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Paul Mineiro
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Journal Articles
- 2016
- [j4]Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Learning Reductions That Really Work. Proc. IEEE 104(1): 136-147 (2016) - 2002
- [j3]Javier R. Movellan, Paul Mineiro, Ruth J. Williams:
A Monte Carlo EM Approach for Partially Observable Diffusion Processes: Theory and Applications to Neural Networks. Neural Comput. 14(7): 1507-1544 (2002) - 1998
- [j2]Javier R. Movellan, Paul Mineiro:
Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition. Mach. Learn. 32(2): 85-100 (1998) - [j1]Paul Mineiro, David Zipser:
Analysis of Direction Selectivity Arising From Recurrent Cortical Interactions. Neural Comput. 10(2): 353-371 (1998)
Conference and Workshop Papers
- 2023
- [c27]Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan:
Personalized Reward Learning with Interaction-Grounded Learning (IGL). ICLR 2023 - [c26]Mark Rucker, Yinglun Zhu, Paul Mineiro:
Infinite Action Contextual Bandits with Reusable Data Exhaust. ICML 2023: 29259-29274 - [c25]Paul Mineiro, Steven R. Howard:
Time-uniform confidence bands for the CDF under nonstationarity. NeurIPS 2023 - [c24]Mengxiao Zhang, Yuheng Zhang, Olga Vrousgou, Haipeng Luo, Paul Mineiro:
Practical Contextual Bandits with Feedback Graphs. NeurIPS 2023 - 2022
- [c23]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. ICML 2022: 27428-27453 - [c22]Yinglun Zhu, Paul Mineiro:
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. ICML 2022: 27574-27590 - [c21]Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford:
Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS 2022 - [c20]Jessica Maghakian, Kishan Panaganti, Paul Mineiro, Akanksha Saran, Cheng Tan:
Interaction-Grounded Learning for Recommender Systems. ORSUM@RecSys 2022 - [c19]Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc T. Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal:
Deploying a Steered Query Optimizer in Production at Microsoft. SIGMOD Conference 2022: 2299-2311 - 2021
- [c18]Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas:
Off-Policy Confidence Sequences. ICML 2021: 5301-5310 - [c17]Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi:
ChaCha for Online AutoML. ICML 2021: 11263-11273 - [c16]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. ICML 2021: 11414-11423 - [c15]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021: 6683-6694 - 2020
- [c14]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. NeurIPS 2020 - 2019
- [c13]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. ICML 2019: 6026-6035 - 2017
- [c12]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. ICML 2017: 923-932 - 2015
- [c11]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings via Randomized Linear Algebra. ECML/PKDD (1) 2015: 37-51 - [c10]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings for Extremely Large Output Spaces. ICLR (Workshop) 2015 - 2014
- [c9]Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. ICML 2014: 494-502 - 2013
- [c8]Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer:
Machine learning on Big Data. ICDE 2013: 1242-1244 - [c7]Paul Mineiro, Nikos Karampatziakis:
Loss-Proportional Subsampling for Subsequent ERM. ICML (3) 2013: 522-530 - [c6]Tyson Condie, Paul Mineiro, Neoklis Polyzotis, Markus Weimer:
Machine learning for big data. SIGMOD Conference 2013: 939-942 - [c5]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. UAI 2013 - 2000
- [c4]Javier R. Movellan, Paul Mineiro, Ruth J. Williams:
Partially Observable SDE Models for Image Sequence Recognition Tasks. NIPS 2000: 880-886 - 1999
- [c3]Javier R. Movellan, Paul Mineiro:
A diffusion network approach to visual speech recognition. AVSP 1999: 15 - 1997
- [c2]Paul Mineiro, Javier R. Movellan, Ruth J. Williams:
Learning Path Distributions Using Nonequilibrium Diffusion Networks. NIPS 1997: 598-604 - [c1]Javier R. Movellan, Paul Mineiro:
Bayesian Robustification for Audio Visual Fusion. NIPS 1997: 742-748
Informal and Other Publications
- 2024
- [i38]Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro:
Efficient Contextual Bandits with Uninformed Feedback Graphs. CoRR abs/2402.08127 (2024) - [i37]Ge Gao, Alexey Taymanov, Eduardo Salinas, Paul Mineiro, Dipendra Misra:
Aligning LLM Agents by Learning Latent Preference from User Edits. CoRR abs/2404.15269 (2024) - [i36]Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro:
