Learning Path Distributions Using Nonequilibrium Diffusion Networks. - Paul Mineiro; Javier R. Movellan; Ruth J. Williams (NIPS, 1997)

Bayesian Robustification for Audio Visual Fusion. - Javier R. Movellan; Paul Mineiro (NIPS, 1997)

Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition. - Javier R. Movellan; Paul Mineiro (Mach. Learn., 1998)

Analysis of Direction Selectivity Arising From Recurrent Cortical Interactions. - Paul Mineiro; David Zipser (Neural Comput., 1998)

A diffusion network approach to visual speech recognition. - Javier R. Movellan; Paul Mineiro (AVSP, 1999)

Partially Observable SDE Models for Image Sequence Recognition Tasks. - Javier R. Movellan; Paul Mineiro; Ruth J. Williams (NIPS, 2000)

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)

Machine learning on Big Data. - Tyson Condie; Paul Mineiro; Neoklis Polyzotis; Markus Weimer (ICDE, 2013)

Loss-Proportional Subsampling for Subsequent ERM. - Paul Mineiro; Nikos Karampatziakis (ICML (3), 2013)

Normalized Online Learning. - Stéphane Ross; Paul Mineiro; John Langford 001 (UAI, 2013)

Combining Structured and Unstructured Randomness in Large Scale PCA. - Nikos Karampatziakis; Paul Mineiro (CoRR, 2013)

Discriminative Features via Generalized Eigenvectors. - Nikos Karampatziakis; Paul Mineiro (ICML, 2014)

A Randomized Algorithm for CCA. - Paul Mineiro; Nikos Karampatziakis (CoRR, 2014)

Fast Label Embeddings via Randomized Linear Algebra. - Paul Mineiro; Nikos Karampatziakis (ECML/PKDD (1), 2015)

Multilabel Prediction via Calibration. - Nikos Karampatziakis; Paul Mineiro (CoRR, 2015)

Fast Label Embeddings for Extremely Large Output Spaces. - Paul Mineiro; Nikos Karampatziakis (ICLR (Workshop), 2015)

A Hierarchical Spectral Method for Extreme Classification. - Paul Mineiro; Nikos Karampatziakis (CoRR, 2015)

Active Information Acquisition. - He He 001; Paul Mineiro; Nikos Karampatziakis (CoRR, 2016)

Learning Reductions That Really Work. - Alina Beygelzimer; Hal Daumé III; John Langford 001; Paul Mineiro (Proc. IEEE, 2016)

Logarithmic Time One-Against-Some. - Hal Daumé III; Nikos Karampatziakis; John Langford 001; Paul Mineiro (ICML, 2017)

Contextual Memory Trees. - Wen Sun 002; Alina Beygelzimer; Hal Daumé III; John Langford 001; Paul Mineiro (ICML, 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)

Empirical Likelihood for Contextual Bandits. - Nikos Karampatziakis; John Langford 001; Paul Mineiro (NeurIPS, 2020)

Off-Policy Confidence Sequences. - Nikos Karampatziakis; Paul Mineiro; Aaditya Ramdas (ICML, 2021)

ChaCha for Online AutoML. - Qingyun Wu; Chi Wang 001; John Langford 001; Paul Mineiro; Marco Rossi 009 (ICML, 2021)

Interaction-Grounded Learning. - Tengyang Xie; John Langford 001; Paul Mineiro; Ida Momennejad (ICML, 2021)

Bellman-consistent Pessimism for Offline Reinforcement Learning. - Tengyang Xie; Ching-An Cheng; Nan Jiang 008; Paul Mineiro; Alekh Agarwal (NeurIPS, 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)

Contextual Bandits with Large Action Spaces: Made Practical. - Yinglun Zhu; Dylan J. Foster; John Langford 001; Paul Mineiro (ICML, 2022)

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. - Yinglun Zhu; Paul Mineiro (ICML, 2022)

Interaction-Grounded Learning with Action-Inclusive Feedback. - Tengyang Xie; Akanksha Saran; Dylan J. Foster; Lekan P. Molu; Ida Momennejad; Nan Jiang 008; Paul Mineiro; John Langford 001 (NeurIPS, 2022)

Interaction-Grounded Learning for Recommender Systems. - Jessica Maghakian; Kishan Panaganti; Paul Mineiro; Akanksha Saran; Cheng Tan (ORSUM@RecSys, 2022)

Deploying a Steered Query Optimizer in Production at Microsoft. - Wangda Zhang; Matteo Interlandi; Paul Mineiro; Shi Qiao 001; Nasim Ghazanfari; Karlen Lie; Marc T. Friedman; Rafah Hosn; Hiren Patel; Alekh Jindal (SIGMOD Conference, 2022)

Anytime-valid off-policy inference for contextual bandits. - Ian Waudby-Smith; Lili Wu; Aaditya Ramdas; Nikos Karampatziakis; Paul Mineiro (CoRR, 2022)

A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean. - Paul Mineiro (CoRR, 2022)

Conditionally Risk-Averse Contextual Bandits. - Mónika Farsang; Paul Mineiro; Wangda Zhang (CoRR, 2022)

Eigen Memory Trees. - Mark Rucker; Jordan T. Ash; John Langford 001; Paul Mineiro; Ida Momennejad (CoRR, 2022)

Towards Data-Driven Offline Simulations for Online Reinforcement Learning. - Shengpu Tang; Felipe Vieira Frujeri; Dipendra Misra; Alex Lamb; John Langford 001; Paul Mineiro; Sebastian Kochman (CoRR, 2022)

Personalized Reward Learning with Interaction-Grounded Learning (IGL). - Jessica Maghakian; Paul Mineiro; Kishan Panaganti; Mark Rucker; Akanksha Saran; Cheng Tan (ICLR, 2023)

Infinite Action Contextual Bandits with Reusable Data Exhaust. - Mark Rucker; Yinglun Zhu; Paul Mineiro (ICML, 2023)

Time-uniform confidence bands for the CDF under nonstationarity. - Paul Mineiro; Steven R. Howard (NeurIPS, 2023)

Practical Contextual Bandits with Feedback Graphs. - Mengxiao Zhang; Yuheng Zhang; Olga Vrousgou; Haipeng Luo; Paul Mineiro (NeurIPS, 2023)

Graph Feedback via Reduction to Regression. - Paul Mineiro (CoRR, 2023)

Efficient Contextual Bandits with Uninformed Feedback Graphs. - Mengxiao Zhang; Yuheng Zhang; Haipeng Luo; Paul Mineiro (ICML, 2024)

Aligning LLM Agents by Learning Latent Preference from User Edits. - Ge Gao; Alexey Taymanov; Eduardo Salinas; Paul Mineiro; Dipendra Misra (NeurIPS, 2024)

Active, anytime-valid risk controlling prediction sets. - Ziyu Xu 001; Nikos Karampatziakis; Paul Mineiro (NeurIPS, 2024)

Provably Efficient Interactive-Grounded Learning with Personalized Reward. - Mengxiao Zhang; Yuheng Zhang; Haipeng Luo; Paul Mineiro (NeurIPS, 2024)

Online Joint Fine-tuning of Multi-Agent Flows. - Paul Mineiro (CoRR, 2024)

Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning. - Yihe Deng; Paul Mineiro (CoRR, 2024)