tl;dr NeurIPS is the largest and argubably the most prestigious machine learning research conference internationally. We look at the 44 accepted publications from singapore-based authors in this 2020 edition of the conference, which is happening live this week (6 - 12 Dec 2020).

What is NeurIPS?

NeurIPS is the Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS). It is the largest conference in Artificial Intelligence with a specific focus on machine learning and neuroscience. Fueled by the resurgence of neural networks and advent of deep learning in 2012, it has grown to become one of (if not the most) prestigious international ML conference.

What was the acceptance rate like?

In the 2020 edition, the conference received 9,467 papers and accepted 1,900 papers, with about 20% acceptance rate across the topics.

Figure 1. Acceptance rate in each subject area, with comparison across 2018, 2019 and 2020. Figure retrieved from the official blog post on 8 December 2020.

The three most popular areas, in terms of submissions are: “Algorithms”, “Deep Learning” and “Applications”, although the latter two have seen a decline in the number of submissions. Acceptance rates for “Theory” and “Neuroscience” continue to be much higher than “Data, Challenges, Implementations, and Software”, which highlight the theoretical focus of the conference and the very high bar for application papers.

Which SG institute has put out more papers?

In the absence of a better metric to measure quality or impact, we look at the volume of accepted submissions per institute from the 44 accepted publications that we have identified as having an SG-based author.

Figure 2. Total accepted papers from each SG-based institute and collaborating institutes on these papers.

NUS and NTU top the charts in terms of volume of publications at NeurIPS this year with SUTD and SMU following closely.

List of Singapore’s Entries

We count 44 accepted publications with an author with a SG-based affiliation and they are listed as follows:

