Accepted Papers

Research Track

  • A Unified Batch Selection Policy for Active Metric Learning (10)
    Priyadarshini K, Siddhartha Chaudhuri, Vivek Borkar and Subhasis Chaudhuri
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  • Subspace Clustering based analysis of Neural Networks (11)
    Uday Singh Saini, Pravallika Devineni and Evangelos E. Papalexakis
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  • Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning (14)
    Zhang-Wei Hong, Prabhat Nagarajan and Guilherme Maeda
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  • Ensembling Shift Detectors: an Extensive Empirical Evaluation (16)
    Simona Maggio and Léo Dreyfus-Schmidt
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  • Deviation-based Marked Temporal Point Process for Marker Prediction (40)
    Maneet Singh, Anand Vir Singh Chauhan, Karamjit Singh, Tanmoy Bhowmik and Shivshankar Reddy
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  • Information Interaction Profile of Choice Adoption (45)
    Gaël Poux-Medard, Julien Velcin and Sabine Loudcher
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  • Learning to Build High-fidelity and Robust Environment Models (46)
    Weinan Zhang, Zhengyu Yang, Jian Shen, Minghuan Liu, Yimin Huang, Xing Zhang, Ruiming Tang and Zhenguo Li
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  • Off-Policy Differentiable Logic Reinforcement Learning (49)
    Li Zhang, Xin Li, Mingzhong Wang and Andong Tian
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  • Adaptive Learning Rate and Momentum for Training Deep Neural Networks (80)
    Zhiyong Hao, Yixuan Jiang, Huihua Yu and Hsiao-Dong Chiang
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  • Deep Conditional Transformation Models (83)
    Philipp Baumann, Torsten Hothorn and David Rügamer
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  • Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks (88)
    Ting-Wu Chin, Ari Morcos and Diana Marculescu
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  • Invertible Manifold Learning for Dimension Reduction (89)
    Li Siyuan, Lin Haitao, Zang Zelin, Wu Lirong, Xia Jun and Li Stan Z
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  • Inter-domain Multi-relational Link Prediction (105)
    Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi and Hisashi Kashima
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  • Attack Transferability Characterization for Adversarially Robust Multi-label Classification (129)
    Zhuo Yang, Yufei Han and Xiangliang Zhang
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  • GraphSVX: Shapley Value Explanations for Graph Neural Networks (135)
    Alexandre Duval and Fragkiskos Malliaros
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  • LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport (142)
    Yanbin Liu, Makoto Yamada, Yao-Hung Tsai, Tam Le, Ruslan Salakhutdinov and Yi Yang
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  • Deep Structural Point Process for Learning Temporal Interaction Networks (145)
    Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu and Bin Wang
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  • A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization (162)
    Jia Bi and Steve R Gunn
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  • Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning (163)
    Muhammad Rizki Maulana and Wee Sun Lee
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  • Spatial Contrastive Learning for Few-Shot Classification (164)
    Yassine Ouali, Céline Hudelot and Myriam Tami
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  • Very Fast Streaming Submodular Function Maximization (178)
    Sebastian Buschjäger, Philipp Honysz, Lukas Pfahler and Katharina Morik
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  • Multi-Agent Imitation Learning with Copulas (179)
    Hongwei Wang, Lantao Yu, Zhangjie Cao and Stefano Ermon
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  • CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints (181)
    Chenyi Liu, Nan Geng, Vaneet Aggarwal, Tian Lan, Yuan Yang and Mingwei Xu
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  • Disentanglement and Local Directions of Variance (184)
    Alexander Rakowski and Christoph Lippert
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  • Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks (191)
    Jun Zhuang and Mohammad Al Hasan
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  • Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More (193)
    Xiulong Yang, Hui Ye, Yang Ye, Xiang Li and Shihao Ji
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  • Dep-L0: Improving L0-based Network Sparsification via Dependency Modeling (194)
    Yang Li and Shihao Ji
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  • Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network (201)
    Pengxin Guo, Chang Deng, Linjie Xu, Xiaonan Huang and Yu Zhang
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  • Neural Topic Models for Hierarchical Topic Detection and Visualization (219)
    Dang Pham and Tuan Le
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  • Semi-structured Document Annotation using Entity and Relation Types (227)
