@inproceedings{xu24sample,
author = {Ziping Xu and Zifan Xu and Runxuan Jiang and Peter Stone and Ambuj Tewari},
title = {Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks},
booktitle = {Proceedings of 12th International Conference on Learning Representations},
year = {2024},
url = {https://openreview.net/forum?id=YZrg56G0JV},
pdf = {research/xu24sample.pdf}
}
@inproceedings{hong24primal,
author = {Kihyuk Hong and Yuhang Li and Ambuj Tewari},
title = {A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
pdf = {research/hong24primal.pdf}
}
@inproceedings{trauger24sequence,
author = {Jacob Trauger and Ambuj Tewari},
title = {Sequence Length Independent Norm-Based Generalization Bounds for Transformers},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
pdf = {research/trauger24sequence.pdf}
}
@inproceedings{kausik24offline,
author = {Chinmaya Kausik and Yangyi Lu and Kevin Tan and Maggie Makar and Yixin Wang and Ambuj Tewari},
title = {Offline Policy Evaluation and Optimization Under Confounding},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
pdf = {research/kausik24offline.pdf}
}
@inproceedings{patel24conformal,
author = {Yash Patel and Sahana Rayan and Ambuj Tewari},
title = {Conformal Contextual Robust Optimization},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
pdf = {research/patel24conformal.pdf}
}
@article{modi24joint,
title = {Joint Learning of Linear Time-Invariant Dynamical Systems},
author = {Aditya Modi and Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
journal = {Automatica},
volume = {164},
pages = {111635},
year = {2024},
url = {https://doi.org/10.1016/j.automatica.2024.111635},
pdf = {research/modi24joint.pdf}
}
@inproceedings{raman24multiclass,
author = {Ananth Raman and Vinod Raman and Unique Subedi and Idan Mehalel and Ambuj Tewari},
title = {Multiclass Online Learnability under Bandit Feedback},
booktitle = {Proceedings of 35th International Conference on Algorithmic Learning Theory},
year = {2024},
pdf = {research/raman24multiclass.pdf}
}
@inproceedings{raman24online,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Online Infinite-Dimensional Regression: Learning Linear Operators},
booktitle = {Proceedings of 35th International Conference on Algorithmic Learning Theory},
year = {2024},
pdf = {research/raman24online.pdf}
}
@inproceedings{raman23learnability,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {On the Learnability of Multilabel Ranking},
booktitle = {Advances in Neural Information Processing Systems 36},
year = {2023},
url = {https://openreview.net/forum?id=cwBeRBe9hq},
pdf = {research/raman23learnability.pdf}
}
@inproceedings{raman23proper,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {On Proper Learnability between Average- and Worst-case Robustness},
booktitle = {Advances in Neural Information Processing Systems 36},
year = {2023},
url = {https://openreview.net/forum?id=4yXnnCK3r9},
pdf = {research/raman23proper.pdf}
}
@article{shim23machine,
title = {Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit},
author = {Eunjae Shim and Ambuj Tewari and Tim Cernak and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
year = {2023},
volume = {63},
number = {12},
pages = {3659--3668},
pdf = {research/shim23machine.pdf},
url = {https://doi.org/10.1021/acs.jcim.3c00577}
}
@inproceedings{hanneke23multiclass,
author = {Steve Hanneke and Shay Moran and Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Multiclass Online Learning and Uniform Convergence},
booktitle = {Proceedings of the 36th Annual Conference on Learning Theory},
year = {2023},
pdf = {research/hanneke23multiclass.pdf},
url = {https://proceedings.mlr.press/v195/hanneke23b.html}
}
@inproceedings{chandrasekaran23learning,
author = {Gautam Chandrasekaran and Ambuj Tewari},
title = {Learning in Online {MDPs}: Is there a Price for Handling the Communicating Case?},
booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
year = {2023},
series = {Proceedings of Machine Learning Research},
volume = {216},
pages = {293--302},
pdf = {research/chandrasekaran23learning.pdf},
url = {https://proceedings.mlr.press/v216/chandrasekaran23a.html}
}
@inproceedings{roy23thompson,
author = {Saptarshi Roy and Sunrit Chakraborty and Ambuj Tewari},
title = {{T}hompson Sampling for High-Dimensional Sparse Linear Contextual Bandits},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
year = {2023},
pdf = {research/roy23thompson.pdf},
url = {https://proceedings.mlr.press/v202/chakraborty23b.html}
}
@inproceedings{kausik23learning,
author = {Chinmaya Kausik and Kevin Tan and Ambuj Tewari},
