Papers
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu, Laney Goldman, Valentina Guo et al.
BlockBoost: Scalable and Efficient Blocking through Boosting
Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno et al.
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao, Jun Wu, Jingrui He
Boundary-Aware Uncertainty for Feature Attribution Explainers
Davin Hill, Aria Masoomi, Max Torop et al.
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
Yu Inatsu, Shion Takeno, Hiroyuki Hanada et al.
Breaking isometric ties and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko et al.
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
Nikita Puchkin, Eduard Gorbunov, Nickolay Kutuzov et al.
Bures-Wasserstein Means of Graphs
Isabel Haasler, Pascal Frossard
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab et al.
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening
Florence Regol, Mark Coates
Causal Bandits with General Causal Models and Interventions
Zirui Yan, Dennis Wei, Dmitriy A Katz et al.
Causal Discovery under Off-Target Interventions
Davin Choo, Kirankumar Shiragur, Caroline Uhler
Causally Inspired Regularization Enables Domain General Representations
Olawale Salaudeen, Sanmi Koyejo
Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf
Causal Q-Aggregation for CATE Model Selection
Hui Lan, Vasilis Syrgkanis
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Jie Hu, Vishwaraj Doshi, Do Young Eun
Certified private data release for sparse Lipschitz functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal et al.
Classifier Calibration with ROC-Regularized Isotonic Regression
Eugène Berta, Francis Bach, Michael Jordan
Clustering Items From Adaptively Collected Inconsistent Feedback
Shubham Gupta, Peter W J Staar, Christian de Sainte Marie
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin et al.
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze et al.
Comparing Comparators in Generalization Bounds
Fredrik Hellström, Benjamin Guedj
Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints
Ahmet Alacaoglu, Stephen J Wright
Compression with Exact Error Distribution for Federated Learning
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li et al.