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

有46篇文章，暂时不整理

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