Papers
Free-rider Attacks on Model Aggregation in Federated Learning
Yann Fraboni, Richard Vidal, Marco Lorenzi
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
Yahav Bechavod, Katrina Ligett, Steven Wu et al.
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
Mucong Ding, Constantinos Daskalakis, Soheil Feizi
Generalization Bounds for Stochastic Saddle Point Problems
Junyu Zhang, Mingyi Hong, Mengdi Wang et al.
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
Damien Scieur, Lewis Liu, Thomas Pumir et al.
Generalized Spectral Clustering via Gromov-Wasserstein Learning
Samir Chowdhury, Tom Needham
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, Rory Mc Grath et al.
Geometrically Enriched Latent Spaces
Georgios Arvanitidis, Soren Hauberg, Bernhard Schölkopf
Good Classifiers are Abundant in the Interpolating Regime
Ryan Theisen, Jason Klusowski, Michael Mahoney
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Song Wei, Shixiang Zhu, Minghe Zhang et al.
Gradient Descent in RKHS with Importance Labeling
Tomoya Murata, Taiji Suzuki
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Nafiseh Ghoroghchian, Gautam Dasarathy, Stark Draper
Graph Gamma Process Linear Dynamical Systems
Rahi Kalantari, Mingyuan Zhou
Graphical Normalizing Flows
Antoine Wehenkel, Gilles Louppe
Group testing for connected communities
Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo et al.
Hadamard Wirtinger Flow for Sparse Phase Retrieval
Fan Wu, Patrick Rebeschini
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs et al.
Hidden Cost of Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Lily Weng et al.
Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors
Benjamin Moseley, Sergei Vassilvtiskii, Yuyan Wang
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky et al.
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations
Luhuan Wu, Andrew Miller, Lauren Anderson et al.
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
Kristjan Greenewald, Karthikeyan Shanmugam, Dmitriy Katz
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard