Related Papers in NeuralIPS 2021 (2021.12.06 - 2021.12.14)

2021/12/06 00:00:00 2021/12/06 00:00:00 paper list

Accept paper lists

Benchmarks papers

Main Track

Anomaly detection / Outlier / Out-of-distribution

  • Online false discovery rate control for anomaly detection in time series

    Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot

  • Detecting Anomalous Event Sequences with Temporal Point Processes

    Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

  • Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection

    HanQin Cai, Jialin Liu, Wotao Yin

  • Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data

    Dongmin Park · Hwanjun Song · Minseok Kim · Jae-Gil Lee

  • ReAct: Out-of-distribution Detection With Rectified Activations

    Yiyou Sun, Chuan Guo, Yixuan Li

  • STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data

    Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu

  • Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

    Keunseo Kim, JunCheol Shin, Heeyoung Kim

  • Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection

    Koby Bibas, Meir Feder, Tal Hassner

Interpretable / Explainable

  • Self-Interpretable Model with Transformation Equivariant Interpretation

    Yipei Wang, Xiaoqian Wang

  • Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

    Naoya Takeishi, Alexandros Kalousis

  • Scalable Rule-Based Representation Learning for Interpretable Classification

    Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang

  • Dynamic Inference with Neural Interpreters

    Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Vincent Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf

  • Understanding Instance-based Interpretability of Variational Auto-Encoders

    Zhifeng Kong, Kamalika Chaudhuri

  • Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

    Dylan Z Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

  • Explaining Latent Representations with a Corpus of Examples

    Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Causal discovery

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Data augmentation

  • Learning Debiased Representation via Disentangled Feature Augmentation

    Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, Jaegul Choo

  • Data Augmentation Can Improve Robustness

    Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy Mann

  • Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics

    Bhavin Choksi, Milad Mozafari, Callum Biggs O’May, B. ADOR, Andrea Alamia, Rufin VanRullen

  • How Data Augmentation affects Optimization for Linear Regression

    Boris Hanin, Yi Sun

  • AugMax: Adversarial Composition of Random Augmentations for Robust Training

    Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang

  • Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

    Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy

  • Self-Supervised GANs with Label Augmentation

    Liang Hou, Huawei Shen, Qi Cao, Xueqi Cheng

  • Explanation-based Data Augmentation for Image Classification

    Sandareka Wickramanayake, Wynne Hsu, Mong-Li Lee


Time series

  • SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

    Alicia Curth, Changhee Lee, Mihaela van der Schaar

  • CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

    YUSUKE TASHIRO, Jiaming Song, Yang Song, Stefano Ermon

  • Coresets for Time Series Clustering

    Lingxiao Huang, K. Sudhir, Nisheeth K Vishnoi

  • MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data

    Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, JUN ZHOU, Jianyong Zhou

  • Deep Explicit Duration Switching Models for Time Series

    Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alex Smola, Bernie Wang, Tim Januschowski

  • Online false discovery rate control for anomaly detection in time series

    Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot

  • Topological Attention for Time Series Forecasting

    Sebastian Zeng, Florian Graf, Christoph Hofer, Roland Kwitt

  • Adjusting for Autocorrelated Errors in Neural Networks for Time Series

    Fan-Keng Sun, Chris Lang, Duane S Boning

  • Probabilistic Transformer For Time Series Analysis

    Binh Tang, David S. Matteson

  • Dynamical Wasserstein Barycenters for Time-series Modeling

    Kevin C Cheng, Shuchin Aeron, Michael C Hughes, Eric Miller

  • Conformal Time-series Forecasting

    Kamilė Stankevičiūtė, Ahmed Alaa, Mihaela van der Schaar

  • Time-series Generation by Contrastive Imitation

    Daniel Jarrett, Ioana Bica, Mihaela van der Schaar


Missing value / Irregular sampled / Imputation

  • Identifiable Generative models for Missing Not at Random Data Imputation

    Chao Ma, Cheng Zhang

  • What’s a good imputation to predict with missing values?