title = {Learning Mixtures of {M}arkov Chains and {MDP}s},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
year = {2023},
pdf = {research/kausik23learning.pdf},
url = {https://proceedings.mlr.press/v202/kausik23a.html}
}
@article{tewari23mhealth,
title = {mHealth Systems Need a Privacy-by-Design Approach: Commentary on "{F}ederated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review"},
author = {Ambuj Tewari},
journal = {Journal of Medical Internet Research},
year = {2023},
volume = {25},
pages = {e46700},
url = {https://doi.org/10.2196/46700},
pdf = {research/tewari23mhealth.pdf},
note = {Invited Commentary}
}
@article{shen23exploring,
title = {Exploring the Relationship Between Privacy and Utility in Mobile Health: A Simulation of Federated Learning, Differential Privacy, and External Attacks},
author = {Alexander Shen and Luke Francisco and Srijan Sen and Ambuj Tewari},
journal = {Journal of Medical Internet Research},
year = {2023},
volume = {25},
pages = {e43664},
url = {https://doi.org/10.2196/43664},
pdf = {research/shen23exploring.pdf}
}
@article{bairakdar23meta-analysis,
title = {A Meta-Analysis of {RNA}-Seq Studies to Identify Novel Genes that Regulate Aging},
author = {Mohamad D. Bairakdar and Ambuj Tewari and Matthias C. Truttman},
journal = {Experimental Gerontology},
volume = {173},
pages = {112107},
year = {2023},
url = {https://doi.org/10.1016/j.exger.2023.112107},
pdf = {research/bairakdar23meta-analysis.pdf}
}
@inproceedings{hong23optimization,
author = {Kihyuk Hong and Yuhang Li and Ambuj Tewari},
title = {An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge},
booktitle = {Proceedings of the 26th International Conference on Artificial Intelligence and Statistics},
year = {2023},
series = {Proceedings of Machine Learning Research},
volume = {206},
pages = {3048--3085},
pdf = {research/hong23optimization.pdf},
url = {https://proceedings.mlr.press/v206/hong23b.html}
}
@article{wu22effectiveness,
title = {Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial},
author = {Jitao Wang and Yu Fang and Elena Frank and Maureen Walton and Margit Burmeister and Ambuj Tewari and Walter Dempsey and Timothy NeCamp and Srijan Sen and Zhenke Wu},
journal = {npj Digital Medicine},
volume = {6},
number = {4},
year = {2022},
url = {https://doi.org/10.1038/s41746-022-00746-y},
pdf = {research/wang22effectiveness.pdf}
}
@article{elbalghiti22generalization,
author = {Othman El Balghiti and Adam N. Elmachtoub and Paul Grigas and Ambuj Tewari},
title = {Generalization Bounds in the Predict-then-Optimize Framework},
journal = {Mathematics of Operations Research},
year = {2022},
note = {accepted},
url = {https://doi.org/10.1287/moor.2022.1330},
pdf = {research/elbalghiti22generalization.pdf}
}
@inproceedings{xu22adaptive,
author = {Ziping Xu and Eunjae Shim and Ambuj Tewari and Paul Zimmerman},
title = {Adaptive Sampling for Discovery},
booktitle = {Advances in Neural Information Processing Systems 35},
year = {2022},
pages = {1114--1126},
url = {https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html},
pdf = {research/xu22adaptive.pdf}
}
@inproceedings{raman22online,
author = {Vinod Raman and Ambuj Tewari},
title = {Online Agnostic Multiclass Boosting},
booktitle = {Advances in Neural Information Processing Systems 35},
year = {2022},
pages = {25908--25920},
url = {https://papers.nips.cc/paper_files/paper/2022/hash/a6acb2d482de9c708f5b03d5a70465d2-Abstract-Conference.html},
pdf = {research/raman22online.pdf}
}
@article{jiang22conformer-rl,
author = {Runxuan Jiang and Tarun Gogineni and Joshua Kammeraad and Yifei He and Ambuj Tewari and Paul M. Zimmerman},
title = {Conformer-{RL}: A deep reinforcement learning library for conformer generation},
journal = {Journal of Computational Chemistry},
year = {2022},
volume = {43},
issue = {27},
pages = {1880--1886},
url = {https://doi.org/10.1002/jcc.26984},
pdf = {research/jiang22conformer-rl.pdf}
}
@article{dawid22learnability,
author = {Philip Dawid and Ambuj Tewari},
title = {On Learnability under General Stochastic Processes},
journal = {Harvard Data Science Review},
volume = {4},
number = {4},
year = {2022},
url = {https://doi.org/10.1162/99608f92.dec7d780},
pdf = {research/dawid22learnability.pdf}
}
@inproceedings{digiovanni22balancing,
author = {Anthony DiGiovanni and Ambuj Tewari},
title = {Balancing Adaptability and Non-exploitability in Repeated Games},
booktitle = {Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence},
series = {Proceedings of Machine Learning Research},
volume = {180},
pages = {559--568},
year = {2022},
url = {https://proceedings.mlr.press/v180/digiovanni22a.html},
