Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning最新文献_第10页

Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints 情景约束下强化学习的近最优保守探索

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-09 DOI: 10.48550/arXiv.2306.06265

Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang

{"title":"Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints","authors":"Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang","doi":"10.48550/arXiv.2306.06265","DOIUrl":"https://doi.org/10.48550/arXiv.2306.06265","url":null,"abstract":"This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the learning process. It focuses on the tabular episodic Markov Decision Process (MDP) setting that has finite states and actions. With the knowledge of an existing safe baseline policy, an algorithm termed as StepMix is proposed to balance the exploitation and exploration while ensuring that the conservative constraint is never violated in each episode with high probability. StepMix features a unique design of a mixture policy that adaptively and smoothly interpolates between the baseline policy and the optimistic policy. Theoretical analysis shows that StepMix achieves near-optimal regret order as in the constraint-free setting, indicating that obeying the stringent episode-wise conservative constraint does not compromise the learning performance. Besides, a randomization-based EpsMix algorithm is also proposed and shown to achieve the same performance as StepMix. The algorithm design and theoretical analysis are further extended to the setting where the baseline policy is not given a priori but must be learned from an offline dataset, and it is proved that similar conservative guarantee and regret can be achieved if the offline dataset is sufficiently large. Experiment results corroborate the theoretical analysis and demonstrate the effectiveness of the proposed conservative exploration strategies.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"27 1","pages":"19527-19564"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83764283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Self-Interpretable Time Series Prediction with Counterfactual Explanations 具有反事实解释的自解释时间序列预测

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-09 DOI: 10.48550/arXiv.2306.06024

Jingquan Yan, Hao Wang

引用次数: 1

Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning 偏好分类:通过辅助偏好学习改进文本分类器

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.04925

Jaehyung Kim, Jinwoo Shin, Dongyeop Kang

{"title":"Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning","authors":"Jaehyung Kim, Jinwoo Shin, Dongyeop Kang","doi":"10.48550/arXiv.2306.04925","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04925","url":null,"abstract":"The development of largely human-annotated benchmarks has driven the success of deep neural networks in various NLP tasks. To enhance the effectiveness of existing benchmarks, collecting new additional input-output pairs is often too costly and challenging, particularly considering their marginal impact on improving the current model accuracy. Instead, additional or complementary annotations on the existing input texts in the benchmarks can be preferable as an efficient way to pay the additional human cost. In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation. From 'pair-wise' comparisons with respect to the task, the auxiliary preference learning enables the model to learn an additional informative training signal that cannot be captured with 'instance-wise' task labels. To this end, we propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences. Here, we provide three different ways to collect preference signals in practice: (a) implicitly extracting from annotation records (for free, but often unavailable), (b) collecting explicitly from crowd workers (high paid), or (c) pre-trained large language models such as GPT-3 (low paid). Given existing classification NLP benchmarks, we demonstrate that the proposed auxiliary preference learning via P2C on them is effective in improving text classifiers. Our codes are publicly available.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"6 1","pages":"16807-16828"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78905568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification CoCo:无监督域自适应图分类的耦合对比框架

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.04979

Nan Yin, Libin Shen, Mengzhu Wang, L. Lan, Zeyu Ma, C. Chen, Xiansheng Hua, Xiao Luo

{"title":"CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification","authors":"Nan Yin, Libin Shen, Mengzhu Wang, L. Lan, Zeyu Ma, C. Chen, Xiansheng Hua, Xiao Luo","doi":"10.48550/arXiv.2306.04979","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04979","url":null,"abstract":"Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network branch and a hierarchical graph kernel network branch, which explore graph topology in implicit and explicit manners. Besides, we incorporate coupled branches into a holistic multi-view contrastive learning framework, which not only incorporates graph representations learned from complementary views for enhanced understanding, but also encourages the similarity between cross-domain example pairs with the same semantics for domain alignment. Extensive experiments on popular datasets show that our CoCo outperforms these competing baselines in different settings generally.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"14 1","pages":"40040-40053"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73405961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Task-specific experimental design for treatment effect estimation 治疗效果评估的特定任务实验设计

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.05484

Beth D. Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye

引用次数: 0

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian 预测量子哈密顿量的高效等变图网络

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.04922

Haiyang Yu, Zhao Xu, X. Qian, Xiaoning Qian, Shuiwang Ji

引用次数: 2

Federated Linear Contextual Bandits with User-level Differential Privacy 具有用户级差分隐私的联邦线性上下文强盗

