Finale Doshi-Velez

Orcid: 0000-0003-2886-3898

Affiliations:
  • Harvard University, USA
  • MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA (former)


According to our database1, Finale Doshi-Velez authored at least 192 papers between 2007 and 2024.

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Bibliography

2024
Directions of Technical Innovation for Regulatable AI Systems.
Commun. ACM, November, 2024

Task-Relevant Feature Selection with Prediction Focused Mixture Models.
Trans. Mach. Learn. Res., 2024

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning.
J. Mach. Learn. Res., 2024

Directly Optimizing Explanations for Desired Properties.
CoRR, 2024

Decision-Point Guided Safe Policy Improvement.
CoRR, 2024

Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills.
CoRR, 2024

Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation.
CoRR, 2024

A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.
CoRR, 2024

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models.
CoRR, 2024

Towards Integrating Personal Knowledge into Test-Time Predictions.
CoRR, 2024

A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning.
CoRR, 2024

AI Procurement Checklists: Revisiting Implementation in the Age of AI Governance.
CoRR, 2024

Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders.
CoRR, 2024

Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials.
CoRR, 2024

Guarantee Regions for Local Explanations.
CoRR, 2024

Non-Stationary Latent Auto-Regressive Bandits.
CoRR, 2024

Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors.
CoRR, 2024

Inverse Reinforcement Learning with Multiple Planning Horizons.
RLJ, 2024

Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure.
Proceedings of the 29th International Conference on Intelligent User Interfaces, 2024

XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping.
Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks.
Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023
Online model selection by learning how compositional kernels evolve.
Trans. Mach. Learn. Res., 2023

Learning-to-defer for sequential medical decision-making under uncertainty.
Trans. Mach. Learn. Res., 2023

Signature Activation: A Sparse Signal View for Holistic Saliency.
CoRR, 2023

Why do universal adversarial attacks work on large language models?: Geometry might be the answer.
CoRR, 2023

Bayesian Inverse Transition Learning for Offline Settings.
CoRR, 2023

SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text.
CoRR, 2023

Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning.
CoRR, 2023

On the Effective Horizon of Inverse Reinforcement Learning.
CoRR, 2023

Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities.
CoRR, 2023

Adaptive interventions for both accuracy and time in AI-assisted human decision making.
CoRR, 2023

Robust Decision-Focused Learning for Reward Transfer.
CoRR, 2023

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2023

Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Travel-time prediction using neural-network-based mixture models.
Proceedings of the 14th International Conference on Ambient Systems, 2023

Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
An interpretable RL framework for pre-deployment modeling in ICU hypotension management.
npj Digit. Medicine, 2022

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables.
J. Mach. Learn. Res., 2022

Modeling Mobile Health Users as Reinforcement Learning Agents.
CoRR, 2022

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks.
CoRR, 2022

(When) Are Contrastive Explanations of Reinforcement Learning Helpful?
CoRR, 2022

Does the explanation satisfy your needs?: A unified view of properties of explanations.
CoRR, 2022

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report.
CoRR, 2022

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry.
CoRR, 2022

A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes.
CoRR, 2022

Policy Optimization with Sparse Global Contrastive Explanations.
CoRR, 2022

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making.
CoRR, 2022

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.
Algorithms, 2022

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Addressing Leakage in Concept Bottleneck Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models.
Proceedings of the Machine Learning for Healthcare Conference, 2022

"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment.
Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI.
Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Wide Mean-Field Bayesian Neural Networks Ignore the Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Robust Off-Policy Evaluation via Human Inputs.
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

A Joint Learning Approach for Semi-supervised Neural Topic Modeling.
Proceedings of the Sixth Workshop on Structured Prediction for NLP, 2022

2021
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.
J. Artif. Intell. Res., 2021