    Arpita Kundu, Subhasish Ghosh and Indrajit Bhattacharya
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  • Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift (234)
    Etienne Bennequin, Victor Bouvier, Antoine Toubhans, Myriam Tami and Hudelot Céline
  • Gaussian Process encoders: VAEs with Reliable Latent-Space Uncertainty (237)
    Judith Bütepage, Lucas Maystre and Mounia Lalmas
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  • Which Minimizer Does My Neural Network Converge To? (264)
    Manuel Nonnenmacher, David Reeb and Ingo Steinwart
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  • Routine Bandits: Minimizing Regret on Recurring Problems (270)
    Hassan Saber, Leo Saci, Audrey Durand and Odalric-Ambrym Maillard
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  • Conservative Online Convex Optimization (271)
    Edoardo Vittori, Martino Bernasconi de Luca, Francesco Trovò and Marcello Restelli
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  • Differentiable Feature Selection, a Reparameterization Approach (272)
    Jérémie Dona and Patrick Gallinari
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  • Optimal Teaching Curricula with Compositional Simplicity Priors (277)
    Manuel Garcia-Piqueras and Jose Hernandez-Orallo
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  • Robustifying Out-of-distribution Detection Using Outlier Mining (282)
    Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang and Somesh Jha
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  • Causal Explanation of Convolutional Neural Networks (287)
    Hichem Debbi
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  • Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach (291)
    Bingjie He, Shukai Li, Chen Zhang, Baihua Zheng and Fugee Tsung
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  • Source Hypothesis Transfer for Zero-Shot Domain Adaptation (301)
    Tomoya Sakai
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  • Learning from Noisy Similar and Dissimilar Data (303)
    Soham Dan, Han Bao and Masashi Sugiyama
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  • Variance Reduced Stochastic Proximal Algorithm for AUC Maximization (306)
    Soham Dan and Dushyant Sahoo
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  • Active Learning in Gaussian Process State Space Model (312)
    Hon Sum Alec Yu, Dingling Yao, Christoph Zimmer, Marc Toussaint and Duy Nguyen-Tuong
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  • Robust Selection Stability Estimation in Correlated Spaces (343)
    Victor Hamer and Pierre Dupont
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  • Interpretable Counterfactual Explanations Guided by Prototypes (352)
    Arnaud Van Looveren and Janis Klaise
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  • Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection (354)
    Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Zhiwei Han and Martin Kleinsteuber
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  • Reservoir Pattern Sampling in Data Streams
    Arnaud Giacometti and Arnaud Soulet (356)
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  • Discovering proper neighbors to improve session-based recommendation (369)
    Lin Liu, Li Wang and Tao Lian
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  • Reconnaissance for Reinforcement Learning with Safety Constraints (392)
    Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintaro Okada, Masanori Koyama and Prabhat Nagarajan
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  • Small-Vote Sample Selection for Label-Noise Learning (399)
    Youze Xu, Yan Yan, Jing-Hao Xue, Yang Lu and Hanzi Wangg
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  • Multi-View Self-Supervised Heterogeneous Graph Embedding (408)
    Jianan Zhao, Qianlong Wen, Shiyu Sun, Yanfang Ye and Chuxu Zhang
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  • Robust Regression via Model Based Methods (418)
    Armin Moharrer, Khashayar Kamran, Edmund Yeh and Stratis Ioannidis
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  • Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation (435)
    Yuanxin Zhuang, Chuan Shi, Cheng Yang, Fuzhen Zhuang and Yangqiu Song
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  • VOGUE: Answer Verbalization through Multi-Task Learning (437)
    Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh, Jens Lehmann and Maria Maleshkova
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  • The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer (443)
    Maarten Buyl and Tijl De Bieos
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  • Gradient-based Label Binning in Multi-label Classification (449)
    Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz and Eyke Hüllermeier
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  • Joint Geometric and Topological Analysis of Hierarchical Datasets (453)
    Lior Aloni, Omer Bobrowski and Ronen Talmon
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  • Finding High-Value Training Data Subset through Differentiable Convex Programming (460)
    Soumi Das, Arshdeep Singh, Saptarshi Chatterjee, Suparna Bhattacharya and Sourangshu Bhattacharya
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  • Consequence-aware Sequential Counterfactual Generation (484)
