Related Papers in NeuralIPS 2021 (2021.12.06 - 2021.12.14)
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
- 1. Main Track
- 1.1. Anomaly detection / Outlier / Out-of-distribution
- 1.2. Interpretable / Explainable
- 1.3. Causal discovery
- 1.4. Data augmentation
- 1.5. Time series
- 1.6. Missing value / Irregular sampled / Imputation
- 1.7. Sequence
- 1.8. Heterogeneous
- 1.9. Recurrent neural network / RNN / LSTM / GRU
- 1.10. Autoencoder
- 1.11. Others
- 2. NeurIPS 2021 Datasets and Benchmarks Accepted Papers