pdf = {research/digiovanni22balancing.pdf}
}
@inproceedings{xu22statistical,
author = {Ziping Xu and Ambuj Tewari},
title = {On the Statistical Benefits of Curriculum Learning},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
series = {Proceedings of Machine Learning Research},
volume = {162},
pages = {24663--24682},
year = {2022},
url = {https://proceedings.mlr.press/v162/xu22i.html},
pdf = {research/xu22statistical.pdf}
}
@article{kim22predicting,
author = {Eunjae Shim and Joshua A. Kammeraad and Ziping Xu and Ambuj Tewari and Tim Cernak and Paul M. Zimmerman},
title = {Predicting Reaction Conditions from Limited Data through Active Transfer Learning},
journal = {Chemical Science},
year = {2022},
volume = {13},
issue = {22},
pages = {6655--6668},
url = {http://dx.doi.org/10.1039/D1SC06932B},
pdf = {research/kim22predicting.pdf}
}
@inproceedings{deng22weighted,
author = {Yuntian Deng and Xingyu Zhou and Baekjin Kim and Ambuj Tewari and Abhishek Gupta and Ness Shroff},
title = {Weighted Gaussian Process Bandits for Non-stationary Environments},
booktitle = {Proceedings of the 25th International Conference on Artificial Intelligence and Statistics},
year = {2022},
series = {Proceedings of Machine Learning Research},
volume = {151},
pages = {6909--6932},
pdf = {research/deng22weighted.pdf},
url = {https://proceedings.mlr.press/v151/deng22b.html}
}
@inproceedings{lu22causal,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari},
title = {Efficient Reinforcement Learning with Prior Causal Knowledge},
booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning},
year = {2022},
pdf = {research/lu22causal.pdf},
url = {https://www.cclear.cc/2022/AcceptedPapers}
}
@inproceedings{lu21causal,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari},
title = {Causal Bandits with Unknown Graph Structure},
booktitle = {Advances in Neural Information Processing Systems 34},
year = {2021},
pages = {24817--24828},
pdf = {research/lu21causal.pdf},
url = {https://proceedings.neurips.cc/paper/2021/hash/d010396ca8abf6ead8cacc2c2f2f26c7-Abstract.html}
}
@inproceedings{xu21representation,
author = {Ziping Xu and Ambuj Tewari},
title = {Representation Learning Beyond Linear Prediction Functions},
booktitle = {Advances in Neural Information Processing Systems 34},
year = {2021},
pages = {4792--4804},
pdf = {research/xu21representation.pdf},
url = {https://proceedings.neurips.cc/paper/2021/hash/258be18e31c8188555c2ff05b4d542c3-Abstract.html}
}
@inproceedings{digiovanni21thompson,
author = {Anthony DiGiovanni and Ambuj Tewari},
title = {{T}hompson Sampling for {M}arkov Games with Piecewise Stationary Opponent Policies},
booktitle = {Proceedings of the 37th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2021},
pdf = {research/digiovanni21thompson.pdf},
series = {Proceeding of Maching Learning Research},
volume = {161},
pages = {738--748},
url = {https://proceedings.mlr.press/v161/digiovanni21a.html}
}
@article{liu21learning,
author = {Jessica Chia Liu and Jack Goetz and Srijan Sen and Ambuj Tewari},
title = {Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data},
journal = {JMIR mHealth and uHealth},
year = {2021},
month = {Mar},
volume = {9},
number = {3},
pages = {e23728},
url = {https://doi.org/10.2196/23728},
pdf = {research/liu21learning.pdf}
}
@inproceedings{lu21low-rank,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari },
title = {Low-Rank Generalized Linear Bandit Problems},
booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics},
pages = {460--468},
year = {2021},
volume = {130},
series = {Proceedings of Machine Learning Research},
pdf = {research/lu21low-rank.pdf},
url = {http://proceedings.mlr.press/v130/lu21a.html}
}
@inproceedings{xu21decision,
author = {Ziping Xu and Amirhossein Meisami and Ambuj Tewari },
title = {Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns},
booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics},
year = {2021},
pages = {127--135},
volume = {130},
series = {Proceedings of Machine Learning Research},
pdf = {research/xu21decision.pdf},
url = {http://proceedings.mlr.press/v130/xu21a.html}
}
@inproceedings{xu20reinforcement,
author = {Ziping Xu and Ambuj Tewari},
title = {Reinforcement Learning in Factored {MDPs}: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/xu20reinforcement.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@inproceedings{jung20equivalence,
author = {Young Hun Jung and Baekjin Kim and Ambuj Tewari},
title = {On the Equivalence between Online and Private Learnability beyond Binary Classification},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/jung20equivalence.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@inproceedings{gogineni20torsionnet,
