Search Results for author:
Found 13 papers, 3 papers with code
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
In this paper, focusing specifically on compositions of functions arising from the different pillars, we aim to reduce this gap, develop new insights for trustworthy ML, and answer questions such as the following.
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data
In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets
To perform target-aware design of novel inhibitor molecules, a protein sequence-conditioned sampling on the generative foundation model is performed.
An Empirical Study of Modular Bias Mitigators and Ensembles
A popular approach to train more stable models is ensemble learning.
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions) when exposed to light.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Fairness GAN
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making.
