Papers
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
Jinming Xu, Ye Tian, Ying Sun et al.
Accelerating Gradient Boosting Machines
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva et al.
Accelerating Smooth Games by Manipulating Spectral Shapes
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas et al.
A Characterization of Mean Squared Error for Estimator with Bagging
Martin Mihelich, Charles Dognin, Yan Shu et al.
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
Foivos Alimisis, Antonio Orvieto, Gary Becigneul et al.
Active Community Detection with Maximal Expected Model Change
Dan Kushnir, Benjamin Mirabelli
Adaptive Discretization for Evaluation of Probabilistic Cost Functions
Christoph Zimmer, Danny Driess, Mona Meister et al.
Adaptive, Distribution-Free Prediction Intervals for Deep Networks
Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb
Adaptive Exploration in Linear Contextual Bandit
Botao Hao, Tor Lattimore, Csaba Szepesvari
Adaptive multi-fidelity optimization with fast learning rates
Côme Fiegel, Victor Gabillon, Michal Valko
Adaptive Online Kernel Sampling for Vertex Classification
Peng Yang, Ping Li
Adaptive Trade-Offs in Off-Policy Learning
Mark Rowland, Will Dabney, Remi Munos
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
Xingchen Ma, Matthew Blaschko
A Deep Generative Model for Fragment-Based Molecule Generation
Marco Podda, Davide Bacciu, Alessio Micheli
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip Amortila, Doina Precup, Prakash Panangaden et al.
A Diversity-aware Model for Majority Vote Ensemble Accuracy
Bob Durrant, Nick Lim
A Double Residual Compression Algorithm for Efficient Distributed Learning
Xiaorui Liu, Yao Li, Jiliang Tang et al.
Adversarial Risk Bounds through Sparsity based Compression
Emilio Balda, Niklas Koep, Arash Behboodi et al.
Adversarial Robustness Guarantees for Classification with Gaussian Processes
Arno Blaas, Andrea Patane, Luca Laurenti et al.
Adversarial Robustness of Flow-Based Generative Models
Phillip Pope, Yogesh Balaji, Soheil Feizi
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option
P Sharoff, Nishant Mehta, Ravi Ganti
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization
Zhize Li, Jian Li
A Framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
Nhan Pham, Lam Nguyen, Dzung Phan et al.
A Linear-time Independence Criterion Based on a Finite Basis Approximation
Longfei Yan, W. Bastiaan Kleijn, Thushara Abhayapala