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stat.ML updates on arXiv.org

Adaptive multi-fidelity optimization with fast learning rates Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance Collective Kernel EFT for Pre-activation ResNets PRIM-cipal components analysis One-Shot Generative Flows: Existence and Obstructions Structural interpretability in SVMs with truncated orthogonal polynomial kernels Amortized Optimal Transport from Sliced Potentials MinShap: A Modified Shapley Value Approach for Feature Selection Unsupervised feature selection using Bayesian Tucker decomposition Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits Best of both worlds: Stochastic & adversarial best-arm identification Scalable Model-Based Clustering with Sequential Monte Carlo Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks Gating Enables Curvature: A Geometric Expressivity Gap in Attention Zeroth-Order Optimization at the Edge of Stability Differentially Private Conformal Prediction CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization Generative Augmented Inference Improving Machine Learning Performance with Synthetic Augmentation PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning Path-Sampled Integrated Gradients Heat and Matérn Kernels on Matchings Doubly Outlier-Robust Online Infinite Hidden Markov Model Momentum Further Constrains Sharpness at the Edge of Stochastic Stability Multistage Conditional Compositional Optimization BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Gonogo: An R Implementation of Test Methods to Perform, A...
Paul A. Roediger · 2020-11-23 · via stat.ML updates on arXiv.org

This work provides documentation for a suite of R functions contained in gonogo.R. The functions provide sensitivity testing practitioners and researchers with an ability to conduct, analyze and simulate various sensitivity experiments involving binary responses and a single stimulus level (e.g., drug dosage, drop height, velocity, etc.). Included are the modern Neyer and 3pod adaptive procedures, as well as the Bruceton and Langlie. The latter two benchmark procedures are capable of being performed according to generalized up-down transformed-response rules. Each procedure is designated phase-one of a three-phase experiment. The goal of phase-one is to achieve overlapping data. The two additional (and optional) refinement phases utilize the D-optimal criteria and the Robbins-Monro-Joseph procedure. The goals of the two refinement phases are to situate testing in the vicinity of the median and tails of the latent response distribution, respectively.