NeurIPS

DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Salva Rühling Cachay, Bo Zhao, Hailey Joren, Rose Yu

Comments Accepted to NeurIPS 2023; Code is available at: https://github.com/Rose-STL-Lab/dyffusion

Journal ref Advances in Neural Information Processing Systems (NeurIPS), 2023

Efficient Equivariant Transfer Learning from Pretrained Models

Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs-Campbell, Payel Das, Lav R. Varshney

Journal ref NeurIPS 2023

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Lin Liu, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang

Comments Accepted by NeurIPS 2022

Journal ref Advances in Neural Information Processing Systems, 2022, 35: 11881-11892

Self-supervised Object-Centric Learning for Videos

Görkay Aydemir, Weidi Xie, Fatma Güney

Comments NeurIPS 2023

RoboHive: A Unified Framework for Robot Learning

Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Jay Vakil, Abhishek Gupta, Aravind Rajeswaran

Comments Accepted at 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks

$f$-Policy Gradients: A General Framework for Goal Conditioned RL using $f$-Divergences

Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang

Comments Accepted at NeurIPS 2023

Lo-Hi: Practical ML Drug Discovery Benchmark

Simon Steshin

Comments 29 pages, Advances in Neural Information Processing Systems, 2023

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

Kai Zhao, Qiyu Kang, Yang Song, Rui She, Sijie Wang, Wee Peng Tay

Comments Accepted by Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, Dec. 2023, spotlight

Self-Discriminative Modeling for Anomalous Graph Detection

Jinyu Cai, Yunhe Zhang, Jicong Fan

Comments This work was submitted to NeurIPS 2023 but was unfortunately rejected

Spiking PointNet: Spiking Neural Networks for Point Clouds

Dayong Ren, Zhe Ma, Yuanpei Chen, Weihang Peng, Xiaode Liu, Yuhan Zhang, Yufei Guo

Comments Accepted by NeurIPS

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

Jingyang Xiang, Siqi Li, Jun Chen, Shipeng Bai, Yukai Ma, Guang Dai, Yong Liu

Comments 14 pages, 4 figures, Accepted by 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

When is Agnostic Reinforcement Learning Statistically Tractable?

Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro

Comments Accepted to NeurIPS 2023

Importance-aware Co-teaching for Offline Model-based Optimization

Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu

Comments Accepted by NeurIPS 2023

Parallel-mentoring for Offline Model-based Optimization

Can Chen, Christopher Beckham, Zixuan Liu, Xue Liu, Christopher Pal

Comments Accepted by NeurIPS 2023

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Weijia Wu, Yuzhong Zhao, Hao Chen, Yuchao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, Chunhua Shen

Journal ref Proc. Advances In Neural Information Processing Systems (NeurIPS 2023)

Online Map Vectorization for Autonomous Driving: A Rasterization Perspective

Gongjie Zhang, Jiahao Lin, Shuang Wu, Yilin Song, Zhipeng Luo, Yang Xue, Shijian Lu, Zuoguan Wang

Comments [NeurIPS 2023]

Learning Domain-Aware Detection Head with Prompt Tuning

Haochen Li, Rui Zhang, Hantao Yao, Xinkai Song, Yifan Hao, Yongwei Zhao, Ling Li, Yunji Chen

Comments Accepted by NeurIPS 2023

L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference

Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues

Comments 27 pages, 6 figures, 3 tables, 6 appendices, NeurIPS 2023

Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, Xuelong Li

Comments Accepted by NeurIPS 2023. 22 pages

Swarm Reinforcement Learning For Adaptive Mesh Refinement

Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Simon Reisch, Luise Kärger, Gerhard Neumann

Comments Accepted at Neural Information Processing Systems (NeurIPS) 2023. Version 1 of this paper is a preliminary version that was accepted as a workshop paper in the International Conference on Learning Representations (ICLR) 2023 Workshop on Physics for Machine Learning

Asymmetric Certified Robustness via Feature-Convex Neural Networks

Samuel Pfrommer, Brendon G. Anderson, Julien Piet, Somayeh Sojoudi

Comments 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Expanding Small-Scale Datasets with Guided Imagination

Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng

Comments NeurIPS 2023. Source code: https://github.com/Vanint/DatasetExpansion

Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning

Zih-Yun Chiu, Yi-Lin Tuan, William Yang Wang, Michael C. Yip

Comments NeurIPS 2023

Rank-N-Contrast: Learning Continuous Representations for Regression

Kaiwen Zha, Peng Cao, Jeany Son, Yuzhe Yang, Dina Katabi

Comments NeurIPS 2023 Spotlight. The first two authors contributed equally to this paper

Sample Complexity of Forecast Aggregation

Tao Lin, Yiling Chen

Comments Accepted by NeurIPS 2023 (spotlight)

Posterior and Computational Uncertainty in Gaussian Processes

Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham

Comments Advances in Neural Information Processing Systems (NeurIPS 2022)

Uni3DETR: Unified 3D Detection Transformer

Zhenyu Wang, Yali Li, Xi Chen, Hengshuang Zhao, Shengjin Wang

Comments Accepted by NeurIPS 2023

Binary Classification with Confidence Difference

Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama

Comments NeurIPS 2023

Quantum Bayesian Optimization

Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet

Comments Accepted to NeurIPS 2023

A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing

Junren Chen, Jonathan Scarlett, Michael K. Ng, Zhaoqiang Liu

Comments Accepted to NeurIPS 2023