Papers
4,025 papers found
Auditing Fairness under Unobserved Confounding
Yewon Byun, Dylan Sam, Michael Oberst et al.
A Unified Framework for Discovering Discrete Symmetries
Pavan Karjol, Rohan Kashyap, Aditya Gopalan et al.
A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas C. Cosier, Rares Iordan, Sicelukwanda N. T. Zwane et al.
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed et al.
Autoregressive Bandits
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran et al.
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
Zhongliang Guo, Weiye Li, Yifei Qian et al.
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models
Yun-Peng Li, Hans-Andrea Loeliger
Bandit Pareto Set Identification: the Fixed Budget Setting
Cyrille Kone, Emilie Kaufmann, Laura Richert
Bayesian Online Learning for Consensus Prediction
Samuel Showalter, Alex J Boyd, Padhraic Smyth et al.
Bayesian Semi-structured Subspace Inference
Daniel Dold, David Ruegamer, Beate Sick et al.
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan M Hussain et al.
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds
Alexandra Maria Hotti, Lennart Alexander Van der Goten, Jens Lagergren
Best Arm Identification with Resource Constraints
Zitian Li, Wang Chi Cheung
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya et al.
Better Batch for Deep Probabilistic Time Series Forecasting
Zhihao Zheng, Seongjin Choi, Lijun Sun
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing, Xiaofeng Lin, Qifan Song et al.
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
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