|
Workshop
|
|
High Probability Guarantees for Random Reshuffling
Hengxu Yu · Xiao Li
|
|
|
Workshop
|
|
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
Song Mei · Yuchen Wu
|
|
|
Workshop
|
Sat 8:50
|
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
Song Mei · Yuchen Wu
|
|
|
Workshop
|
|
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
Song Mei · Yuchen Wu
|
|
|
Poster
|
Wed 15:00
|
Sample Complexity of Forecast Aggregation
Tao Lin · Yiling Chen
|
|
|
Workshop
|
Sat 11:15
|
Benefits of learning with symmetries: eigenvectors, graph representations and sample complexity
Stefanie Jegelka
|
|
|
Workshop
|
|
Learning via Wasserstein-Based High Probability Generalisation Bounds
Paul Viallard · Maxime Haddouche · Umut Simsekli · Benjamin Guedj
|
|
|
Poster
|
Thu 8:45
|
Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise
Ta Duy Nguyen · Thien H Nguyen · Alina Ene · Huy Nguyen
|
|
|
Poster
|
Tue 15:15
|
Learning via Wasserstein-Based High Probability Generalisation Bounds
Paul Viallard · Maxime Haddouche · Umut Simsekli · Benjamin Guedj
|
|
|
Workshop
|
|
SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling
Benjamin Missaoui · Chongbin Yuan
|
|
|
Poster
|
Tue 15:15
|
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Behrooz Tahmasebi · Stefanie Jegelka
|
|
|
Workshop
|
|
Duality and Sample Complexity for the Gromov-Wasserstein Distance
Zhengxin Zhang · Ziv Goldfeld · Youssef Mroueh · Bharath Sriperumbudur
|
|