author = {Tarun Gogineni and Ziping Xu and Exequiel Punzalan and Runxuan Jiang and Joshua Kammeraad and Ambuj Tewari and Paul Zimmerman},
title = {{TorsionNet}: A Reinforcement Learning Approach to Sequential Conformer Search},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/gogineni20torsionnet.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@article{faradonbeh20optimism,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Optimism-Based Adaptive Regulation of Linear-Quadratic Systems},
journal = {IEEE Transactions on Automatic Control},
volume = {66},
number = {4},
pages = {1802--1808},
month = {April},
year = {2020},
pdf = {research/faradonbeh20optimism.pdf},
url = {https://doi.org/10.1109/TAC.2020.2998952}
}
@article{wong20lasso,
author = {Kam Chung Wong and Zifan Li and Ambuj Tewari},
title = {Lasso Guarantees for $\beta$-Mixing Heavy Tailed Time Series},
journal = {Annals of Statistics},
year = {2020},
volume = {48},
number = {2},
pages = {1124--1142},
url = {http://dx.doi.org/10.1214/19-AOS1840},
pdf = {research/wong20lasso.pdf}
}
@inproceedings{modi20no-regret,
author = {Aditya Modi and Ambuj Tewari},
title = {No-regret Exploration in Contextual Reinforcement Learning},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/modi20no-regret.pdf},
url = {http://www.auai.org/uai2020/proceedings/355_main_paper.pdf}
}
@inproceedings{niss20what,
author = {Laura Niss and Ambuj Tewari},
title = {What You See May Not Be What You Get: {UCB} Bandit Algorithms Robust to ε-Contamination},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/niss20what.pdf},
url = {http://www.auai.org/uai2020/proceedings/198_main_paper.pdf}
}
@inproceedings{lu20regret,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari and Zhenyu Yan},
title = {Regret Analysis of Bandit Problems with Causal Background Knowledge},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/lu20regret.pdf},
url = {http://www.auai.org/uai2020/proceedings/77_main_paper.pdf}
}
@inproceedings{kim20randomized,
author = {Baekjin Kim and Ambuj Tewari},
title = {Randomized Exploration for Non-Stationary Stochastic Linear Bandits},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/kim20randomized.pdf},
url = {http://www.auai.org/uai2020/proceedings/49_main_paper.pdf}
}
@article{faradonbeh20adaptive,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {On Adaptive Linear-Quadratic Regulators},
journal = {Automatica},
year = {2020},
volume = {117},
month = {July},
url = {https://doi.org/10.1016/j.automatica.2020.108982},
pdf = {research/faradonbeh20adaptive.pdf}
}
@article{kammeraad20machine,
title = {What Does the Machine Learn? {K}nowledge Representations of Chemical Reactivity},
author = {Joshua Andrew Kammeraad and Jack Goetz and Eric A. Walker and Ambuj Tewari and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
year = {2020},
volume = {60},
number = {3},
pages = {1290-1301},
url = {https://dx.doi.org/10.1021/acs.jcim.9b00721},
pdf = {research/kammeraad20machine.pdf}
}
@article{faradonbeh20input,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Input Perturbations for Adaptive Control and Learning},
journal = {Automatica},
year = {2020},
volume = {117},
month = {July},
url = {https://doi.org/10.1016/j.automatica.2020.108950},
pdf = {research/faradonbeh20input.pdf}
}
@inproceedings{modi20sample,
author = {Aditya Modi and Nan Jiang and Ambuj Tewari and Satinder Singh},
title = {Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles},
booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics},
year = {2020},
pdf = {research/modi20sample.pdf},
url = {http://proceedings.mlr.press/v108/modi20a.html}
}
@article{necamp20assessing,
title = {Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-randomized Trial},
author = {Timothy NeCamp and Srijan Sen and Elena Frank and Maureen A. Walton and Edward L. Ionides and Yu Fang and Ambuj Tewari and Zhenke Wu},
journal = {Journal of Medical Internet Research},
volume = {22},
number = {3},
pages = {e15033},
year = {2020},
url = {https://doi.org/10.2196/15033},
pdf = {research/necamp20assessing.pdf}
}
@inproceedings{balghiti19generalization,
author = {Othman El Balghiti and Adam Elmachtoub and Paul Grigas and Ambuj Tewari},
title = {Generalization Bounds in the Predict-then-Optimize Framework},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {14389--14398},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/balghiti19generalization.pdf}
}
@inproceedings{jung19regret,
author = {Young Hun Jung and Ambuj Tewari},