    Marine Le Morvan, Julie Josse, Erwan Scornet, Gael Varoquaux

  • MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

    Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar

  • Assessing Fairness in the Presence of Missing Data

    Yiliang Zhang, Qi Long

  • Coresets for Clustering with Missing Values

    Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu

  • Time-series Generation by Contrastive Imitation

    Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

  • CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

    YUSUKE TASHIRO, Jiaming Song, Yang Song, Stefano Ermon


Sequence

  • Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

    Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, Lei Li

  • Sequence-to-Sequence Learning with Latent Neural Grammars

    Yoon Kim

  • A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering

    Tom Hess, Michal Moshkovitz, Sivan Sabato

  • Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling

    Hongyu Gong, Yun Tang, Juan Pino, Xian Li

  • Contrastively Disentangled Sequential Variational Autoencoder

    Junwen Bai, Weiran Wang, Carla P Gomes

  • Detecting Anomalous Event Sequences with Temporal Point Processes

    Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann


Heterogeneous

  • RelaySum for Decentralized Deep Learning on Heterogeneous Data

    Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U Stich, Martin Jaggi

  • Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media

    Yiyue Qian, Yiming Zhang, Yanfang Ye, Chuxu Zhang

  • Distributed Machine Learning with Sparse Heterogeneous Data

    Dominic Richards, Sahand Negahban, Patrick Rebeschini

  • FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

    Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, Nicholas Donald Lane


Recurrent neural network / RNN / LSTM / GRU

  • Charting and Navigating the Space of Solutions for Recurrent Neural Networks

    Elia Turner, Kabir Vinay Dabholkar, Omri Barak

  • Self-Instantiated Recurrent Units with Dynamic Soft Recursion

    Aston Zhang, Yi Tay, Yikang Shen, Alvin Chan, Shuai Zhang

  • SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization

    Ziming Zhang, Yun Yue, Guojun Wu, Yanhua Li, Haichong Zhang

  • (需要看看) On the Provable Generalization of Recurrent Neural Networks

    Lifu Wang, Bo Shen, Bo Hu, Xing Cao

  • Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation

    Jingyu Zhao, Yanwen Fang, Guodong Li

  • Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

    Jimmy T.H. Smith, Scott Linderman, David Sussillo

  • Noisy Recurrent Neural Networks

    Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney

  • Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers

    Albert Gu, Isys Johnson, Karan Goel, Khaled Kamal Saab, Tri Dao, Atri Rudra, Christopher Re

  • Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

    Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

  • (需要看看) Learning and Generalization in RNNs

    Abhishek Panigrahi, Navin Goyal

  • Framing RNN as a kernel method: A neural ODE approach

    Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau

  • Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training

    Anup Sarma, Sonali Singh, Huaipan Jiang, Rui Zhang, Mahmut Kandemir, Chita Das


Autoencoder

  • Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency

    Anish Chakrabarty, Swagatam Das

  • Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

    Naoya Takeishi, Alexandros Kalousis

  • On the Value of Infinite Gradients in Variational Autoencoder Models

    Bin Dai, Li Kevin Wenliang, David Wipf

  • (需要看看) Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders

    Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper

  • Neighborhood Reconstructing Autoencoders

    Yonghyeon LEE, Hyeokjun Kwon, Frank C. Park

  • Model Selection for Bayesian Autoencoders

    Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V Bonilla, Maurizio Filippone

  • Clockwork Variational Autoencoders

    Vaibhav Saxena, Jimmy Ba, Danijar Hafner

  • A Contrastive Learning Approach for Training Variational Autoencoder Priors

    Jyoti Aneja, Alex Schwing, Jan Kautz, Arash Vahdat

  • Contrastively Disentangled Sequential Variational Autoencoder

    Junwen Bai, Weiran Wang, Carla P Gomes

  • Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning

    Robin Winter, Frank Noe, Djork-Arne Clevert


Others

  • Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems

    Zixiu Wang, Yiwen Guo, Hu Ding

  • Automatic Unsupervised Outlier Model Selection

    Yue Zhao, Ryan Rossi, Leman Akoglu

  • Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration

    Yu Wang, Jingyang Lin, Jingjing Zou, Yingwei Pan, Ting Yao, Tao Mei

  • Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers

    Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis, Animesh Garg, Allan Douglas Jepson

  • Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers

    Tommaso d’Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel

  • Approximate optimization of convex functions with outlier noise

    Anindya De, Sanjeev Khanna, Huan Li, Hesam Nikpey


NeurIPS 2021 Datasets and Benchmarks Accepted Papers

Anomaly detection / Outlier / Out-of-distribution

  • SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

    Robin Chan · Krzysztof Lis · Svenja Uhlemeyer · Hermann Blum · Sina Honari · Roland Siegwart · Pascal Fua · Mathieu Salzmann · Matthias Rottmann

  • (要看一下) Revisiting Time Series Outlier Detection: Definitions and Benchmarks

    Henry Lai · Daochen Zha · Junjie Xu · Yue Zhao · Guanchu Wang · Xia Hu

Interpretable / Explainable

  • Chaos as an interpretable benchmark for forecasting and data-driven modelling

    William Gilpin

  • Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

    Yang Liu · Sujay Khandagale · Colin White · Willie Neiswanger

  • FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

    Mingjie Li · Wenjia Cai · Rui Liu · Yuetian Weng · Xiaoyun Zhao · Cong Wang · Xin Chen · Zhong Liu · Caineng Pan · Mengke Li · yingfeng zheng · Yizhi Liu · Flora Salim · Karin Verspoor · Xiaodan Liang · Xiaojun Chang

  • Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

    Sarah Wiegreffe · Ana Marasovic

causal discovery

  • Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning Nan Rosemary Ke · Aniket Didolkar · Sarthak Mittal · Anirudh Goyal ALIAS PARTH GOYAL · Guillaume Lajoie · Stefan Bauer · Danilo Jimenez Rezende · Michael Mozer · Yoshua Bengio · Chris Pal

Time series

  • Revisiting Time Series Outlier Detection: Definitions and Benchmarks

    Henry Lai · Daochen Zha · Junjie Xu · Yue Zhao · Guanchu Wang · Xia Hu

  • Monash Time Series Forecasting Archive

    Rakshitha Godahewa · Christoph Bergmeir · Geoffrey Webb · Rob Hyndman · Pablo Montero-Manso

  • Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions

    Chenyu Yi · SIYUAN YANG · Haoliang Li · Yap-peng Tan · Alex Kot