NeurIPS

Exploring User-level Gradient Inversion with a Diffusion Prior

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley Malin, Kieran Parsons, Ye Wang

Comments Presented at the International Workshop on Federated Learning in the Age of Foundation Models in conjunction with NeurIPS 2023

Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures

Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

Comments Accepted at NeurIPS 2023. 35 pages, 6 figures

Journal ref Published paper: https://openreview.net/forum?id=TXoZiUZywf | Oral presentation: https://neurips.cc/virtual/2023/oral/73845

Fitting trees to $\ell_1$-hyperbolic distances

Joon-Hyeok Yim, Anna C. Gilbert

Comments 12 pages, 2 figures, 14 pages supplementary. 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Journal ref Advances in Neural Information Processing Systems (2023) 7263-7288

CAST: Cross-Attention in Space and Time for Video Action Recognition

Dongho Lee, Jongseo Lee, Jinwoo Choi

Comments This is an accepted NeurIPS 2023. Project webpage is available at https://jong980812.github.io/CAST.github.io/ Code is available at https://github.com/KHU-VLL/CAST

Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction

Manuel Indaco, Daniel Gass, William James Fawcett, Richard Galvez, Paul J. Wright, Andrés Muñoz-Jaramillo

Comments Submission to Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

Adversarial Attacks on Transformers-Based Malware Detectors

Yash Jakhotiya, Heramb Patil, Jugal Rawlani, Sunil B. Mane

Comments Accepted to the 2022 NeurIPS ML Safety Workshop. Code available at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers

Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning

Minjong Yoo, Sangwoo Cho, Honguk Woo

Comments 12 pages, 5 figures, acceepted in NeurIPS 2022

Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning

Sakhinana Sagar Srinivas, Venkataramana Runkana

Comments Paper Accepted at Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models at NeurIPS 2023

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Comments 16 pages (main body) + 34 pages (supplemental material), accepted for publication in NeurIPS 2022 Track Datasets and Benchmarks

Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models

Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana

Comments Our paper is published at the workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models at NeurIPS 2023

Gamma Ray AGNs: Estimating Redshifts and Blazar Classification using traditional Neural Networks with smart initialization and self-supervised learning

Sarvesh Gharat, Abhimanyu Borthakur, Gopal Bhatta

Comments Extended Abstract accepted NeurIPS ML4PS workshop 2023. Complete manuscripts can be found at arXiv:2309.12846 and arXiv:2310.06095

A Spectral View of Adversarially Robust Features

Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant

Comments Neurips 2018 (this version corrects a minor error in the proof of Theorem 5)

Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design

Sakhinana Sagar Srinivas, Venkataramana Runkana

Comments Paper was accepted at R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Foundation Models, NeurIPS-2023. Please find the links: accepted-papers?authuser=0" target="_blank" rel="noopener">https://sites.google.com/view/r0-fomo/accepted-papers?authuser=0 and https://neurips.cc/virtual/2023/workshop/66517

Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles

Yifan Hu, Jie Wang, Xin Chen, Niao He

Comments A preliminary version of this manuscript has appeared in a conference proceeding. Please refer to Yifan Hu, Xin Chen, and Niao He. On the bias-variance-cost tradeoff of stochastic optimization. Advances in Neural Information Processing Systems, 2021

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity

Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang

Comments NeurIPS 2023

Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction

Tianyu Liu, Qitan Lv, Jie Wang, Shuling Yang, Hanzhu Chen

Journal ref Advances in Neural Information Processing Systems 36 (2024)

Adaptive Draft-Verification for Efficient Large Language Model Decoding

Xukun Liu, Bowen Lei, Ruqi Zhang, Dongkuan Xu

Comments Under review of Neurips 2024

Could a Large Language Model be Conscious?

David J. Chalmers

Comments Invited lecture at NeurIPS, November 28, 2022

Journal ref Boston Review, August 9, 2023

Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel

Yixin Chen, Tonmoy Dey, Alan Kuhnle

Comments 32 pages, 8 figures, to be published in NeurIPS 2021

Journal ref Advances in Neural Information Processing Systems 34 (2021): 25528-25539

FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection

Jiaqi Wang, Xiaochen Wang, Lingjuan Lyu, Jinghui Chen, Fenglong Ma

Comments Submitted to Neurips 2024 DB Track

GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data

Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

Comments Published in Transactions on Machine Learning Research (TMLR) 2024. Also accepted and selected for oral presentation at NeurIPS 2023 - Table Representation Learning Workshop

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan

Comments Accepted to NeurIPS 2023, the codes are available at https://github.com/qitianwu/SGFormer

On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective

Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama

Comments NeurIPS 2023, 21 pages, 20 figures. Keywords: Weight Decay, Regularization, Optimization, Deep Learning

Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection

Jordan F. Masakuna, DJeff Kanda Nkashama, Arian Soltani, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza

Comments This is an accepted extended abstract at Black in AI Workshop which will be co-located with NeurIPS 2024 in Canada

Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation

Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana

Comments Accepted in Workshop on Graph Learning for Industrial Applications : Finance, Crime Detection, Medicine, and Social Media (NeurIPS 2022)

Large Language Models Are Zero-Shot Time Series Forecasters

Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson

Comments NeurIPS 2023. Code available at: https://github.com/ngruver/llmtime

Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration

Dongyoung Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo

Comments NeurIPS 2024. Project webpage: https://sites.google.com/view/rl-vcse

Event-Based Contrastive Learning for Medical Time Series

Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott, Aparna Balagopalan, Bryan Jangeesingh, Marzyeh Ghassemi, Collin Stultz

Comments Accepted at Unifying Representations in Neural Models Workshop in NeurIPS 2023, MLHC 2024

Journal ref MLHC 2024

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems

Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré

Comments NeurIPS 2023; camera-ready version

Journal ref Advances in Neural Information Processing Systems 36, 2023

Physics-informed inference of aerial animal movements from weather radar data

Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré

Comments NeurIPS 2022, AI4Science workshop

Journal ref AI for Science Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), 2022