    Philip Naumann and Eirini Ntoutsi
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  • Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound (489)
    Paul Viallard, Pascal Germain, Amaury Habrard and Emilie Morvant
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  • Midpoint Regularization: from High Uncertainty Training Labels to Conservative Classification Decisions (492)
    Hongyu Guo
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  • Sibling Regression for Generalized Linear Models (501)
    Shiv Shankar and Daniel Sheldon
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  • Unifying Domain Adaptation and Domain Generalization for Transfer among Racial Groups across Medical Systems (517)
    Farzaneh Khoshnevisan and Min Chi
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  • Continuous-time Markov-switching GARCH Process with Robust State Path Identification and Volatility Estimation (528)
    Yinan Li and Fang Liu
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  • Fast Conditional Network Compression Using Bayesian HyperNetworks (529)
    Phuoc Nguyen, Truyen Tran, Ky Le, Sunil Gupta, Santu Rana, Dang Nguyen, Trong Nguyen, Shannon Ryan and Svetha Venkatesh
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  • FedDNA: Federated Learning with Decoupled Normalization-Layer Aggregation for Non-IID Data (539)
    Jian-Hui Duan, Wenzhong Li and Sanglu Lu
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  • Black-box Optimizer with Stochastic Implicit Natural Gradient (547)
    Yueming Lyu and Ivor Tsang
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  • Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process (579)
    Yugang Ji, Jia Tianrui, Yuan Fang and Chuan Shi
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  • Model-based offline Policy Optimization with Distribution Correcting Regularization (581)
    Jian Shen, Mingcheng Chen, Zhicheng Zhang, Zhengyu Yang, Weinan Zhang and Yong Yu
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  • More General and Effective Model Compression via an Additive Combination of Compressions (590)
    Yerlan Idelbayev and Miguel Á. Carreira-Perpiñán
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  • NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction (591)
    Zhenyu Zhang, Bowen Yu, Xiaobo Shu and Tingwen Liu
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  • Certification of Model Robustness in Active Class Selection (598)
    Philipp Baumann, Torsten Hothorn and David Rügamer
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  • Explainable Online Deep Neural Network Selection using Adaptive Saliency Maps for Time Series Forecasting (599)
    Amal Saadallah, Matthias Jakobs and Katharina Morik
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  • Reparameterized Sampling for Generative Adversarial Networks (600)
    Yifei Wang, Yisen Wang, Jiansheng Yang and Zhouchen Lin
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  • On Generalization of Graph Autoencoders with Adversarial Training (604)
    Tianjin Huang, Vlado Menkovski, Yulong Pei and Mykola Pechenizkiy
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  • Disagreement Options: Task Adaptation Through Temporally Extended Actions (607)
    Matthias Hutsebaut-Buysse, Tom De Schepper, Kevin Mets and Steven Latre
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  • Hyper-Parameter Optimization for Latent Spaces in Dynamic Recommender Systems (613)
    Bruno Veloso, Luciano Caroprese, Matthias Konig, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos and Joao Gama
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  • Iterated Matrix Reordering (618)
    Gauthier Van Vracem and Siegfried Nijssen
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  • Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality (624)
    Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet and Nick Lim
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  • Inductive Link Prediction with Interactive Structure Learning on Attributed Graph (635)
    Shuo Yang, Binbin Hu, Zhiqiang Zhang, Wang Sun, Yang Wang, Yuetian Cao, Borui Ye, Xingyu Zhong, Jun Zhou and Yanming Fang
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  • Change Detection in Multivariate Datastreams Controlling False Alarms (636)
    Luca Frittoli, Diego Carrera and Giacomo Boracchi<
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  • Semi-Supervised Semantic Visualization for Networked Documents (647)
    Ce Zhang and Hady Lauw
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  • Representation Learning on Multi-Layered Heterogeneous Network (648)
    Ce Zhang and Hady Lauw
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  • FedPHP: Federated Personalization with Inherited Private Models (654)
    Li Xin-Chun, De-Chuan Zhan, Shao Yunfeng, Bingshuai Li and Shaoming Song
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  • Variational Hyper-Encoding Networks (659)
    Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam and Svetha Venkatesh
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  • Rumour Detection via Zero-shot Cross-lingual Transfer Learning (661)
    Lin Tian, Xiuzhen Zhang and Jey Han Lau