title = {Regret Bounds for {T}hompson Sampling in Restless Bandit Problems},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {9005--9014},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/jung19regret.pdf}
}
@inproceedings{abernethy19online,
author = {Jacob Abernethy and Young Hun Jung and Chansoo Lee and Audra McMillan and Ambuj Tewari},
title = {Online Learning via the Differential Privacy Lens},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {8892--8902},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/abernethy19online.pdf}
}
@inproceedings{kim19optimality,
author = {Baekjin Kim and Ambuj Tewari},
title = {On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {2691--2700},
year = {2019},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/kim19optimality.pdf}
}
@article{walker2019learning,
title = {Learning to Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst},
author = {Eric Walker and Joshua Andrew Kammeraad and Jack Goetz and Michael Robo and Ambuj Tewari and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
volume = {59},
number = {9},
pages = {3645--3654},
year = {2019},
url = {https://doi.org/10.1021/acs.jcim.9b00313},
pdf = {research/walker2019learning.pdf}
}
@inproceedings{faradonbeh19applications,
title = {On Applications of Bootstrap in Continuous Space Reinforcement Learning},
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
booktitle = {Proceedings of the 2019 IEEE 58th Conference on Decision and Control},
year = {2019},
pages = {1977--1984},
pdf = {research/faradonbeh19applications.pdf},
url = {https://doi.org/10.1109/CDC40024.2019.9029975}
}
@incollection{rabbi19optimizing,
author = {Mashfiqui Rabbi and Predrag Klasnja and Tanzeem Choudhury and Ambuj Tewari and Susan Murphy},
title = {Optimizing mHealth Interventions with a Bandit},
booktitle = {Mobile Sensing and Digital Phenotyping: New Developments in Psychoinformatics},
publisher = {Springer},
year = {2019},
editor = {Harald Baumeister and Christian Montag},
url = {https://doi.org/10.1007/978-3-030-31620-4_18},
pdf = {research/rabbi19optimizing.pdf}
}
@inproceedings{faradonbeh19randomized,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems},
booktitle = {Proceedings of the 2019 IEEE Data Science Workshop},
pages = {170--174},
year = {2019},
url = {https://doi.org/10.1109/DSW.2019.8755578},
pdf = {research/faradonbeh19randomized.pdf}
}
@article{faradonbeh19finite,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Finite Time Adaptive Stabilization of {LQ} Systems},
journal = {IEEE Transactions on Automatic Control},
volume = {64},
number = {8},
month = aug,
pages = {3498--3505},
year = {2019},
url = {https://doi.org/10.1109/TAC.2018.2883241},
pdf = {research/faradonbeh19finite.pdf}
}
@inproceedings{zhang19online,
author = {Daniel T. Zhang and Young Hun Jung and Ambuj Tewari},
title = {Online Multiclass Boosting with Bandit Feedback},
booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
volume = {89},
year = {2019},
pages = {1148--1156},
pdf = {research/zhang19online.pdf},
url = {http://proceedings.mlr.press/v89/zhang19e.html}
}
@inproceedings{sun18linear,
author = {Yitong Sun and Anna C. Gilbert and Ambuj Tewari},
title = {But How Does It Work in Theory? {L}inear {SVM} with Random Features},
booktitle = {Advances in Neural Information Processing Systems 31},
year = {2018},
pages = {3383--3392},
url = {https://proceedings.neurips.cc/paper/2018},
pdf = {research/sun18linear.pdf}
}
@inproceedings{goetz18active,
author = {Jack Goetz and Ambuj Tewari and Paul Zimmerman},
title = {Active Learning for Non-Parametric Regression Using Purely Random Trees},
booktitle = {Advances in Neural Information Processing Systems 31},
year = {2018},
pages = {2542--2551},
url = {https://proceedings.neurips.cc/paper/2018},
pdf = {research/goetz18active.pdf}
}
@article{nahum-shani18just-in-time,
author = {Inbal Nahum-Shani and Shawna N. Smith and Bonnie J. Spring and Linda M. Collins and Katie Witkiewitz and Ambuj Tewari and Susan A. Murphy},
title = {Just-in-Time Adaptive Interventions {(JITAIs)} in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support},
journal = {Annals of Behavioral Medicine},
month = {May},
volume = {52},
number = {6},
year = {2018},
pages = {446--462},
url = {http://dx.doi.org/10.1007/s12160-016-9830-8},
pdf = {research/nahum-shani18just-in-time.pdf}
}
@article{faradonbeh18finite,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Finite Time Identification in Unstable Linear Systems},
journal = {Automatica},
year = {2018},
volume = {96},
month = {October},
pages = {342--353},
url = {https://doi.org/10.1016/j.automatica.2018.07.008},
