Papers
Bandit Online Learning with Unknown Delays
Bingcong Li, Tianyi Chen, Georgios B. Giannakis
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
Xiaoyu Li, Francesco Orabona
Active Exploration in Markov Decision Processes
Jean Tarbouriech, Alessandro Lazaric
Conditionally Independent Multiresolution Gaussian Processes
Jalil Taghia, Thomas Schön
Conservative Exploration using Interleaving
Sumeet Katariya, Branislav Kveton, Zheng Wen et al.
Correcting the bias in least squares regression with volume-rescaled sampling
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models
Jiong Zhang, Parameswaran Raman, Shihao Ji et al.
Generalized Boltzmann Machine with Deep Neural Structure
Yingru Liu, Dongliang Xie, Xin Wang
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition
Zhehui Chen, Xingguo Li, Lin Yang et al.
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Tengyuan Liang, James Stokes
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives
Hadrien Hendrikx, Francis Bach, Laurent Massoulie
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin et al.
Attenuating Bias in Word vectors
Sunipa Dev, Jeff Phillips
Deep Neural Networks Learn Non-Smooth Functions Effectively
Masaaki Imaizumi, Kenji Fukumizu
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen, Hiroaki Sasaki, Richard Turner
Optimal Transport for Multi-source Domain Adaptation under Target Shift
Ievgen Redko, Nicolas Courty, Rémi Flamary et al.
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
George Papamakarios, David Sterratt, Iain Murray
On Kernel Derivative Approximation with Random Fourier Features
Zoltan Szabo, Bharath Sriperumbudur
A new evaluation framework for topic modeling algorithms based on synthetic corpora
Hanyu Shi, Martin Gerlach, Isabel Diersen et al.
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization
Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi et al.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak et al.
SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity
Luca Ambrogioni, Patrick Ebel, Max Hinne et al.
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya et al.
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
Gaël Letarte, Emilie Morvant, Pascal Germain
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Juho Lee, Lancelot James, Seungjin Choi et al.