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.05275

Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hajzinia, J. Yang

引用次数: 1

On Computing Optimal Tree Ensembles 关于计算最优树集成

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04423

Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge

{"title":"On Computing Optimal Tree Ensembles","authors":"Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge","doi":"10.48550/arXiv.2306.04423","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04423","url":null,"abstract":"Random forests and, more generally, (decisionnobreakdash-)tree ensembles are widely used methods for classification and regression. Recent algorithmic advances allow to compute decision trees that are optimal for various measures such as their size or depth. We are not aware of such research for tree ensembles and aim to contribute to this area. Mainly, we provide two novel algorithms and corresponding lower bounds. First, we are able to carry over and substantially improve on tractability results for decision trees, obtaining a $(6delta D S)^S cdot poly$-time algorithm, where $S$ is the number of cuts in the tree ensemble, $D$ the largest domain size, and $delta$ is the largest number of features in which two examples differ. To achieve this, we introduce the witness-tree technique which also seems promising for practice. Second, we show that dynamic programming, which has been successful for decision trees, may also be viable for tree ensembles, providing an $ell^n cdot poly$-time algorithm, where $ell$ is the number of trees and $n$ the number of examples. Finally, we compare the number of cuts necessary to classify training data sets for decision trees and tree ensembles, showing that ensembles may need exponentially fewer cuts for increasing number of trees.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"100 1","pages":"17364-17374"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89677484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training-Free Neural Active Learning with Initialization-Robustness Guarantees 具有初始化-鲁棒性保证的无训练神经主动学习

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04454

Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, K. H. Low

{"title":"Training-Free Neural Active Learning with Initialization-Robustness Guarantees","authors":"Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, K. H. Low","doi":"10.48550/arXiv.2306.04454","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04454","url":null,"abstract":"Existing neural active learning algorithms have aimed to optimize the predictive performance of neural networks (NNs) by selecting data for labelling. However, other than a good predictive performance, being robust against random parameter initializations is also a crucial requirement in safety-critical applications. To this end, we introduce our expected variance with Gaussian processes (EV-GP) criterion for neural active learning, which is theoretically guaranteed to select data points which lead to trained NNs with both (a) good predictive performances and (b) initialization robustness. Importantly, our EV-GP criterion is training-free, i.e., it does not require any training of the NN during data selection, which makes it computationally efficient. We empirically demonstrate that our EV-GP criterion is highly correlated with both initialization robustness and generalization performance, and show that it consistently outperforms baseline methods in terms of both desiderata, especially in situations with limited initial data or large batch sizes.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"29 1","pages":"12931-12971"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90006971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized Teacher Forcing for Learning Chaotic Dynamics 混沌动力学学习的广义教师强迫

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04406

F. Hess, Z. Monfared, Manuela Brenner, D. Durstewitz

{"title":"Generalized Teacher Forcing for Learning Chaotic Dynamics","authors":"F. Hess, Z. Monfared, Manuela Brenner, D. Durstewitz","doi":"10.48550/arXiv.2306.04406","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04406","url":null,"abstract":"Chaotic dynamical systems (DS) are ubiquitous in nature and society. Often we are interested in reconstructing such systems from observed time series for prediction or mechanistic insight, where by reconstruction we mean learning geometrical and invariant temporal properties of the system in question (like attractors). However, training reconstruction algorithms like recurrent neural networks (RNNs) on such systems by gradient-descent based techniques faces severe challenges. This is mainly due to exploding gradients caused by the exponential divergence of trajectories in chaotic systems. Moreover, for (scientific) interpretability we wish to have as low dimensional reconstructions as possible, preferably in a model which is mathematically tractable. Here we report that a surprisingly simple modification of teacher forcing leads to provably strictly all-time bounded gradients in training on chaotic systems, and, when paired with a simple architectural rearrangement of a tractable RNN design, piecewise-linear RNNs (PLRNNs), allows for faithful reconstruction in spaces of at most the dimensionality of the observed system. We show on several DS that with these amendments we can reconstruct DS better than current SOTA algorithms, in much lower dimensions. Performance differences were particularly compelling on real world data with which most other methods severely struggled. This work thus led to a simple yet powerful DS reconstruction algorithm which is highly interpretable at the same time.","PeriodicalId":74529,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning","volume":"27 1","pages":"13017-13049"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74683388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3