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Oral
Tue 10:45 Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Leena Chennuru Vankadara · Sebastian Bordt · Ulrike von Luxburg · Debarghya Ghoshdastidar
Oral
Tue 12:15 GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
Mucong Ding · Constantinos Daskalakis · Soheil Feizi
Poster
Tue 14:00 Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Yu Gong · Hossein Hajimirsadeghi · Jiawei He · Thibaut Durand · Greg Mori
Poster
Tue 14:00 Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.
Neil Jethani · Mukund Sudarshan · Yindalon Aphinyanaphongs · Rajesh Ranganath
Poster
Tue 14:00 The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
Fergus Simpson · Alexis Boukouvalas · Vaclav Cadek · Elvijs Sarkans · Nicolas Durrande
Poster
Tue 14:00 On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Youssef Mroueh · Truyen Nguyen
Poster
Tue 14:00 On the Privacy Properties of GAN-generated Samples
Zinan Lin · Vyas Sekar · Giulia Fanti
Poster
Tue 14:00 Learning GPLVM with arbitrary kernels using the unscented transformation
Daniel Augusto Ramos Macedo Antunes de Souza · Diego Mesquita · João Paulo Gomes · César Lincoln Mattos
Poster
Tue 14:00 Principal Subspace Estimation Under Information Diffusion
Fan Zhou · Ping Li · Zhixin Zhou
Poster
Tue 14:00 Misspecification in Prediction Problems and Robustness via Improper Learning
Annie Marsden · John Duchi · Gregory Valiant
Poster
Tue 14:00 Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Mayee Chen · Benjamin Cohen-Wang · Stephen Mussmann · Frederic Sala · Christopher Re
Poster
Tue 18:30 Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
Frederik Kunstner · Raunak Kumar · Mark Schmidt