pdf = {research/faradonbeh18finite.pdf}
}
@inproceedings{modi18markov,
author = {Aditya Modi and Nan Jiang and Satinder Singh and Ambuj Tewari},
title = {{M}arkov Decision Processes with Continuous Side Information},
booktitle = {Proceedings of the 29th International Conference on Algorithmic Learning Theory},
series = {Proceedings of Machine Learning Research},
volume = {83},
pages = {597--618},
year = {2018},
url = {http://proceedings.mlr.press/v83/modi18a.html},
pdf = {research/modi18markov.pdf}
}
@inproceedings{jung18online,
author = {Young Hun Jung and Ambuj Tewari},
title = {Online Boosting Algorithms for Multi-label Ranking},
booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
volume = {84},
pages = {279--287},
year = {2018},
url = {http://proceedings.mlr.press/v84/jung18a.html},
pdf = {research/jung18online.pdf}
}
@article{li18beyond,
author = {Zifan Li and Ambuj Tewari},
title = {Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits},
journal = {Journal of Machine Learning Research},
year = {2018},
volume = {18},
number = {183},
pages = {1--24},
url = {http://jmlr.org/papers/v18/17-364.html},
pdf = {research/li18beyond.pdf}
}
@article{natarajan18cost,
author = {Nagarajan Natarajan and Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Cost-Sensitive Learning with Noisy Labels},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {155},
pages = {1--33},
year = {2018},
url = {http://jmlr.org/papers/v18/15-226.html},
pdf = {research/natarajan18cost.pdf}
}
@article{li18sampled,
author = {Zifan Li and Ambuj Tewari},
title = {Sampled Fictitious Play is {H}annan Consistent},
journal = {Games and Economic Behavior},
year = {2018},
volume = {109},
pages = {401--412},
url = {https://doi.org/10.1016/j.geb.2018.01.005},
pdf = {research/li18sampled.pdf}
}
@article{ramaswamy18consistent,
author = {Harish G. Ramaswamy and Ambuj Tewari and Shivani Agarwal},
title = {Consistent Algorithms for Multiclass Classification with an Abstain Option},
journal = {Electronic Journal of Statistics},
volume = {12},
number = {1},
year = {2018},
pages = {530--554},
url = {https://doi.org/10.1214/17-EJS1388},
pdf = {research/ramaswamy18consistent.pdf}
}
@article{faradonbeh17optimality,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Optimality of Fast Matching Algorithms for Random Networks with Applications to Structural Controllability},
journal = {IEEE Transactions on Control of Network Systems},
year = {2017},
volume = {4},
number = {4},
pages = {770--780},
url = {https://doi.org/10.1109/TCNS.2016.2553366},
pdf = {research/faradonbeh17optimality.pdf}
}
@inproceedings{greenewald17action,
author = {Kristjan Greenewald and Ambuj Tewari and Predrag Klasnja and Susan A. Murphy},
title = {Action Centered Contextual Bandits},
booktitle = {Advances in Neural Information Processing Systems 30},
year = {2017},
pages = {5979--5987},
url = {https://proceedings.neurips.cc/paper/2017},
pdf = {research/greenewald17action.pdf}
}
@inproceedings{jung17online,
author = {Young Hun Jung and Jack Goetz and Ambuj Tewari},
title = {Online Multiclass Boosting},
booktitle = {Advances in Neural Information Processing Systems 30},
year = {2017},
pages = {920--929},
url = {https://proceedings.neurips.cc/paper/2017},
pdf = {research/jung17online.pdf}
}
@article{chaudhuri17online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Learning to Rank with Top-k Feedback},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {103},
pages = {1--50},
year = {2017},
pdf = {research/chaudhuri17online.pdf},
url = {http://jmlr.org/papers/v18/16-285.html}
}
@article{jain17partial,
author = {Prateek Jain and Inderjit S. Dhillon and Ambuj Tewari},
title = {Partial Hard Thresholding},
journal = {IEEE Transactions on Information Theory},
volume = {63},
number = {5},
year = {2017},
pages = {3029--3038},
url = {https://doi.org/10.1109/TIT.2017.2686880},
pdf = {research/jain17partial.pdf}
}
@incollection{tewari17ads,
author = {Ambuj Tewari and Susan A. Murphy},
title = {From Ads to Interventions: Contextual Bandits in Mobile Health},
booktitle = {Mobile Health: Sensors, Analytic Methods, and Applications},
publisher = {Springer},
year = {2017},
editor = {Jim Rehg and Susan A. Murphy and Santosh Kumar},
url = {https://www.springer.com/us/book/9783319513935},
pdf = {research/tewari17ads.pdf}
}
@inproceedings{chaudhuri16phased,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games},
booktitle = {Advances in Neural Information Processing Systems 29},
year = {2016},
pages = {2433--2441},
pdf = {research/chaudhuri16phased.pdf},
url = {https://proceedings.neurips.cc/paper/2016}
}
@inproceedings{ramaswamy16mixture,
