NeurIPS

Optimal Sparse Decision Trees

Xiyang Hu, Cynthia Rudin, Margo Seltzer

Comments 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada

Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs

Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci

Comments Accepted to Neurips 2023

Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks

Hao-kai Zhang, Chenghong Zhu, Mingrui Jing, Xin Wang

Comments 28 pages including appendix. To appear at NeurIPS 2023

Uncertainty Aware Deep Learning for Particle Accelerators

Kishansingh Rajput, Malachi Schram, Karthik Somayaji

Comments 6 pages, 2 figures, Neurips Physical Sciences Workshop

Improving neural network representations using human similarity judgments

Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith

Comments Published as a conference paper at NeurIPS 2023

Permutation Equivariant Neural Functionals

Allan Zhou, Kaien Yang, Kaylee Burns, Adriano Cardace, Yiding Jiang, Samuel Sokota, J. Zico Kolter, Chelsea Finn

Comments To appear in Neural Information Processing Systems (NeurIPS), 2023

A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process

Jiayu Chen, Vaneet Aggarwal, Tian Lan

Comments This paper is accepted by NeurIPS 2023

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang, Yashas Malur Saidutta, Ching-Hua Lee, Yilin Shen, Hongxia Jin

Comments Accepted to Neural Information Processing Systems (NeurIPS) 2023 conference

Bicriteria Approximation Algorithms for the Submodular Cover Problem

Wenjing Chen, Victoria G. Crawford

Comments Accepted at NeurIPS 2023

Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator

Hanzhuo Huang, Yufan Feng, Cheng Shi, Lan Xu, Jingyi Yu, Sibei Yang

Comments NeurIPS 2023; Project available at: https://github.com/SooLab/Free-Bloom

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

Tianyi Liu, Kejun Wu, Yi Wang, Wenyang Liu, Kim-Hui Yap, Lap-Pui Chau

Comments Accepted by NeurIPS Dataset and Benchmark Track 2023

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

Mahesh Shakya, Bishesh Khanal

Comments accepted to NeurIPS 2023

Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks

Tolga Ergen, Mert Pilanci

Comments Accepted to NeurIPS 2023

3D Indoor Instance Segmentation in an Open-World

Mohamed El Amine Boudjoghra, Salwa K. Al Khatib, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Fahad Khan

Comments Accepted at NeurIPS 2023

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Shuwei Shao, Zhongcai Pei, Xingming Wu, Zhong Liu, Weihai Chen, Zhengguo Li

Comments Accepted by NeurIPS 2023

VidChapters-7M: Video Chapters at Scale

Antoine Yang, Arsha Nagrani, Ivan Laptev, Josef Sivic, Cordelia Schmid

Comments Accepted at NeurIPS 2023 Track on Datasets and Benchmarks; Project Webpage: https://antoyang.github.io/vidchapters.html ; 31 pages; 8 figures

Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts

Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru, Haifeng Xu

Comments NeurIPS 2023 (Spotlight)

GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph

Xin Li, Dongze Lian, Zhihe Lu, Jiawang Bai, Zhibo Chen, Xinchao Wang

Comments Accepted by NeurIPS 2023. The manuscript will be further revised based on the reviews

Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Dongjie Wang, Meng Xiao, Min Wu, Pengfei Wang, Yuanchun Zhou, Yanjie Fu

Comments Accepted by NeurIPS 2023

Iterative Reachability Estimation for Safe Reinforcement Learning

Milan Ganai, Zheng Gong, Chenning Yu, Sylvia Herbert, Sicun Gao

Comments Accepted in NeurIPS 2023

Dream the Impossible: Outlier Imagination with Diffusion Models

Xuefeng Du, Yiyou Sun, Xiaojin Zhu, Yixuan Li

Comments NeurIPS 2023

Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann

Comments To appear at NeurIPS 2023

Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head

Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu

Comments Accepted to NeurIPS 2023

Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma, Ting Wang

Comments Accepted by NeurIPS'23

DropCompute: simple and more robust distributed synchronous training via compute variance reduction

Niv Giladi, Shahar Gottlieb, Moran Shkolnik, Asaf Karnieli, Ron Banner, Elad Hoffer, Kfir Yehuda Levy, Daniel Soudry

Comments https://github.com/paper-submissions/dropcompute

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

DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho, Min-Seop Kwak, Sungjin Cho, Seungryong Kim

Comments To appear at NeurIPS 2023. Project Page: https://ku-cvlab.github.io/DaRF/

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun

Comments Accepted at NeurIPS 2023

Hierarchical Integration Diffusion Model for Realistic Image Deblurring

Zheng Chen, Yulun Zhang, Ding Liu, Bin Xia, Jinjin Gu, Linghe Kong, Xin Yuan

Comments Accepted to NeurIPS 2023 (Spotlight). Code is available at https://github.com/zhengchen1999/HI-Diff

MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing

Jianfei Yang, He Huang, Yunjiao Zhou, Xinyan Chen, Yuecong Xu, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie

Comments The paper has been accepted by NeurIPS 2023 Datasets and Benchmarks Track. Project page: https://ntu-aiot-lab.github.io/mm-fi

Partition and Code: learning how to compress graphs

Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein

Comments Published at NeurIPS 2021

Journal ref Proc. Adv. Neur. Inf. Process. Syst. (NeurIPS) vol. 34 pp. 18603 - 18619 (2021)