Show newer

Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence. (arXiv:2206.04110v1 [stat.ME]) arxiv.org/abs/2206.04110

Deep Hierarchical Planning from Pixels. (arXiv:2206.04114v1 [cs.AI]) arxiv.org/abs/2206.04114

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. (arXiv:2206.04119v1 [q-bio.BM]) arxiv.org/abs/2206.04119

ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. (arXiv:2206.04122v1 [cs.GT]) arxiv.org/abs/2206.04122

Bayesian multivariate logistic regression for superiority and inferiority decision-making under treatment heterogeneity. (arXiv:2206.04133v1 [stat.ME]) arxiv.org/abs/2206.04133

Inference for Matched Tuples and Fully Blocked Factorial Designs. (arXiv:2206.04157v1 [econ.EM]) arxiv.org/abs/2206.04157

On Gradient Descent Convergence beyond the Edge of Stability. (arXiv:2206.04172v1 [cs.LG]) arxiv.org/abs/2206.04172

How does overparametrization affect performance on minority groups?. (arXiv:2206.03515v1 [cs.LG]) arxiv.org/abs/2206.03515

Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. (arXiv:2206.03520v1 [stat.ML]) arxiv.org/abs/2206.03520

Confidentiality Protection in the 2020 US Census of Population and Housing. (arXiv:2206.03524v1 [stat.AP]) arxiv.org/abs/2206.03524

Decoupled Self-supervised Learning for Non-Homophilous Graphs. (arXiv:2206.03601v1 [cs.LG]) arxiv.org/abs/2206.03601

FedPop: A Bayesian Approach for Personalised Federated Learning. (arXiv:2206.03611v1 [cs.LG]) arxiv.org/abs/2206.03611

Bayesian additive regression trees for probabilistic programming. (arXiv:2206.03619v1 [stat.CO]) arxiv.org/abs/2206.03619

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. (arXiv:2206.03633v1 [cs.LG]) arxiv.org/abs/2206.03633

Robust self-tuning semiparametric PCA for contaminated elliptical distribution. (arXiv:2206.03662v1 [stat.ME]) arxiv.org/abs/2206.03662

Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. (arXiv:2206.03688v1 [cs.LG]) arxiv.org/abs/2206.03688

Impossibility of Collective Intelligence. (arXiv:2206.02786v1 [cs.LG]) arxiv.org/abs/2206.02786

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. (arXiv:2206.02792v1 [cs.LG]) arxiv.org/abs/2206.02792

RORL: Robust Offline Reinforcement Learning via Conservative Smoothing. (arXiv:2206.02829v1 [cs.LG]) arxiv.org/abs/2206.02829

Show older
Qoto Mastodon

QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.