author = {Harish G. Ramaswamy and Clayton Scott and Ambuj Tewari},
title = {Mixture Proportion Estimation via Kernel Embeddings of Distributions},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {48},
pages = {2052--2060},
year = {2016},
url = {http://jmlr.org/proceedings/papers/v48/ramaswamy16.html},
pdf = {research/ramaswamy16mixture.pdf}
}
@inproceedings{jiang16structural,
author = {Nan Jiang and Satinder Singh and Ambuj Tewari},
title = {On Structural Properties of {MDPs} that Bound Loss due to Shallow Planning},
booktitle = {Proceedings of the 25th International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {1640--1647},
publisher = {AAAI Press},
url = {http://www.ijcai.org/Abstract/16/235},
pdf = {research/jiang16structural.pdf}
}
@incollection{abernethy16perturbation,
author = {Jacob Abernethy and Chansoo Lee and Ambuj Tewari},
title = {Perturbation Techniques in Online Learning and Optimization},
booktitle = {Perturbations, Optimization, and Statistics},
series = {Neural Information Processing Series},
publisher = {MIT Press},
year = {2016},
editor = {Tamir Hazan and George Papandreou and Daniel Tarlow},
chapter = {8},
pdf = {research/abernethy16perturbation.pdf},
url = {https://mitpress.mit.edu/books/perturbations-optimization-and-statistics}
}
@inproceedings{chaudhuri16online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Learning to Rank with Feedback at the Top},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {277--285},
series = {JMLR Workshop and Conference Proceedings},
volume = {51},
year = {2016},
pdf = {research/chaudhuri16online.pdf},
url = {http://jmlr.org/proceedings/papers/v51/chaudhuri16.html}
}
@article{liao16sample,
author = {Peng Liao and Predrag Klasnja and Ambuj Tewari and Susan A. Murphy},
title = {Sample Size Calculations for Micro-randomized Trials in mHealth},
journal = {Statistics in Medicine},
volume = {35},
number = {12},
pages = {1944--1971},
year = {2016},
url = {http://dx.doi.org/10.1002/sim.6847},
pdf = {research/liao16sample.pdf}
}
@article{klasnja15microrandomized,
author = {Predrag Klasnja and Eric B. Hekler and Saul Shiffman and Audrey Boruvka and Daniel Almirall and Ambuj Tewari and Susan A. Murphy},
title = {Microrandomized trials: An experimental design for developing just-in-time adaptive interventions},
journal = {Health Psychology},
volume = {34},
number = {Suppl},
month = {Dec},
pages = {1220--1228},
year = {2015},
url = {http://dx.doi.org/10.1037/hea0000305},
pdf = {research/klasnja15microrandomized.pdf}
}
@inproceedings{jain15predtron,
author = {Prateek Jain and Nagarajan Natarajan and Ambuj Tewari},
title = {Predtron: A Family of Online Algorithms for General Prediction Problems},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {1009--1017},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/jain15predtron.pdf}
}
@inproceedings{jain15alternating,
author = {Prateek Jain and Ambuj Tewari},
title = {Alternating Minimization for Regression Problems with Vector-valued Outputs},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {1126--1134},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/jain15alternating.pdf}
}
@inproceedings{abernethy15fighting,
author = {Jacob Abernethy and Chansoo Lee and Ambuj Tewari},
title = {Fighting Bandits with a New Kind of Smoothness},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {2188--2196},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/abernethy15fighting.pdf}
}
@inproceedings{ramaswamy15convex,
author = {Harish G. Ramaswamy and Ambuj Tewari and Shivani Agarwal},
title = {Convex Calibrated Surrogates for Hierarchical Classification},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {37},
pages = {1852--1860},
year = {2015},
url = {http://jmlr.org/proceedings/papers/v37/ramaswamy15.html},
pdf = {research/ramaswamy15convex.pdf}
}
@inproceedings{tewari15generalization,
author = {Ambuj Tewari and Sougata Chaudhuri},
title = {Generalization error bounds for learning to rank: Does the length of document lists matter?},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {37},
pages = {315--323},
year = {2015},
url = {http://jmlr.org/proceedings/papers/v37/tewari15.html},
pdf = {research/tewari15generalization.pdf}
}
@inproceedings{chaudhuri15online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Ranking with Top-1 Feedback},
booktitle = {Proceedings of the 18th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {38},
pages = {129--137},
year = {2015},
note = {Honorable Mention, Best Student Paper Award},
pdf = {research/chaudhuri15online.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v38/chaudhuri15.html}
