NeurIPS

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

Chenxiao Yang, Qitian Wu, Junchi Yan

Comments in NeurIPS 2022

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan

Comments in NeurIPS 2022

torchode: A Parallel ODE Solver for PyTorch

Marten Lienen, Stephan Günnemann

Comments Accepted at The Symbiosis of Deep Learning and Differential Equations Workshop, NeurIPS, 2022

Local Bayesian optimization via maximizing probability of descent

Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett

Comments NeurIPS 2022

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

Kuan-Lin Chen, Harinath Garudadri, Bhaskar D. Rao

Comments 31 pages. Accepted at NeurIPS 2022

Deep Counterfactual Estimation with Categorical Background Variables

Edward De Brouwer

Comments Proceedings of the NeurIPS 2022 Conference

Improved Algorithms for Neural Active Learning

Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He

Comments Published on NeurIPS 2022

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

Kevin Bello, Bryon Aragam, Pradeep Ravikumar

Comments 28 pages, 13 figures, published at NeurIPS 2022

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes

Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry Vetrov

Comments Published in NeurIPS 2022. First three authors contributed equally

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms

Surbhi Goel, Sham Kakade, Adam Tauman Kalai, Cyril Zhang

Comments v2: final camera-ready revisions for NeurIPS 2022

RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence

Kyungmin Lee, Jinwoo Shin

Comments Camera-ready version

Journal ref NeurIPS 2022

3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds

Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi

Comments Accepted by NeurIPS 2022 Datasets and Benchmarks Track. Code: https://github.com/antoalli/3D_OS

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham Kakade, Eran Malach, Cyril Zhang

Comments v3: final camera-ready revisions for NeurIPS 2022

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon

Comments Published at NeurIPS 2022. The first two listed names contributed equally to this project

Adapting to Online Label Shift with Provable Guarantees

Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou

Comments NeurIPS 2022; the first two authors contributed equally

Learning Deep Input-Output Stable Dynamics

Yuji Okamoto, Ryosuke Kojima

Comments NeurIPS 2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

Bang An, Zora Che, Mucong Ding, Furong Huang

Comments Accepted to NeurIPS 2022

On the effectiveness of persistent homology

Renata Turkeš, Guido Montúfar, Nina Otter

Comments Main text 10 pages; Appendices 23 pages; References 6 pages; 32 figures. To appear in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Theorem 1 guarantees that PH with respect to the tubular filtration (Definition 1, Figure 9) can detect convexity in any d-dimensional Euclidean space (Appendix A). A convexity measure is detected with PH on a real-world dataset (Appendix G)

SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Song Bo, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, DengDeng Yu, Matthew Poon, Animesh Garg

Comments Neurips 2022. https://openreview.net/forum?id=TscdNx8udf5

AutoML Two-Sample Test

Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf

Comments NeurIPS 2022

Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction

Kaifeng Lyu, Zhiyuan Li, Sanjeev Arora

Comments 76 pages, many figures; NeurIPS 2022 camera-ready version; fixes minor typos

AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos

Yanze Wu, Xintao Wang, Gen Li, Ying Shan

Comments NeurIPS 2022. Codes and models are available at https://github.com/TencentARC/AnimeSR

Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables

Yi-Shan Wu, Yevgeny Seldin

Comments aligned with the camera-ready version published to NeurIPS 2022

Parallel Tempering With a Variational Reference

Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell

Comments In Advances in Neural Information Processing Systems, 2022

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

Nikolaj Thams, Michael Oberst, David Sontag

Comments NeurIPS 2022; Equal Contribution by Nikolaj/Michael, order determined by coin flip

TaSIL: Taylor Series Imitation Learning

Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni

Comments Appeared at NeurIPS 2022. V2: added to related work, updated notation, fixed small errors in appendix

The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization

Dongsung Huh, Avinash Baidya

Comments NeurIPS 2022

Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces

Mojmír Mutný, Andreas Krause

Journal ref NeurIPS 2022

Recipe for a General, Powerful, Scalable Graph Transformer

Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini

Comments In Proceedings of NeurIPS 2022

Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited

Nils M. Kriege

Comments NeurIPS 2022