Papers
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
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
Usaid Awan, Marco Morucci, Vittorio Orlandi et al.
A Locally Adaptive Bayesian Cubature Method
Matthew Fisher, Chris Oates, Catherine Powell et al.
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Avishek Ghosh, Ramchandran Kannan
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case
Maxime Laborde, Adam Oberman
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
Amortized Inference of Variational Bounds for Learning Noisy-OR
Yiming Yan, Melissa Ailem, Fei Sha
A Multiclass Classification Approach to Label Ranking
Robin Vogel, Stéphan Clémen\con
An approximate KLD based experimental design for models with intractable likelihoods
Ziqiao Ao, Jinglai Li
An Asymptotic Rate for the LASSO Loss
Cynthia Rush