NeurIPS

Contrastive Neural Ratio Estimation for Simulation-based Inference

Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

Comments 11 pages. 34 pages with references and supplemental material. Accepted at NeurIPS 2022. Updated version corrects code implementation error and all experiments. Code at https://github.com/bkmi/cnre

The $s$-value: evaluating stability with respect to distributional shifts

Suyash Gupta, Dominik Rothenhäusler

Comments Camera ready version of 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning

Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do

Comments Accepted to NeurIPS 2023

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes

Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik

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

Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor

Julien Grand-Clément, Marek Petrik

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

Identifying Causal Effects via Context-specific Independence Relations

Santtu Tikka, Antti Hyttinen, Juha Karvanen

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

Journal ref 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Model-Free Active Exploration in Reinforcement Learning

Alessio Russo, Alexandre Proutiere

Journal ref Advances in Neural Information Processing Systems 36 (NeurIPS 2023)

Molecular Diffusion Models with Virtual Receptors

Matan Halfon, Eyal Rozenberg, Ehud Rivlin, Daniel Freedman

Journal ref https://neurips.cc/virtual/2023/77389

WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images

Yannik Glaser, Justin E. Stopa, Linnea M. Wolniewicz, Ralph Foster, Doug Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski

Comments 20 pages, 9 figures, submitted to NeurIPS 2024

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model

Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg

Comments NeurIPS 2023 spotlight; code: https://github.com/likenneth/honest_llama

Distribution Learnability and Robustness

Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner

Comments In NeurIPS 2023

On Computing Pairwise Statistics with Local Differential Privacy

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

Comments Published in NeurIPS 2023

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

Mucong Ding, Tahseen Rabbani, Bang An, Evan Z Wang, Furong Huang

Comments NeurIPS 2022

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution

Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson

Comments Published in Advances in Neural Information Processing Systems 36 (NeurIPS 2023)

GemNet: Universal Directional Graph Neural Networks for Molecules

Johannes Gasteiger, Florian Becker, Stephan Günnemann

Comments Published as a conference paper at NeurIPS 2021. Author name changed from Johannes Klicpera to Johannes Gasteiger

Efficient Graph Similarity Computation with Alignment Regularization

Wei Zhuo, Guang Tan

Comments NeurIPS 2022

EduQate: Generating Adaptive Curricula through RMABs in Education Settings

Sidney Tio, Dexun Li, Pradeep Varakantham

Comments 9 pages, NeurIPS 2024 pre-print

The Importance of Directional Feedback for LLM-based Optimizers

Allen Nie, Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

Comments Accepted and Presented at Foundation Models for Decision Making at NeurIPS 2023 (December 15, 2023). Work completed from June 2023 to September 2023

Learning DAGs from Data with Few Root Causes

Panagiotis Misiakos, Chris Wendler, Markus Püschel

Comments to be published in 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Journal ref NeurIPS 2023

Powerset Convolutional Neural Networks

Chris Wendler, Dan Alistarh, Markus Püschel

Comments Advances in Neural Information Processing Systems 32

Journal ref Advances in Neural Information Processing Systems, Vol. 32, pp. 927-938, 2019

A machine learning pipeline for automated insect monitoring

Aditya Jain, Fagner Cunha, Michael Bunsen, Léonard Pasi, Anna Viklund, Maxim Larrivée, David Rolnick

Journal ref NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning

espiownage: Tracking Transients in Steelpan Drum Strikes Using Surveillance Technology

Scott H. Hawley, Andrew C. Morrison, Grant S. Morgan

Comments 6 pages, 5 figures, submitted to NeurIPS 2021 Workshop on Machine Learning and the Physical Sciences

ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments

Ge Shi, Ziwen Kan, Jason Smucny, Ian Davidson

Comments 19 pages, 10 figures, submission to Neurips 2024

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems

Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng

Comments NeurIPS'23 Spotlight

Journal ref Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023) 56234-56255

Calibrating Where It Matters: Constrained Temperature Scaling

Stephen McKenna, Jacob Carse

Comments Presented at Medical Imaging Meets NeurIPS 2023

DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design

Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht

Comments To appear in ICML 2024. A preliminary version of this work (arXiv:2310.03494) was presented at the ALOE workshop, NeurIPS 2023. arXiv admin note: text overlap with arXiv:2310.03494

AstroCLIP: A Cross-Modal Foundation Model for Galaxies

Liam Parker, Francois Lanusse, Siavash Golkar, Leopoldo Sarra, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Geraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Regaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho

Comments 18 pages, accepted in Monthly Notices of the Royal Astronomical Society, Presented at the NeurIPS 2023 AI4Science Workshop

AdaPTwin: Low-Cost Adaptive Compression of Product Twins in Transformers

Emil Biju, Anirudh Sriram, Mert Pilanci

Comments 12 pages, 3 figures, submitted to NeurIPS 2024

SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding

Jiefeng Ma, Yan Wang, Chenyu Liu, Jun Du, Yu Hu, Zhenrong Zhang, Pengfei Hu, Qing Wang, Jianshu Zhang

Comments NeurIPS 2024 Track on Datasets and Benchmarks under review

Robust Knowledge Transfer in Tiered Reinforcement Learning

Jiawei Huang, Niao He

Comments 47 Pages; 1 Figure; NeurIPS 2023