NeurIPS 2024 Conference Submissions

Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization
Mingwei Xu, Xiaofeng Cao, Ivor Tsang
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Policy-shaped prediction: avoiding distractions in model-based reinforcement learning
Miles Richard Hutson, Isaac Kauvar, Nick Haber
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Recognize Any Regions
Haosen Yang, Chuofan Ma, Bin Wen, Yi Jiang, Zehuan Yuan, Xiatian Zhu
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
- Published: 25 Sept 2024, Last Modified: 15 Jan 2025
- NeurIPS 2024 poster
- Readers: Everyone
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 spotlight
- Readers: Everyone
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
Jisong Kim, Minjae Seong, Jun Won Choi
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
On the Surprising Effectiveness of Attention Transfer for Vision Transformers
Alexander Cong Li, Yuandong Tian, Beidi Chen, Deepak Pathak, Xinlei Chen
- Published: 25 Sept 2024, Last Modified: 16 Jan 2025
- NeurIPS 2024 poster
- Readers: Everyone
A Canonicalization Perspective on Invariant and Equivariant Learning
George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead
Minsu Kim, Walid Saad, Merouane Abdelkader DEBBAH, Choong Seon Hong
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Tree of Attributes Prompt Learning for Vision-Language Models
Tong Ding, Wanhua Li, Zhongqi Miao, Hanspeter Pfister
- 15 May 2024 (modified: 06 Nov 2024)
- Submitted to NeurIPS 2024
- Readers: Everyone
UniAR: A Unified model for predicting human Attention and Responses on visual content
Peizhao Li, Junfeng He, Gang Li, Rachit Bhargava, Shaolei Shen, NACHIAPPAN VALLIAPPAN, Youwei Liang, Hongxiang Gu, Venky Ramachandran, Golnaz farhadi, Yang Li, Kai J Kohlhoff, Vidhya Navalpakkam
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Hypothesis Testing the Circuit Hypothesis in LLMs
Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David Blei
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
Martin Andres Bertran, Shuai Tang, Michael Kearns, Jamie Heather Morgenstern, Aaron Roth, Steven Wu
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
Ruiqi Zhong, Heng Wang, Dan Klein, Jacob Steinhardt
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Faster Algorithms for User-Level Private Stochastic Convex Optimization
Andrew Lowy, Daogao Liu, Hilal Asi
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
Milad Khademi Nori, IL MIN KIM
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Boundary Decomposition for Nadir Objective Vector Estimation
Ruihao Zheng, Zhenkun Wang
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
OSLO: One-Shot Label-Only Membership Inference Attacks
Yuefeng Peng, Jaechul Roh, Subhransu Maji, Amir Houmansadr
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul Krishnan, Chris J. Maddison
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Semidefinite Relaxations of the Gromov-Wasserstein Distance
Junyu Chen, Binh Nguyen, Shang Hui Koh, Yong Sheng Soh
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Are Graph Neural Networks Optimal Approximation Algorithms?
Morris Yau, Nikolaos Karalias, Eric Hanqing Lu, Jessica Xu, Stefanie Jegelka
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 spotlight
- Readers: Everyone
TableRAG: Million-Token Table Understanding with Language Models
Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister
- Published: 25 Sept 2024, Last Modified: 26 Dec 2024
- NeurIPS 2024 poster
- Readers: Everyone
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 spotlight
- Readers: Everyone
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
Anthony Liang, Guy Tennenholtz, ChihWei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone