3.2k citations · 5.1k across the 137 of their papers we have counts for
86 papers · 1 filter
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
Leo Benac, Abhishek Sharma, Alihan Huyuk +1
Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants o…
Federated ADMM from Bayesian Duality
Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez +1
We propose a new Bayesian approach to generalize the federated Alternating Direction Method of Multipliers (ADMM). We show that the solutions of variational-Bayesian (VB) objective…
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning
Alihan Hüyük, Finale Doshi-Velez
Long-term planning, as in reinforcement learning (RL), involves finding strategies: actions that collectively work toward a goal rather than individually optimizing their immediate…
Connecting Federated ADMM to Bayes
Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez
We provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complem…
Diverse Concept Proposals for Concept Bottleneck Models
Katrina Brown, Marton Havasi, Finale Doshi-Velez
Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number o…
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
Inko Bovenzi, Adi Carmel, Michael Hu +5
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning…
Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories
Leo Benac, Abhishek Sharma, Sonali Parbhoo +1
We consider the problem of estimating the transition dynamics from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a…
Decision-Point Guided Safe Policy Improvement
Abhishek Sharma, Leo Benac, Sonali Parbhoo +1
Within batch reinforcement learning, safe policy improvement (SPI) seeks to ensure that the learnt policy performs at least as well as the behavior policy that generated the datase…
Transparent Trade-offs between Properties of Explanations
Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby +2
When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable propertie…
Inverse Reinforcement Learning with Multiple Planning Horizons
Jiayu Yao, Weiwei Pan, Finale Doshi-Velez +1
In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons.…
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models
Ze Yu Zhang, Arun Verma, Finale Doshi-Velez +1
Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deploym…
Towards Integrating Personal Knowledge into Test-Time Predictions
Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
Machine learning (ML) models can make decisions based on large amounts of data, but they can be missing personal knowledge available to human users about whom predictions are made.…
Guarantee Regions for Local Explanations
Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez
Interpretability methods that utilise local surrogate models (e.g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not…
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani +4
Online reinforcement learning (RL) algorithms offer great potential for personalizing treatment for participants in clinical trials. However, deploying an online, autonomous algori…
Non-Stationary Latent Auto-Regressive Bandits
Anna L. Trella, Walter Dempsey, Asim H. Gazi +3
For the non-stationary multi-armed bandit (MAB) problem, many existing methods allow a general mechanism for the non-stationarity, but rely on a budget for the non-stationarity tha…
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
Yidou Weng, Finale Doshi-Velez
This paper proposes a model learning Semi-parametric relationships in an Expert Bayesian Network (SEBN) with linear parameter and structure constraints. We use Gaussian Processes a…
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Lauren H. Cooke, Harvey Klyne, Edwin Zhang +3
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work m…
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Varshini Subhash, Anna Bialas, Weiwei Pan +1
Transformer based large language models with emergent capabilities are becoming increasingly ubiquitous in society. However, the task of understanding and interpreting their intern…
Bayesian Inverse Transition Learning for Offline Settings
Leo Benac, Sonali Parbhoo, Finale Doshi-Velez
Offline Reinforcement learning is commonly used for sequential decision-making in domains such as healthcare and education, where the rewards are known and the transition dynamics…
On the Effective Horizon of Inverse Reinforcement Learning
Yiqing Xu, Finale Doshi-Velez, David Hsu
Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning, over a given time horizon, to compute an approximately optimal policy fo…
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Sarah Rathnam, Sonali Parbhoo, Weiwei Pan +2
Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to restrict planning to a less complex set of policies when estim…
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Shengpu Tang, Maggie Makar, Michael W. Sjoding +2
Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent fact…
Decision-Focused Model-based Reinforcement Learning for Reward Transfer
Abhishek Sharma, Sonali Parbhoo, Omer Gottesman +1
Model-based reinforcement learning (MBRL) provides a way to learn a transition model of the environment, which can then be used to plan personalized policies for different patient…