arXiv Statistics @arxiv_stats@qoto.org

Revisiting Model Stitching to Compare Neural Representations. (arXiv:2106.07682v1 [cs.LG]) http://arxiv.org/abs/2106.07682

Adaptive normalization for IPW estimation. (arXiv:2106.07695v1 [stat.ME]) http://arxiv.org/abs/2106.07695

Robust Inference for High-Dimensional Linear Models via Residual Randomization. (arXiv:2106.07717v1 [stat.ME]) http://arxiv.org/abs/2106.07717

An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks. (arXiv:2106.07724v1 [cs.LG]) http://arxiv.org/abs/2106.07724

Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality. (arXiv:2106.07725v1 [math.ST]) http://arxiv.org/abs/2106.07725

Counterfactual Explanations as Interventions in Latent Space. (arXiv:2106.07754v1 [cs.AI]) http://arxiv.org/abs/2106.07754

Linear-Time Probabilistic Solutions of Boundary Value Problems. (arXiv:2106.07761v1 [stat.ML]) http://arxiv.org/abs/2106.07761

Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. (arXiv:2106.07767v1 [cs.LG]) http://arxiv.org/abs/2106.07767

The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization. (arXiv:2106.07769v1 [cs.LG]) http://arxiv.org/abs/2106.07769

Bootstrapping Clustered Data in R using lmeresampler. (arXiv:2106.06568v1 [stat.ME]) http://arxiv.org/abs/2106.06568

Understanding Deflation Process in Over-parametrized Tensor Decomposition. (arXiv:2106.06573v1 [stat.ML]) http://arxiv.org/abs/2106.06573

Analysis of historical data leveraging the sandpile model of self-organized criticality demonstrates the efficacy of prescribed burns in reducing risk of destructive wildfires. (arXiv:2106.06591v1 [stat.AP]) http://arxiv.org/abs/2106.06591

On an Asymptotic Distribution for the MLE. (arXiv:2106.06597v1 [math.ST]) http://arxiv.org/abs/2106.06597

Inference for treatment-specific survival curves using machine learning. (arXiv:2106.06602v1 [stat.ME]) http://arxiv.org/abs/2106.06602

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. (arXiv:2106.06607v1 [cs.LG]) http://arxiv.org/abs/2106.06607

Statistical Analysis from the Fourier Integral Theorem. (arXiv:2106.06608v1 [stat.ME]) http://arxiv.org/abs/2106.06608

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics. (arXiv:2106.06610v1 [cs.LG]) http://arxiv.org/abs/2106.06610

Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. (arXiv:2106.06669v1 [stat.ME]) http://arxiv.org/abs/2106.06669

Examining Passenger Vehicle Miles Traveled and Carbon Emissions in the Boston Metropolitan Area. (arXiv:2106.06677v1 [cs.CY]) http://arxiv.org/abs/2106.06677

Scaling Vision with Sparse Mixture of Experts. (arXiv:2106.05974v1 [cs.CV]) http://arxiv.org/abs/2106.05974