research
∙
09/20/2023
Signature Activation: A Sparse Signal View for Holistic Saliency
The adoption of machine learning in healthcare calls for model transpare...
share
research
∙
09/01/2023
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Transformer based large language models with emergent capabilities are b...
research
∙
08/09/2023
Bayesian Inverse Transition Learning for Offline Settings
Offline Reinforcement learning is commonly used for sequential decision-...
research
∙
07/28/2023
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text
A common way to explore text corpora is through low-dimensional projecti...
research
∙
07/13/2023
On the Effective Horizon of Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) algorithms often rely on (forward) ...
research
∙
06/22/2023
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
There is increasing attention being given to how to regulate AI systems....
research
∙
06/20/2023
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Discount regularization, using a shorter planning horizon when calculati...
research
∙
06/12/2023
Adaptive interventions for both accuracy and time in AI-assisted human decision making
In settings where users are both time-pressured and need high accuracy, ...
research
∙
05/02/2023
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Many reinforcement learning (RL) applications have combinatorial action ...
research
∙
04/06/2023
Robust Decision-Focused Learning for Reward Transfer
Decision-focused (DF) model-based reinforcement learning has recently be...
research
∙
12/01/2022
Modeling Mobile Health Users as Reinforcement Learning Agents
Mobile health (mHealth) technologies empower patients to adopt/maintain ...
research
∙
11/16/2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Comparing Bayesian neural networks (BNNs) with different widths is chall...
research
∙
11/14/2022
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?
Global explanations of a reinforcement learning (RL) agent's expected be...
research
∙
11/10/2022
Does the explanation satisfy your needs?: A unified view of properties of explanations
Interpretability provides a means for humans to verify aspects of machin...
research
∙
10/27/2022
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
In September 2021, the "One Hundred Year Study on Artificial Intelligenc...
research
∙
09/18/2022
Towards Robust Off-Policy Evaluation via Human Inputs
Off-policy Evaluation (OPE) methods are crucial tools for evaluating pol...
research
∙
08/15/2022
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Dental disease is one of the most common chronic diseases despite being ...
research
∙
08/02/2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
For responsible decision making in safety-critical settings, machine lea...
research
∙
07/30/2022
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes
In the reinforcement learning literature, there are many algorithms deve...
research
∙
07/13/2022
Policy Optimization with Sparse Global Contrastive Explanations
We develop a Reinforcement Learning (RL) framework for improving an exis...
research
∙
06/22/2022
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Recent years have seen a surge of interest in the field of explainable A...
research
∙
06/08/2022
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to p...
research
∙
05/11/2022
"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment
Many researchers and policymakers have expressed excitement about how al...
research
∙
04/07/2022
A Joint Learning Approach for Semi-supervised Neural Topic Modeling
Topic models are some of the most popular ways to represent textual data...
research
∙
02/23/2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
research
∙
01/20/2022
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making
Assessing the effects of a policy based on observational data from a dif...
research
∙
11/28/2021
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation
Off-policy policy evaluation methods for sequential decision making can ...
research
∙
10/25/2021
On Learning Prediction-Focused Mixtures
Probabilistic models help us encode latent structures that both model th...
research
∙
09/22/2021
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
Machine learning models that utilize patient data across time (rather th...
research
∙
09/16/2021
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
In batch reinforcement learning, there can be poorly explored state-acti...
research
∙
09/13/2021
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty
We propose SLTD (`Sequential Learning-to-Defer') a framework for learnin...
research
∙
09/13/2021
State Relevance for Off-Policy Evaluation
Importance sampling-based estimators for off-policy evaluation (OPE) are...
research
∙
07/21/2021
Online structural kernel selection for mobile health
Motivated by the need for efficient and personalized learning in mobile ...
research
∙
06/24/2021
Promises and Pitfalls of Black-Box Concept Learning Models
Machine learning models that incorporate concept learning as an intermed...
research
∙
06/13/2021
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
Variational inference enables approximate posterior inference of the hig...
research
∙
06/06/2021
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a pred...
research
∙
03/29/2021
Learning Under Adversarial and Interventional Shifts
Machine learning models are often trained on data from one distribution ...
research
∙
02/09/2021
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement
In representation learning, there has been recent interest in developing...
research
∙
02/02/2021
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
For machine learning models to be most useful in numerous sociotechnical...
research
∙
02/01/2021
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
Major depressive disorder is a debilitating disease affecting 264 millio...
research
∙
01/13/2021
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
We propose Preferential MoE, a novel human-ML mixture-of-experts model t...
research
∙
01/09/2021
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Many batch RL health applications first discretize time into fixed inter...
research
∙
12/10/2020
Artificial Intelligence Cooperation
The rise of Artificial Intelligence (AI) will bring with it an ever-incr...
research
∙
12/04/2020
Learning Interpretable Concept-Based Models with Human Feedback
Machine learning models that first learn a representation of a domain in...
research
∙
10/21/2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Domains where supervised models are deployed often come with task-specif...
research
∙
07/14/2020
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Variational Auto-encoders (VAEs) are deep generative latent variable mod...
research
∙
07/12/2020
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Traditional training of deep classifiers yields overconfident models tha...
research
∙
06/29/2020
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
We present two elegant solutions for modeling continuous-time dynamics, ...
research
∙
06/21/2020
Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks
Neural Linear Models (NLM) are deep models that produce predictive uncer...
research
∙
06/11/2020
