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Tue 9:00 Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Jason Altschuler · Kunal Talwar
Thu 14:00 On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Vincent Szolnoky · Viktor Andersson · Balazs Kulcsar · Rebecka Jörnsten
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning
Shikun Li · Xiaobo Xia · Hansong Zhang · Yibing Zhan · Shiming Ge · Tongliang Liu
Confidence-based Reliable Learning under Dual Noises
Peng Cui · Yang Yue · Zhijie Deng · Jun Zhu
Thu 14:00 Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation
Lars Schmarje · Vasco Grossmann · Claudius Zelenka · Sabine Dippel · Rainer Kiko · Mariusz Oszust · Matti Pastell · Jenny Stracke · Anna Valros · Nina Volkmann · Reinhard Koch
Tue 9:00 Active Ranking without Strong Stochastic Transitivity
Hao Lou · Tao Jin · Yue Wu · Pan Xu · Quanquan Gu · Farzad Farnoud
Self-Supervised Image Restoration with Blurry and Noisy Pairs
Zhilu Zhang · RongJian Xu · Ming Liu · Zifei Yan · Wangmeng Zuo
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Xi Jiang · Jianlin Liu · Jinbao Wang · Qiang Nie · Kai WU · Yong Liu · Chengjie Wang · Feng Zheng
MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification
REN WANG · Jiayue Wang · Tae Sung Kim · JINSUNG KIM · Hyuk-Jae Lee
Thu 14:00 Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data
Yusuke Tanaka · Tomoharu Iwata · naonori ueda
Tue 9:00 Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Jiayuan Ye · Reza Shokri
Tue 10:00 Panel 1C-5: Privacy of Noisy… & Near-Optimal Private and…
Shyam Narayanan · Kunal Talwar