learning Preference Constraints in Linear Bandits
Authors: David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Date: 2022-06-10 00:00:00
Tasks: Decision Making, Sequential Decision Making

We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize the challenge of interactively learning about these constraints as a novel linear bandit problem which we call constrained linear best-arm identification. To solve this problem, we propose the Adaptive Constraint Learning (ACOL) algorithm. We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case. In the average case, ACOL's sample complexity bound is still significantly tighter than bounds of simpler approaches. In synthetic experiments, ACOL performs on par with an oracle solution and outperforms a range of baselines. As an application, we consider learning constraints to represent human preferences in a driving simulation. ACOL is significantly more sample efficient than alternatives for this application. Further, we find that learning preferences as constraints is more robust to changes in the driving scenario than encoding the preferences directly in the reward function.
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Authors: Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng
Date: 2022-06-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially the return-on-investment (ROI) constraint. ROIs change non-monotonically during the sequential bidding process, and often induce a see-saw effect between constraint satisfaction and objective optimization. While some existing approaches show promising results in static or mildly changing ad markets, they fail to generalize to highly dynamic ad markets with ROI constraints, due to their inability to adaptively balance constraints and objectives amidst non-stationarity and partial observability. In this work, we specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, our method exploits an indicator-augmented reward function free of extra trade-off parameters and develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework to adaptively control the constraint-objective trade-off in non-stationary ad markets. Extensive experiments on a large-scale industrial dataset with two problem settings reveal that CBRL generalizes well in both in-distribution and out-of-distribution data regimes, and enjoys superior learning efficiency and stability.
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
Authors: Wonseok Hwang, Dongjun Lee, Kyoungyeon Cho, Hanuhl Lee, Minjoon Seo
Date: 2022-06-10 00:00:00
Tasks: Language Modelling

The recent advances of deep learning have dramatically changed how machine learning, especially in the domain of natural language processing, can be applied to legal domain. However, this shift to the data-driven approaches calls for larger and more diverse datasets, which are nevertheless still small in number, especially in non-English languages. Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task. The legal corpus consists of 147k Korean precedents (259M tokens), of which 63k are sentenced in last 4 years and 96k are from the first and the second level courts in which factual issues are reviewed. The two classification tasks are case names (11.3k) and statutes (2.8k) prediction from the factual description of individual cases. The LJP tasks consist of (1) 10.5k criminal examples where the model is asked to predict fine amount, imprisonment with labor, and imprisonment without labor ranges for the given facts, and (2) 4.7k civil examples where the inputs are facts and claim for relief and outputs are the degrees of claim acceptance. The summarization task consists of the Supreme Court precedents and the corresponding summaries (20k). We also release realistic variants of the datasets by extending the domain (1) to infrequent case categories in case name (31k examples) and statute (17.7k) classification tasks, and (2) to long input sequences in the summarization task (51k). Finally, we release LCUBE, the first Korean legal language model trained on the legal corpus from this study. Given the uniqueness of the Law of South Korea and the diversity of the legal tasks covered in this work, we believe that LBOX OPEN contributes to the multilinguality of global legal research. LBOX OPEN and LCUBE will be publicly available.