1Part-dependent Label Noise: Towards Instance-dependent Label NoiseXiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi SugiyamaPattern Recognition, Computer Science, Artificial Intelligence
2Causal Intervention for Weakly-Supervised Semantic Segmentation.Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru SunSegmentation, Natural Language Processing, Computer Science, Artificial Intelligence
3Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal EffectKaihua Tang, Jianqiang Huang, Hanwang ZhangMomentum, Econometrics, Mathematics, Form Of The Good, Causal Effect
4Interventional Few-Shot LearningZhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng HuaMedical Physics, Computer Science
5Provably Consistent Partial-Label LearningLei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi SugiyamaTheoretical Computer Science, Computer Science
6Dual T: Reducing Estimation Error for Transition Matrix in Label-noise LearningYu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi SugiyamaStochastic Matrix, Algorithm, Mathematics
7Watch out! Motion is Blurring the Vision of Your Deep Neural NetworksQing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang LiuComputer Vision, Computer Science, Artificial Intelligence, Deep Neural Networks
8Federated Bayesian Optimization via Thompson SamplingZhongxiang Dai, Bryan Kian Hsiang Low, Patrick JailletBayesian Optimization, Thompson Sampling, Machine Learning, Computer Science, Artificial Intelligence
9Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual LearningMassimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent CharlinKnowledge Management, Computer Science, Continual Learning
10Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement LearningCong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu ChiJob Shop Scheduling, Reinforcement Learning, Operations Research, Computer Science
11The route to chaos in routing games: When is price of anarchy too optimistic?Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, Georgios PiliourasPrice Of Anarchy, Mathematical Economics, Economics
12Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian TheobaltVoxel, Pattern Recognition, Computer Science, Artificial Intelligence
13Synthesizing Tasks for Block-based ProgrammingUmair Z. Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish SinglaProgramming Language, Computer Science
14Variational Bayesian UnlearningQuoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick JailletBayesian Probability, Artificial Intelligence
15Theory-Inspired Path-Regularized Differential Network Architecture SearchPan Zhou, Caiming Xiong, Richard Socher, Steven Chu Hong HoiNetwork Architecture, Artificial Intelligence, Computer Science
16Rethinking Importance Weighting for Deep Learning under Distribution ShiftTongtong Fang, Nan Lu, Gang Niu, Masashi SugiyamaWeighting, Deep Learning, Machine Learning, Computer Science, Artificial Intelligence
17ConvBERT: Improving BERT with Span-based Dynamic ConvolutionZi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng YanConvolution, Algorithm, Computer Science
18Neural encoding with visual attentionMeenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. SabuncuEncoding, Speech Recognition, Computer Science, Visual Attention
19Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based GamesYunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi ZhangReinforcement Learning, Artificial Intelligence, Computer Science
20MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler.Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi ChangMesa, Data Mining, Computer Science
21Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-SobolevXiao Wang, Qi Lei, Ioannis PanageasLangevin Dynamics, Sobolev Space, Geodesic, Manifold, Convergence, Mathematical Analysis, Mathematics
22Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient DescentDimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis SkoulakisGradient Descent, Dimensionality Reduction, Mathematical Optimization, Computer Science, Online Learning
23On Testing of SamplersKuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty
24Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution ExamplesJay Nandy, Wynne Hsu, Mong Li LeeMathematical Optimization, Computer Science
25No-Regret Learning and Mixed Nash Equilibria: They Do Not MixEmmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios PiliourasRegret, Nash Equilibrium, Mathematical Economics, Economics
26Optimal Query Complexity of Secure Stochastic Convex OptimizationWei Tang, Chien-Ju Ho, Yang LiuConvex Optimization, Mathematical Optimization, Computer Science
27Domain Generalization for Medical Imaging Classification with Linear-Dependency RegularizationHaoliang Li, Yufei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, Alex C. KotMedical Imaging, Regularization, Pattern Recognition, Computer Science, Artificial Intelligence, Linear Dependency
28Cross-Scale Internal Graph Neural Network for Image Super-ResolutionShangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change LoyAlgorithm, Computer Science, Cross Scale, Graph Neural Networks, Superresolution
29Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationJianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong LiInvariant, Algorithm, Computer Science, Optical Flow Estimation
30Inference Stage Optimization for Cross-scenario 3D Human Pose EstimationJianfeng Zhang, Xuecheng Nie, Jiashi FengInference, Pose, Machine Learning, Computer Science, Artificial Intelligence
31Efficient Distance Approximation for Structured High-Dimensional Distributions via LearningArnab Bhattacharyya, Sutanu Gayen, Kuldeep S Meel, N. V. VinodchandranMathematical Analysis, Mathematics, Distance Approximation, High Dimensional
32The Generalized Lasso with Nonlinear Observations and Generative PriorsZhaoqiang Liu, Jonathan ScarlettLasso, Prior Probability, Nonlinear System, Generative Grammar, Algorithm, Computer Science
33Improving Generalization in Reinforcement Learning with Mixture RegularizationKaixin Wang, Bingyi Kang, Jie Shao, Jiashi FengReinforcement Learning, Regularization, Artificial Intelligence, Computer Science
34Correlation Robust Influence MaximizationLouis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik NatarajanMaximization, Correlation, Mathematical Optimization, Mathematics
35Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian OptimizationSreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold SohBayesian Optimization, Artificial Intelligence, Computer Science, Inverse Reinforcement Learning
36Taming Discrete Integration via the Boon of DimensionalityJeffrey M. Dudek, Dror Fried, Kuldeep S. MeelCurse Of Dimensionality, Theoretical Computer Science, Computer Science
37Digraph Inception Convolutional NetworksZekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, Andrew LimDigraph, Discrete Mathematics, Computer Science
38Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep LearningPan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu Hong Hoi, Weinan EDeep Learning, Cognitive Science, Computer Science, Artificial Intelligence
39Balanced Meta-Softmax for Long-Tailed Visual RecognitionRen Jiawei, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, hongsheng LiSoftmax Function, Speech Recognition, Computer Science, Visual Recognition
40Chaos, Extremism and Optimism: Volume Analysis of Learning in GamesYun Kuen Cheung, Georgios PiliourasOptimism, Social Psychology, Psychology, Volume Analysis
41Spectral Temporal Graph Neural Network for Multivariate Time-series ForecastingDefu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi ZhangTime Series, Multivariate Statistics, Pattern Recognition, Computer Science, Artificial Intelligence, Graph Neural Networks
42Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement LearningJianda Chen, Shangyu Chen, Sinno Jialin PanReinforcement Learning, Channel, Pruning, Distributed Computing, Computer Science
43Self-Supervised Relationship ProbingJiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad Morariu, Handong Zhao, Tong SunMachine Learning, Psychology, Artificial Intelligence
44Partially View-aligned ClusteringZhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi PengCluster Analysis, Pattern Recognition, Computer Science, Artificial Intelligence

There is no particular order of note. The list was ranked by early citations, which is as good as random for now. Data was obtained from the pretty awesome Microsoft Academic Graph.

More ML Papers

Visit to view a full list of ML publications from Singapore.