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Finale Doshi-Velez

Identifiers

  • name variant Finale Doshi-Velez 0.60 · backfill

Papers (49)

  1. Quantifying Potential Observation Missingness in Inverse Reinforcement Learning cs.LG · 2026 · author #4
  2. Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data cs.AI · 2026 · author #3
  3. A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data cs.MA · 2026 · author #4
  4. Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories cs.LG · 2024 · author #4
  5. Quality of Uncertainty Quantification for Bayesian Neural Network Inference cs.LG · 2019 · author #4
  6. Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies cs.LG · 2019 · author #2
  7. Exploring Computational User Models for Agent Policy Summarization cs.LG · 2019 · author #3
  8. Defining Admissible Rewards for High Confidence Policy Evaluation cs.LG · 2019 · author #3
  9. A general method for regularizing tensor decomposition methods via pseudo-data stat.ML · 2019 · author #3
  10. Output-Constrained Bayesian Neural Networks cs.LG · 2019 · author #8
  11. Truly Batch Apprenticeship Learning with Deep Successor Features cs.LG · 2019 · author #3
  12. Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning cs.LG · 2019 · author #9
  13. Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights cs.LG · 2018 · author #5
  14. Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction cs.CL · 2018 · author #2
  15. Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters cs.LG · 2018 · author #6
  16. Learning Qualitatively Diverse and Interpretable Rules for Classification cs.LG · 2018 · author #3
  17. Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors stat.ML · 2018 · author #3
  18. Evaluating Reinforcement Learning Algorithms in Observational Health Settings cs.LG · 2018 · author #20
  19. Human-in-the-Loop Interpretability Prior stat.ML · 2018 · author #5
  20. Representation Balancing MDPs for Off-Policy Policy Evaluation cs.LG · 2018 · author #6
  21. A particle-based variational approach to Bayesian Non-negative Matrix Factorization stat.ML · 2018 · author #2
  22. Unsupervised Grammar Induction with Depth-bounded PCFG cs.CL · 2018 · author #2
  23. How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation cs.AI · 2018 · author #6
  24. Prediction-Constrained Topic Models for Antidepressant Recommendation cs.LG · 2017 · author #7
  25. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients cs.LG · 2017 · author #2
  26. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability stat.ML · 2017 · author #6
  27. Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning stat.ML · 2017 · author #3
  28. Weighted Tensor Decomposition for Learning Latent Variables with Partial Data stat.ML · 2017 · author #3
  29. Roll-back Hamiltonian Monte Carlo stat.ML · 2017 · author #2
  30. Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models stat.ML · 2017 · author #7
  31. Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables stat.ML · 2017 · author #3
  32. Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes stat.ML · 2017 · author #4
  33. Model Selection in Bayesian Neural Networks via Horseshoe Priors stat.ML · 2017 · author #2
  34. Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations cs.LG · 2017 · author #3
  35. Towards A Rigorous Science of Interpretable Machine Learning stat.ML · 2017 · author #1
  36. Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling cs.CL · 2017 · author #2
  37. Supervised topic models for clinical interpretability stat.ML · 2016 · author #5
  38. Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes stat.ML · 2016 · author #3
  39. Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models stat.ML · 2016 · author #2
  40. Rapid Posterior Exploration in Bayesian Non-negative Matrix Factorization stat.ML · 2016 · author #2
  41. An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization cs.LG · 2016 · author #3
  42. Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models stat.ML · 2016 · author #2
  43. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks stat.ML · 2016 · author #3
  44. Spectral M-estimation with Applications to Hidden Markov Models stat.CO · 2016 · author #3
  45. Restricted Indian Buffet Processes stat.ME · 2015 · author #1
  46. Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems cs.LG · 2015 · author #3
  47. Graph-Sparse LDA: A Topic Model with Structured Sparsity stat.ML · 2014 · author #1
  48. Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations cs.LG · 2013 · author #1
  49. Correlated Non-Parametric Latent Feature Models cs.LG · 2012 · author #1

Mentions

  • 1205.2650 #1 · backfill · confidence 0.70 Finale Doshi-Velez

Frequent Coauthors