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  • Adaptive Node Embedding Propagation for Semi-Supervised Classification (662)
    Yuya Ogawa, Seiji Maekawa, Yuya Sasaki, Yasuhiro Fujiwara and Makoto Onizuka
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  • Explainable Multiple Instance Learning with Instance Selection Randomized Trees (672)
    Tomáš Komárek, Jan Brabec and Petr Somol
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  • Follow Your Path: a Progressive Method for Knowledge Distillation(676)
    Wenxian Shi, Yuxuan Song, Bohan Li, Hao Zhou and Lei Li
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  • The Curious Case of Convex Neural Networks (698)
    Sarath Sivaprasad, Ankur Singh, Naresh Manwani and Vineet Gandhi
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  • Bayesian Optimization with a Prior for the Optimum (701)
    Artur Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer and Frank Hutter
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  • GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs (702)
    Siddharth Bhatia, Yiwei Wang, Bryan Hooi and Tanmoy Tanmoy Chakraborty
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  • Probing Negative Sampling Strategies to Learn Graph Representations via Unsupervised Contrastive Learning (704)
    Shiyi Chen, Ziao Wang, Xinni Zhang, Xiaofeng Zhang and Dan Peng
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  • Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs (707)
    Shouheng Li, Dongwoo Kim and Qing Wang
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  • Adversarial Representation Learning With Closed-Form Solvers (708)
    Bashir Sadeghi, Lan Wang and Vishnu Boddeti
  • Self-Supervised Multi-Task Representation Learning for Sequential Medical Images (712)
    Nanqing Dong, Michael Kampffmeyer and Irina Voiculescu
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  • Learning Unbiased Representations via Rényi Minimization (722)
    Vincent Grari, Sylvain Lamprier and Marcin Detyniecki
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  • Privacy Amplification via Iteration for Shuffled and Online PNSGD (729)
    Matteo Sordello, Zhiqi Bu and Jinshuo Dong
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  • Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data (742)
    Sunil Aryal and Jonathan Wells
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  • Diversity-aware $k$-median: Clustering with fair center representation (746)
    Suhas Thejaswi, Bruno Ordozgoiti and Aristides Gionis
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  • Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration (761)
    Xuan Kan, Hejie Cui and Carl Yangi
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  • The Bures Metric for Generative Adversarial Networks (789)
    Hannes De Meulemeester, Joachim Schreurs, Michael Fanuel, Bart De Moor and Johan Suykens
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  • Learning Weakly Convex Sets in Metric Spaces (825)
    Eike Stadtländer, Tamas Horvath and Stefan Wrobel
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  • Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing (828)
    Kan Chen, Zhiqi Bu and Shiyun Xu
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  • Label-assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection (830)
    Shuyi Zhang, Chao Pan, Liyan Song, Xiaoyu Wu, Zheng Hu, Ke Pei, Peter Tino and Xin Yao
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  • Rank aggregation for non-stationary data streams (839)
    Ekhine Irurozki, Aritz Pérez, Jesús López and Javier Del Ser
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  • Learning disentangled representations with the Wasserstein Autoencoder (840)
    Benoit Gaujac, Ilya Feige and David Barber
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  • Graph Fraud Detection based on Accessibility Score Distributions (851)
    Minji Yoon
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  • Sparse Information Filter for Fast Gaussian Process Regression (854)
    Lucas Kania, Manuel Schuerch, Dario Azzimonti and Alessio Benavoli
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  • Deep Adaptive Multi-intention Inverse Reinforcement Learning (861)
    Ariyan Bighashdel, Panagiotis Meletis, Pavol Jancura and Gijs Dubbelman
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  • UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning (863)
    Robin Louiset, Pietro Gori, Benoit Dufumier, Josselin Houenou, Antoine Grigis and Edouard Duchesnay
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  • Approximation algorithms for confidence bands for time series (870)
    Nikolaj Tatti
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  • Unsupervised Task Clustering for Multi-Task Reinforcement Learning (878)
    Johannes Ackermann, Oliver Richter and Roger Wattenhofer
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  • Principled Interpolation in Normalizing Flows (885)
    Samuel G. Fadel, Sebastian Mair, Ricardo Torres and Ulf Brefeld
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  • A Mixed Noise and Constraint Based Approach to Causal Inference in Time Series (896)
    Karim Assaad, Emilie Devijver, Eric Gaussier and Ali Aït-Bachir
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  • CycleGan through the lens of (Dynamical) Optimal Transport (899)