On Learning Prediction-Focused Mixtures.
CoRR, 2021

Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning.
CoRR, 2021

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty.
CoRR, 2021

Online structural kernel selection for mobile health.
CoRR, 2021

Promises and Pitfalls of Black-Box Concept Learning Models.
CoRR, 2021

Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data.
CoRR, 2021

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning.
CoRR, 2021

Learning Under Adversarial and Interventional Shifts.
CoRR, 2021

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible.
CoRR, 2021

Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment.
CoRR, 2021

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition.
Comput. Linguistics, 2021

Machine Learning Techniques for Accountability.
AI Mag., 2021

Interactive Cohort Analysis and Hypothesis Discovery by Exploring Temporal Patterns in Population-Level Health Records.
Proceedings of the IEEE Workshop on Visual Analytics in Healthcare, 2021

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Power Constrained Bandits.
Proceedings of the Machine Learning for Healthcare Conference, 2021

State Relevance for Off-Policy Evaluation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement.
Proceedings of the 38th International Conference on Machine Learning, 2021

Evaluating the Interpretability of Generative Models by Interactive Reconstruction.
Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens.
Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Learning Predictive and Interpretable Timeseries Summaries from ICU Data.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020
Artificial Intelligence & Cooperation.
CoRR, 2020

Learning Interpretable Concept-Based Models with Human Feedback.
CoRR, 2020

Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks.
CoRR, 2020

BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty.
CoRR, 2020

Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks.
CoRR, 2020

PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes.
CoRR, 2020

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment.
CoRR, 2020

Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning.
CoRR, 2020

PoRB-Nets: Poisson Process Radial Basis Function Networks.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Incorporating Interpretable Output Constraints in Bayesian Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Active Screening on Recurrent Diseases Contact Networks with Uncertainty: A Reinforcement Learning Approach.
Proceedings of the Multi-Agent-Based Simulation XXI - 21st International Workshop, 2020

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions.
Proceedings of the 37th International Conference on Machine Learning, 2020

Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning.
Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.
Proceedings of the AMIA 2020, 2020

Prediction Focused Topic Models via Feature Selection.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

POPCORN: Partially Observed Prediction Constrained Reinforcement Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Regional Tree Regularization for Interpretability in Deep Neural Networks.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Ensembles of Locally Independent Prediction Models.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Assessing topic model relevance: Evaluation and informative priors.
Stat. Anal. Data Min., 2019

A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization.
J. Mach. Learn. Res., 2019

Model Selection in Bayesian Neural Networks via Horseshoe Priors.
J. Mach. Learn. Res., 2019

Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks.
CoRR, 2019

Prediction Focused Topic Models for Electronic Health Records.
CoRR, 2019

Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem.
CoRR, 2019

Prediction Focused Topic Models via Vocab Selection.
CoRR, 2019

Regional Tree Regularization for Interpretability in Black Box Models.
CoRR, 2019

Quality of Uncertainty Quantification for Bayesian Neural Network Inference.
CoRR, 2019

Defining Admissible Rewards for High Confidence Policy Evaluation.
CoRR, 2019

A general method for regularizing tensor decomposition methods via pseudo-data.
CoRR, 2019

Output-Constrained Bayesian Neural Networks.
CoRR, 2019

An Evaluation of the Human-Interpretability of Explanation.
CoRR, 2019

Summarizing agent strategies.
Auton. Agents Multi Agent Syst., 2019

Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Truly Batch Apprenticeship Learning with Deep Successor Features.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Exploring Computational User Models for Agent Policy Summarization.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Combining parametric and nonparametric models for off-policy evaluation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Human Evaluation of Models Built for Interpretability.
Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing, 2019

Toward Robust Policy Summarization.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Unsupervised Learning of PCFGs with Normalizing Flow.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models.
IEEE Trans. Vis. Comput. Graph., 2018

Unsupervised Grammar Induction with Depth-bounded PCFG.
Trans. Assoc. Comput. Linguistics, 2018

Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights.
CoRR, 2018

Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters.
CoRR, 2018

Learning Qualitatively Diverse and Interpretable Rules for Classification.
CoRR, 2018