Provably Efficient Interactive-Grounded Learning with Personalized Reward. CoRR abs/2405.20677 (2024) - [i35]Paul Mineiro:
Online Joint Fine-tuning of Multi-Agent Flows. CoRR abs/2406.04516 (2024) - [i34]Ziyu Xu, Nikos Karampatziakis, Paul Mineiro:
Active, anytime-valid risk controlling prediction sets. CoRR abs/2406.10490 (2024) - 2023
- [i33]Mark Rucker, Yinglun Zhu, Paul Mineiro:
Infinite Action Contextual Bandits with Reusable Data Exhaust. CoRR abs/2302.08551 (2023) - [i32]Paul Mineiro:
Graph Feedback via Reduction to Regression. CoRR abs/2302.08631 (2023) - [i31]Paul Mineiro, Steven R. Howard:
Time-uniform confidence bands for the CDF under nonstationarity. CoRR abs/2302.14248 (2023) - 2022
- [i30]Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford:
Interaction-Grounded Learning with Action-inclusive Feedback. CoRR abs/2206.08364 (2022) - [i29]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. CoRR abs/2207.05836 (2022) - [i28]Yinglun Zhu, Paul Mineiro:
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. CoRR abs/2207.05849 (2022) - [i27]Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, Paul Mineiro:
Anytime-valid off-policy inference for contextual bandits. CoRR abs/2210.10768 (2022) - [i26]Paul Mineiro:
A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean. CoRR abs/2210.11133 (2022) - [i25]Mónika Farsang, Paul Mineiro, Wangda Zhang:
Conditionally Risk-Averse Contextual Bandits. CoRR abs/2210.13573 (2022) - [i24]Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc T. Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal:
Deploying a Steered Query Optimizer in Production at Microsoft. CoRR abs/2210.13625 (2022) - [i23]Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad:
Eigen Memory Trees. CoRR abs/2210.14077 (2022) - [i22]Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman:
Towards Data-Driven Offline Simulations for Online Reinforcement Learning. CoRR abs/2211.07614 (2022) - [i21]Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan:
Personalized Reward Learning with Interaction-Grounded Learning (IGL). CoRR abs/2211.15823 (2022) - 2021
- [i20]Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas:
Off-policy Confidence Sequences. CoRR abs/2102.09540 (2021) - [i19]Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan:
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL. CoRR abs/2106.00589 (2021) - [i18]Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi:
ChaCha for Online AutoML. CoRR abs/2106.04815 (2021) - [i17]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. CoRR abs/2106.04887 (2021) - [i16]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR abs/2106.06926 (2021) - 2019
- [i15]Nikos Karampatziakis, Sebastian Kochman, Jade Huang, Paul Mineiro, Kathy Osborne, Weizhu Chen:
Lessons from Real-World Reinforcement Learning in a Customer Support Bot. CoRR abs/1905.02219 (2019) - [i14]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. CoRR abs/1906.03323 (2019) - 2018
- [i13]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. CoRR abs/1807.06473 (2018) - 2016
- [i12]He He, Paul Mineiro, Nikos Karampatziakis:
Active Information Acquisition. CoRR abs/1602.02181 (2016) - [i11]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. CoRR abs/1606.04988 (2016) - 2015
- [i10]Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Learning Reductions that Really Work. CoRR abs/1502.02704 (2015) - [i9]Nikos Karampatziakis, Paul Mineiro:
Multilabel Prediction via Calibration. CoRR abs/1502.02710 (2015) - [i8]Paul Mineiro, Nikos Karampatziakis:
A Hierarchical Spectral Method for Extreme Classification. CoRR abs/1511.03260 (2015) - 2014
- [i7]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. CoRR abs/1408.2065 (2014) - [i6]Paul Mineiro, Nikos Karampatziakis:
A Randomized Algorithm for CCA. CoRR abs/1411.3409 (2014) - [i5]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings for Extremely Large Output Spaces. CoRR abs/1412.6547 (2014) - 2013
- [i4]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. CoRR abs/1305.6646 (2013) - [i3]Paul Mineiro, Nikos Karampatziakis:
Loss-Proportional Subsampling for Subsequent ERM. CoRR abs/1306.1840 (2013) - [i2]Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. CoRR abs/1310.1934 (2013) - [i1]Nikos Karampatziakis, Paul Mineiro:
Combining Structured and Unstructured Randomness in Large Scale PCA. CoRR abs/1310.6304 (2013)
Coauthor Index
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