    Emmanuel de Bézenac, Ibrahim Ayed and Patrick Gallinari
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  • Correlation Clustering with Global Weight Bounds (903)
    Domenico Mandaglio, Andrea Tagarelli and Francesco Gullo
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  • Deep Model Compression Via Two-Stage Deep Reinforcement Learning (908)
    Huixin Zhan, Wei-Ming Lin and Yongcan Cao
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  • TaxoRef: Embeddings Evaluation for AI-driven Taxonomy Refinement (907)
    Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica and Navid Nobani
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  • Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise (922)
    Andrea Castellani, Sebastian Schmitt and Barbara Hammer
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  • Efficient and Less Centralized Federated Learning (932)
    Li Chou, Zichang Liu, Zhuang Wang and Anshumali Shrivastava
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  • VeriDL: Integrity Verification of Outsourced Deep Learning Services (935)
    Boxiang Dong, Bo Zhang and Wendy Hui Wang
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  • MaxVA: Fast Adaptation of Stepsizes by Maximizing Observed Variance of Gradients (936)
    Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu and Tom Goldstein
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  • Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits (941)
    Kaushik Roy, Qi Zhang, Manas Gaur and Amit Sheth
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  • Quantized Gromov-Wasserstein (949)
    Samir Chowdhury, David Miller and Tom Needham
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  • Topological anomaly detection in dynamic multilayer blockchain networks (951)
    Dorcas Ofori-Boateng, Yulia Gel, Murat Kantarcioglu, Cuneyt Akcora and Ignacio Segovia Dominguez
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  • Augmenting Open-Domain Event Detection with Synthetic Data from GPT-2 (954)
    Amir Pouran Ben Veyseh, Minh Van Nguyen, Bonan Min and Thien Huu Nguyen
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  • Decoupling Sparsity and Smoothness in Dirichlet Belief Networks (958)
    Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li and Scott Sisson
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  • Continual Learning with Dual Regularizations (962)
    Xuejun Han and Yuhong Guo
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  • Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets (1011)
    Shayan Jawed, Hadi Jomaa, Lars Schmidt-Thieme and Josif Grabocka
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  • EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference (1014)
    Sumaiya Tabassum Nimi, Md Adnan Arefeen, Md Yusuf Sarwar Uddin and Yugyung Lee
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  • Exploiting History Data for Non-stationary MAB (1017)
    Gerlando Re, Fabio Chiusano, Francesco Trovò, Diego Carrera, Giacomo Boracchi and Marcello Restelli
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  • Bayesian crowdsourcing with constraints (1025)
    Panagiotis Traganitis and Georgios B. Giannakis
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  • Dropout's Dream Land: Generalization from Learned Simulators to Reality (1026)
    Zac Wellmer and James Kwok
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  • Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item Recommendation (1031)
    Yingpeng Du, Hongzhi Liu and Zhonghai Wu
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  • PATHATTACK: Attacking Shortest Paths in Complex Networks (1035)
    Benjamin Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik, Tina Eliassi-Rad and Scott Alfeld
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  • High-probability Kernel Alignment Regret Bounds for Online Kernel Selection (1037)
    Shizhong Liao and Junfan Li
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  • Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction (1040)
    Yun Yue, Yongchao Liu, Suo Tong, Minghao Li, Zhen Zhang, Chunyang Wen, Huanjun Bao, Lihong Gu, Jinjie Gu and Yixiang Mu
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  • Streaming Decision Trees for Lifelong Learning (1050)
    Lukasz Korycki and Bartosz Krawczyk
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  • Enhancing Summarization with Text Classification via Topic Consistency (1054)
    Jingzhou Liu and Yiming Yang
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  • Goal Modelling for Deep Reinforcement Learning Agents (1058)
    Jonathan Leung, Zhiqi Shen, Zhiwei Zeng and Chunyan Miao
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  • Disparity Between Batches as a Signal for Early Stopping (1075)
    Mahsa Forouzesh and Patrick Thiran
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  • Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities (1096)
    Afshin Sadeghi, Diego Collarana, Damien Graux and Jens Lehmann
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  • Transformers: "The End of History" for Natural Language Processing? (1104)
    Anton Chernyavskiy, Dmitry Ilvovsky and Preslav Nakov
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