Evaluating Reinforcement Learning Algorithms in Observational Health Settings.
CoRR, 2018

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation.
CoRR, 2018

Representation Balancing MDPs for Off-policy Policy Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Human-in-the-Loop Interpretability Prior.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors.
Proceedings of the 35th International Conference on Machine Learning, 2018

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Agent Strategy Summarization.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.
Proceedings of the AMIA 2018, 2018

Semi-Supervised Prediction-Constrained Topic Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Weighted Tensor Decomposition for Learning Latent Variables with Partial Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Restricted Indian buffet processes.
Stat. Comput., 2017

A Bayesian Framework for Learning Rule Sets for Interpretable Classification.
J. Mach. Learn. Res., 2017

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.
J. Am. Medical Informatics Assoc., 2017

Prediction-Constrained Topic Models for Antidepressant Recommendation.
CoRR, 2017

Accountability of AI Under the Law: The Role of Explanation.
CoRR, 2017

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.
CoRR, 2017

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models.
CoRR, 2017

Promoting Domain-Specific Terms in Topic Models with Informative Priors.
CoRR, 2017

A Roadmap for a Rigorous Science of Interpretability.
CoRR, 2017

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

Combining Kernel and Model Based Learning for HIV Therapy Selection.
Proceedings of the Summit on Clinical Research Informatics, 2017

Predicting intervention onset in the ICU with switching state space models.
Proceedings of the Summit on Clinical Research Informatics, 2017

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders.
J. Mach. Learn. Res., 2016

A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations.
CoRR, 2016

An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization.
CoRR, 2016

Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models.
CoRR, 2016

Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes.
CoRR, 2016

Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Bayesian Rule Sets for Interpretable Classification.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input.
Proceedings of the COLING 2016, 2016

Spectral M-estimation with Applications to Hidden Markov Models.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems.
CoRR, 2015

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Graph-Sparse LDA: A Topic Model with Structured Sparsity.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Unfolding physiological state: mortality modelling in intensive care units.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Hidden Parameter Markov Decision Processes: An Emerging Paradigm for Modeling Families of Related Tasks.
Proceedings of the 2014 AAAI Fall Symposia, Arlington, Virginia, USA, November 13-15, 2014, 2014

Preface.
Proceedings of the Modern Artificial Intelligence for Health Analytics, 2014

2012
Bayesian nonparametric methods for reinforcement learning in partially observable domains.
PhD thesis, 2012

Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs.
Artif. Intell., 2012

A Bayesian nonparametric approach to modeling battery health.
Proceedings of the IEEE International Conference on Robotics and Automation, 2012

2011
A Bayesian nonparametric approach to modeling motion patterns.
Auton. Robots, 2011

Reports of the AAAI 2011 Spring Symposia.
AI Mag., 2011

Online Discovery of Feature Dependencies.
Proceedings of the 28th International Conference on Machine Learning, 2011

Infinite Dynamic Bayesian Networks.
Proceedings of the 28th International Conference on Machine Learning, 2011

A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains.
Proceedings of the 33th Annual Meeting of the Cognitive Science Society, 2011

Organizing Committee.
Proceedings of the Computational Physiology, 2011

2010
Nonparametric Bayesian Policy Priors for Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

A Bayesian Nonparametric Approach to Modeling Mobility Patterns.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009
Variational Inference for the Indian Buffet Process.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Correlated Non-Parametric Latent Feature Models.
Proceedings of the UAI 2009, 2009

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

The Infinite Partially Observable Markov Decision Process.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Accelerated sampling for the Indian Buffet Process.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Spoken language interaction with model uncertainty: an adaptive human-robot interaction system.
Connect. Sci., 2008

The permutable POMDP: fast solutions to POMDPs for preference elicitation.
Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008

2007
Collision detection in legged locomotion using supervised learning.
Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29, 2007

Efficient Model Learning for Dialog Management.
Proceedings of the Multidisciplinary Collaboration for Socially Assistive Robotics, 2007


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