arXiv Statistics @arxiv_stats@qoto.org

Optimization Landscape of Tucker Decomposition. (arXiv:2006.16297v1 [cs.LG]) http://arxiv.org/abs/2006.16297

Adversarial Learning for Debiasing Knowledge Graph Embeddings. (arXiv:2006.16309v1 [cs.LG]) http://arxiv.org/abs/2006.16309

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising. (arXiv:2006.16312v1 [cs.LG]) http://arxiv.org/abs/2006.16312

Scaling Symbolic Methods using Gradients for Neural Model Explanation. (arXiv:2006.16322v1 [cs.LG]) http://arxiv.org/abs/2006.16322

Inference in Bayesian Additive Vector Autoregressive Tree Models. (arXiv:2006.16333v1 [econ.EM]) http://arxiv.org/abs/2006.16333

Handling Missing Data in Decision Trees: A Probabilistic Approach. (arXiv:2006.16341v1 [cs.LG]) http://arxiv.org/abs/2006.16341

Material Recognition for Automated Progress Monitoring using Deep Learning Methods. (arXiv:2006.16344v1 [cs.CV]) http://arxiv.org/abs/2006.16344

Data integration in high dimension with multiple quantiles. (arXiv:2006.16357v1 [stat.ME]) http://arxiv.org/abs/2006.16357

Penalized regression with multiple loss functions and selection by vote. (arXiv:2006.16361v1 [stat.ME]) http://arxiv.org/abs/2006.16361

Dominate or Delete: Decentralized Competing Bandits with Uniform Valuation. (arXiv:2006.15166v1 [cs.LG]) http://arxiv.org/abs/2006.15166

Deep Involutive Generative Models for Neural MCMC. (arXiv:2006.15167v1 [stat.ML]) http://arxiv.org/abs/2006.15167

Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings. (arXiv:2006.15168v1 [stat.ML]) http://arxiv.org/abs/2006.15168

A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications. (arXiv:2006.15172v1 [cs.LG]) http://arxiv.org/abs/2006.15172

Interpretable Factorization for Neural Network ECG Models. (arXiv:2006.15189v1 [cs.LG]) http://arxiv.org/abs/2006.15189

Is SGD a Bayesian sampler? Well, almost. (arXiv:2006.15191v1 [cs.LG]) http://arxiv.org/abs/2006.15191

Online learning with Corrupted context: Corrupted Contextual Bandits. (arXiv:2006.15194v1 [cs.LG]) http://arxiv.org/abs/2006.15194

DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning. (arXiv:2006.15199v1 [cs.LG]) http://arxiv.org/abs/2006.15199

Likelihood Maximization and Moment Matching in Low SNR Gaussian Mixture Models. (arXiv:2006.15202v1 [math.ST]) http://arxiv.org/abs/2006.15202

Robust Out-of-distribution Detection via Informative Outlier Mining. (arXiv:2006.15207v1 [cs.LG]) http://arxiv.org/abs/2006.15207

ANOVA exemplars for understanding data drift. (arXiv:2006.14621v1 [stat.ME]) http://arxiv.org/abs/2006.14621