Paul Mineiro

According to our database, Paul Mineiro authored at least 49 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning.

Yihe Deng, Paul Mineiro

CoRR, 2024

Active, anytime-valid risk controlling prediction sets.

Ziyu Xu, Nikos Karampatziakis, Paul Mineiro

CoRR, 2024

Online Joint Fine-tuning of Multi-Agent Flows.

Paul Mineiro

CoRR, 2024

Provably Efficient Interactive-Grounded Learning with Personalized Reward.

Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro

CoRR, 2024

Aligning LLM Agents by Learning Latent Preference from User Edits.

Ge Gao, Alexey Taymanov, Eduardo Salinas, Paul Mineiro, Dipendra Misra

CoRR, 2024

Efficient Contextual Bandits with Uninformed Feedback Graphs.

Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Graph Feedback via Reduction to Regression.

Paul Mineiro

CoRR, 2023

Practical Contextual Bandits with Feedback Graphs.

Mengxiao Zhang, Yuheng Zhang, Olga Vrousgou, Haipeng Luo, Paul Mineiro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Time-uniform confidence bands for the CDF under nonstationarity.

Paul Mineiro, Steven R. Howard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Infinite Action Contextual Bandits with Reusable Data Exhaust.

Mark Rucker, Yinglun Zhu, Paul Mineiro

Proceedings of the International Conference on Machine Learning, 2023

Personalized Reward Learning with Interaction-Grounded Learning (IGL).

Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Towards Data-Driven Offline Simulations for Online Reinforcement Learning.

Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman

CoRR, 2022

Eigen Memory Trees.

Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad

CoRR, 2022

Conditionally Risk-Averse Contextual Bandits.

Mónika Farsang, Paul Mineiro, Wangda Zhang

CoRR, 2022

A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean.

Paul Mineiro

CoRR, 2022

Anytime-valid off-policy inference for contextual bandits.

Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, Paul Mineiro

CoRR, 2022

Deploying a Steered Query Optimizer in Production at Microsoft.

Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc T. Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Interaction-Grounded Learning for Recommender Systems.

Jessica Maghakian, Kishan Panaganti, Paul Mineiro, Akanksha Saran, Cheng Tan

Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-located with the 16th ACM Conference on Recommender Systems, 2022

Interaction-Grounded Learning with Action-Inclusive Feedback.

Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces.

Yinglun Zhu, Paul Mineiro

Proceedings of the International Conference on Machine Learning, 2022

Contextual Bandits with Large Action Spaces: Made Practical.

Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro

Proceedings of the International Conference on Machine Learning, 2022

2021

Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL.

Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan

CoRR, 2021

Bellman-consistent Pessimism for Offline Reinforcement Learning.

Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Interaction-Grounded Learning.

Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad

Proceedings of the 38th International Conference on Machine Learning, 2021

ChaCha for Online AutoML.

Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi

Proceedings of the 38th International Conference on Machine Learning, 2021

Off-Policy Confidence Sequences.

Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Empirical Likelihood for Contextual Bandits.

Nikos Karampatziakis, John Langford, Paul Mineiro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Lessons from Real-World Reinforcement Learning in a Customer Support Bot.

Nikos Karampatziakis, Sebastian Kochman, Jade Huang, Paul Mineiro, Kathy Osborne, Weizhu Chen

CoRR, 2019

Contextual Memory Trees.

Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro

Proceedings of the 36th International Conference on Machine Learning, 2019

2017

Logarithmic Time One-Against-Some.

Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Learning Reductions That Really Work.

Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro

Proc. IEEE, 2016

Active Information Acquisition.

He He, Paul Mineiro, Nikos Karampatziakis

CoRR, 2016

2015

A Hierarchical Spectral Method for Extreme Classification.

Paul Mineiro, Nikos Karampatziakis

CoRR, 2015

Fast Label Embeddings for Extremely Large Output Spaces.

Paul Mineiro, Nikos Karampatziakis

Proceedings of the 3rd International Conference on Learning Representations, 2015

Multilabel Prediction via Calibration.

Nikos Karampatziakis, Paul Mineiro

CoRR, 2015

Fast Label Embeddings via Randomized Linear Algebra.

Paul Mineiro, Nikos Karampatziakis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

2014

A Randomized Algorithm for CCA.

Paul Mineiro, Nikos Karampatziakis

CoRR, 2014

Discriminative Features via Generalized Eigenvectors.

Nikos Karampatziakis, Paul Mineiro

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Combining Structured and Unstructured Randomness in Large Scale PCA.

Nikos Karampatziakis, Paul Mineiro

CoRR, 2013

Normalized Online Learning.

Stéphane Ross, Paul Mineiro, John Langford

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Loss-Proportional Subsampling for Subsequent ERM.

Paul Mineiro, Nikos Karampatziakis

Proceedings of the 30th International Conference on Machine Learning, 2013

Machine learning on Big Data.

Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer

Proceedings of the 29th IEEE International Conference on Data Engineering, 2013

2002

A Monte Carlo EM Approach for Partially Observable Diffusion Processes: Theory and Applications to Neural Networks.

Javier R. Movellan, Paul Mineiro, Ruth J. Williams

Neural Comput., 2002

2000

Partially Observable SDE Models for Image Sequence Recognition Tasks.

Javier R. Movellan, Paul Mineiro, Ruth J. Williams

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

A diffusion network approach to visual speech recognition.

Javier R. Movellan, Paul Mineiro

Proceedings of the Auditory-Visual Speech Processing, 1999

1998

Analysis of Direction Selectivity Arising From Recurrent Cortical Interactions.

Paul Mineiro, David Zipser

Neural Comput., 1998

Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition.

Javier R. Movellan, Paul Mineiro

Mach. Learn., 1998

1997

Bayesian Robustification for Audio Visual Fusion.

Javier R. Movellan, Paul Mineiro

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Learning Path Distributions Using Nonequilibrium Diffusion Networks.

Paul Mineiro, Javier R. Movellan, Ruth J. Williams

Proceedings of the Advances in Neural Information Processing Systems 10, 1997