109 citations · 252 across the 16 of their papers we have counts for
16 papers
Structured Evaluation of Synthetic Tabular Data
Scott Cheng-Hsin Yang, Baxter Eaves, Michael Schmidt +2
Tabular data is common yet typically incomplete, small in volume, and access-restricted due to privacy concerns. Synthetic data generation offers potential solutions. Many metrics…
A Psychological Theory of Explainability
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI…
Explainable AI for Natural Adversarial Images
Tomas Folke, ZhaoBin Li, Ravi B. Sojitra +2
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends o…
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson +1
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential fo…
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be unde…
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi B. Sojitra +2
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts…
Interpretable deep Gaussian processes with moments
Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao +1
Deep Gaussian Processes (DGPs) combine the expressiveness of Deep Neural Networks (DNNs) with quantified uncertainty of Gaussian Processes (GPs). Expressive power and intractable i…
Standing Wave Decomposition Gaussian Process
Chi-Ken Lu, Scott Cheng-Hsin Yang, Patrick Shafto
We propose a Standing Wave Decomposition (SWD) approximation to Gaussian Process regression (GP). GP involves a costly matrix inversion operation, which limits applicability to lar…
Discrepancy-Based Algorithms for Non-Stationary Rested Bandits
Corinna Cortes, Giulia DeSalvo, Vitaly Kuznetsov +2
We study the multi-armed bandit problem where the rewards are realizations of general non-stationary stochastic processes, a setting that generalizes many existing lines of work an…
Optimal Cooperative Inference
Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi +3
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasin…
Online Learning with Automata-based Expert Sequences
Mehryar Mohri, Scott Yang
We consider a general framework of online learning with expert advice where regret is defined with respect to sequences of experts accepted by a weighted automaton. Our framework c…
Online Learning with Abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile +2
We present an extensive study of the key problem of online learning where algorithms are allowed to abstain from making predictions. In the adversarial setting, we show how existin…
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov +2
We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are b…
Structured Prediction Theory Based on Factor Graph Complexity
Corinna Cortes, Mehryar Mohri, Vitaly Kuznetsov +1
We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very w…
Accelerating Optimization via Adaptive Prediction
Mehryar Mohri, Scott Yang
We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gr…
How Xenopus laevis embryos replicate reliably: investigating the random-completion problem
Scott Cheng-Hsin Yang, John Bechhoefer
DNA synthesis in \textit{Xenopus} frog embryos initiates stochastically in time at many sites (origins) along the chromosome. Stochastic initiation implies fluctuations in the time…