All Papers

Unbiased Multi-Label Learning from Crowdsourced Annotations

Mingxuan Xia, Zenan Huang, Runze Wu et al.

Unbiased Watermark for Large Language Models

Zhengmian Hu, Lichang Chen, Xidong Wu et al.

Uncertainty-aware Action Decoupling Transformer for Action Anticipation

Hongji Guo, Nakul Agarwal, Shao-Yuan Lo et al.

Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning

Sheng Xu, Guiliang Liu

Uncertainty-Aware GAN for Single Image Super Resolution

Uncertainty-aware Graph-based Hyperspectral Image Classification

Linlin Yu, Yifei Lou, Feng Chen

Uncertainty-Aware Reward-Free Exploration with General Function Approximation

Junkai Zhang, Weitong Zhang, Dongruo Zhou et al.

Uncertainty-aware sign language video retrieval with probability distribution modeling

Xuan Wu, Hongxiang Li, yuanjiang luo et al.

Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer

Yuang Ai, Xiaoqiang Zhou, Huaibo Huang et al.

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

Jiayuan Chen, Kehan Guo, Zhen Liu et al.

Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset

Mijoo Kim, Junseok Kwon

Uncertainty-Driven Spectral Compressive Imaging with Spatial-Frequency Transformer

Lintao Peng, Siyu Xie, Liheng Bian

Uncertainty Estimation by Density Aware Evidential Deep Learning

Taeseong Yoon, Heeyoung Kim

Uncertainty for Active Learning on Graphs

Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier et al.

Uncertainty-Guided Never-Ending Learning to Drive

Lei Lai, Eshed Ohn-Bar, Sanjay Arora et al.

Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution

Tailin Wu, Willie Neiswanger, Hongtao Zheng et al.

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Shunsuke Horii, Yoichi Chikahara

Uncertainty Quantification via Stable Distribution Propagation

Felix Petersen, Aashwin Mishra, Hilde Kuehne et al.

Uncertainty Regularized Evidential Regression

Kai Ye, Tiejin Chen, Hua Wei et al.

Uncertainty Visualization via Low-Dimensional Posterior Projections

Omer Yair, Tomer Michaeli, Elias Nehme

Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning

James Chapman, Lennie Wells, Ana Lawry Aguila

Uncovering and Mitigating the Hidden Chasm: A Study on the Text-Text Domain Gap in Euphemism Identification

Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

Hang Du, Sicheng Zhang, Binzhu Xie et al.

Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

Emily McMilin

Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise

Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook et al.

Understanding Addition in Transformers

Philip Quirke, Fazl Barez

Understanding and Diagnosing Deep Reinforcement Learning

Ezgi Korkmaz

Understanding and Improving Optimization in Predictive Coding Networks

Nicholas Alonso, Jeffrey Krichmar, Emre Neftci

Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective

Yu Mitsuzumi, Akisato Kimura, Hisashi Kashima

Understanding and Leveraging the Learning Phases of Neural Networks

Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning

Zijun Long, Lipeng Zhuang, George W Killick et al.

Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks

Hao Chen, Jindong Wang, Ankit Parag Shah et al.

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression

Runtian Zhai, Bingbin Liu, Andrej Risteski et al.

Understanding Catastrophic Forgetting in Language Models via Implicit Inference

Suhas Kotha, Jacob Springer, Aditi Raghunathan

Understanding Certified Training with Interval Bound Propagation

Yuhao Mao, Mark N Müller, Marc Fischer et al.

Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory

Wei Huang, Ye Shi, Zhongyi Cai et al.

Understanding Diffusion Models by Feynman's Path Integral

Yuji Hirono, Akinori Tanaka, Kenji Fukushima

Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision

Wonjoon Chang, Dahee Kwon, Jaesik Choi

Understanding Domain Generalization: A Noise Robustness Perspective

Rui Qiao, Bryan Kian Hsiang Low

Understanding Expressivity of GNN in Rule Learning

Haiquan Qiu, Yongqi Zhang, Yong Li et al.

Understanding Finetuning for Factual Knowledge Extraction

Gaurav Ghosal, Tatsunori Hashimoto, Aditi Raghunathan

Understanding Forgetting in Continual Learning with Linear Regression

Meng Ding, Kaiyi Ji, Di Wang et al.

Understanding Heterophily for Graph Neural Networks

Junfu Wang, Yuanfang Guo, Liang Yang et al.

Understanding In-Context Learning from Repetitions

Jianhao (Elliott) Yan, Jin Xu, Chiyu Song et al.

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

Satwik Bhattamishra, Arkil Patel, Phil Blunsom et al.

Understanding Inter-Concept Relationships in Concept-Based Models

Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik

Understanding MLP-Mixer as a wide and sparse MLP

Tomohiro Hayase, Ryo Karakida

Understanding Multi-compositional learning in Vision and Language models via Category Theory

Sotirios Panagiotis Takis Chytas, Hyunwoo J. Kim, Vikas Singh

Understanding Physical Dynamics with Counterfactual World Modeling

Rahul Mysore Venkatesh, Honglin Chen, Kevin Feigelis et al.