Search Results for author:
Found 27 papers, 6 papers with code
Structured Evaluation of Synthetic Tabular Data
Many metrics exist for evaluating the quality of synthetic tabular data; however, we lack an objective, coherent interpretation of the many metrics.
Coupled Variational Autoencoder
Variational auto-encoders are powerful probabilistic models in generative tasks but suffer from generating low-quality samples which are caused by the holes in the prior.
Evolution of beliefs in social networks
Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission).
A Psychological Theory of Explainability
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 generated explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel
With Bochner's theorem, DGP with squared exponential kernel can be viewed as a deep trigonometric network consisting of the random feature layers, sine and cosine activation units, and random weight layers.
Discrete Probabilistic Inverse Optimal Transport
Optimal transport (OT) formalizes the problem of finding an optimal coupling between probability measures given a cost matrix.
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning.
Explainable AI for Natural Adversarial Images
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set.
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Limited expert time is a key bottleneck in medical imaging.
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee.
Efficient Discretizations of Optimal Transport
Obtaining solutions to Optimal Transportation (OT) problems is typically intractable when the marginal spaces are continuous.
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
We consider constrained policy optimization in Reinforcement Learning, where the constraints are in form of marginals on state visitations and global action executions.
Interactive Learning from Activity Description
We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities.
General Reinforcement Learning
Grounded language learning
+2
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Learning a manifold from a teacher’s demonstrations
We consider the problem of learning a manifold from a teacher's demonstration.
Sequential Cooperative Bayesian Inference
We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data.
Conditional Deep Gaussian Processes: multi-fidelity kernel learning
Recently, [1] pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations.
Learning a manifold from a teacher's demonstrations
We consider the problem of learning a manifold from a teacher's demonstration.
A mathematical theory of cooperative communication
Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction.
Interpretable deep Gaussian processes with moments
We propose interpretable DGP based on approximating DGP as a GP by calculating the exact moments, which additionally identify the heavy-tailed nature of some DGP distributions.
Generalizing the theory of cooperative inference
Cooperation information sharing is important to theories of human learning and has potential implications for machine learning.
Standing Wave Decomposition Gaussian Process
We propose a Standing Wave Decomposition (SWD) approximation to Gaussian Process regression (GP).
Optimal Cooperative Inference
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
In this paper, we present a preliminary theoretical model and analysis of the mutual interaction between humans and algorithms, based on an iterated learning framework that is inspired from the study of human language evolution.
Infant directed speech is consistent with teaching
We qualitatively compare the simulated teaching data with human IDS, finding that the teaching data exhibit many features of IDS, including some that have been taken as evidence IDS is not for teaching.
Toward a general, scaleable framework for Bayesian teaching with applications to topic models
We propose an approach based on human teaching where the problem is formalized as selecting a small subset of the data that will, with high probability, lead the human user to the correct inference.
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view.
