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Tue 7:00 Implications of sparsity and high triangle density for graph representation learning
Hannah Sansford · Alexander Modell · Nick Whiteley · Patrick Rubin-Delanchy
Thu 2:15 Federated Learning under Distributed Concept Drift
Ellango Jothimurugesan · Kevin Hsieh · Jianyu Wang · Gauri Joshi · Phillip B Gibbons
Test Of Time
Wed 5:00 Deep Gaussian Processes
Neil Lawrence · Andreas Damianou
Invited Talk
Thu 0:00 An Automatic Finite-Sample Robustness Check: Can Dropping a Little Data Change Conclusions?
Tamara Broderick
Invited Talk
Tue 0:00 Causal Effect Estimation with Context and Confounders
Arthur Gretton
Tue 7:30 Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity
Youssef Allouah · Sadegh Farhadkhani · Rachid Guerraoui · Nirupam Gupta · Rafael Pinot · John Stephan
Thu 5:00 Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
Vincent Plassier · Eric Moulines · Alain Durmus
Thu 5:00 NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Muralikrishnna Guruswamy Sethuraman · Romain Lopez · Rahul Mohan · Faramarz Fekri · Tommaso Biancalani · Jan-Christian Huetter
Tue 7:30 The ELBO of Variational Autoencoders Converges to a Sum of Entropies
Simon Damm · Dennis Forster · Dmytro Velychko · Zhenwen Dai · Asja Fischer · Jörg Lücke
Wed 7:30 Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar · Sandeep Madireddy · Prasanna Balaprakash · Tapabrata Maiti · Gustavo de los Campos · Ian Fischer
Wed 7:30 Algorithm for Constrained Markov Decision Process with Linear Convergence
Egor Gladin · Maksim Lavrik-Karmazin · Karina Zainullina · Varvara Rudenko · Alexander Gasnikov · Martin Takac
Thu 5:00 Actually Sparse Variational Gaussian Processes
Jake Cunningham · Daniel Augusto de Souza · So Takao · Mark van der Wilk · Marc Deisenroth