}
@article{rakhlin15online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning via Sequential Complexities},
year = {2015},
journal = {Journal of Machine Learning Research},
volume = {16},
month = {Feb},
pages = {155--186},
pdf = {research/rakhlin15online.pdf},
url = {http://jmlr.org/papers/v16/rakhlin15a.html}
}
@article{rakhlin15sequential,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Sequential Complexities and Uniform Martingale Laws of Large Numbers},
journal = {Probability Theory and Related Fields},
year = {2015},
volume = {161},
number = {1--2},
pages = {111--153},
pdf = {research/rakhlin15sequential.pdf},
url = {http://dx.doi.org/10.1007/s00440-013-0545-5}
}
@inproceedings{jain14iterative,
author = {Prateek Jain and Ambuj Tewari and Purushottam Kar},
title = {On Iterative Hard Thresholding Methods for High-dimensional {M}-Estimation},
booktitle = {Advances in Neural Information Processing Systems 27},
pages = {685--693},
year = {2014},
pdf = {research/jain14iterative.pdf},
url = {https://proceedings.neurips.cc/paper/2014}
}
@inproceedings{abernethy14online,
author = {Jacob Abernethy and Chansoo Lee and Abhinav Sinha and Ambuj Tewari},
title = {Online Linear Optimization via Smoothing},
series = {JMLR Workshop and Conference Proceedings},
volume = {35},
year = {2014},
pages = {807--823},
booktitle = {Proceedings of the 27th Annual Conference on Learning Theory},
pdf = {research/abernethy14online.pdf},
url = {http://jmlr.org/proceedings/papers/v35/abernethy14.html}
}
@article{chiang14prediction,
author = {Kai-Yang Chiang and Cho-Jui Hsieh and Nagarajan Natarajan and Ambuj Tewari and Inderjit S. Dhillon},
title = {Prediction and Clustering in Signed Networks: A Local to Global Perspective},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
pages = {1177--1213},
month = {March},
pdf = {research/chiang14prediction.pdf},
url = {http://jmlr.org/papers/v15/chiang14a.html}
}
@incollection{tewari13learning,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Learning Theory},
booktitle = {Academic Press Library in Signal Processing: Volume 1 Signal Processing Theory and Machine Learning},
series = {Academic Press Library in Signal Processing},
volume = {1},
publisher = {Elsevier},
year = {2014},
editor = {Paulo S.R. Diniz and Johan A.K. Suykens and Rama Chellappa and Sergios Theodoridis},
chapter = {14},
pages = {775--816},
pdf = {research/tewari13learning.pdf},
url = {http://dx.doi.org/10.1016/B978-0-12-396502-8.00014-0}
}
@inproceedings{ramaswamy13convex,
author = {Harish G. Ramaswamy and Shivani Agarwal and Ambuj Tewari},
title = {Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses},
booktitle = {Advances in Neural Information Processing Systems 26},
pages = {1475--1483},
year = {2013},
pdf = {research/ramaswamy13convex.pdf},
url = {https://proceedings.neurips.cc/paper/2013}
}
@inproceedings{natarajan13learning,
author = {Nagarajan Natarajan and Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Learning with Noisy Labels},
booktitle = {Advances in Neural Information Processing Systems 26},
pages = {1196--1204},
year = {2013},
pdf = {research/natarajan13learning.pdf},
url = {https://proceedings.neurips.cc/paper/2013}
}
@inproceedings{yang13robust,
author = {Eunho Yang and Ambuj Tewari and Pradeep Ravikumar},
title = {On Robust Estimation of High Dimensional Generalized Linear Models},
booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {1834--1840},
publisher = {AAAI Press},
url = {http://ijcai.org/Abstract/13/271},
pdf = {research/yang13robust.pdf}
}
@article{singh-blom13prediction,
author = {U. Martin Singh-Blom and Nagarajan Natarajan and Ambuj Tewari and John O. Woods and Inderjit S. Dhillon and Edward M. Marcotte},
title = {Prediction and validation of gene-disease associations using methods inspired by social network analyses},
year = {2013},
journal = {PLoS One},
volume = {8},
number = {5},
pages = {e58977},
url = {http://dx.doi.org/10.1371/journal.pone.0058977},
pdf = {research/singh-blom13prediction.pdf}
}
@article{saha13non-asymptotic,
author = {Ankan Saha and Ambuj Tewari},
title = {On the Non-asymptotic Convergence of Cyclic Coordinate Descent Methods},
year = {2013},
volume = {23},
number = {1},
journal = {SIAM Journal on Optimization},
pages = {576--601},
url = {http://dx.doi.org/10.1137/110840054},
pdf = {research/saha13non-asymptotic.pdf}
}
@inproceedings{scherrer12feature,
author = {Chad Scherrer and Ambuj Tewari and Mahantesh Halappanavar and David Haglin},
title = {Feature Clustering for Accelerating Parallel Coordinate Descent},
year = {2012},
booktitle = {Advances in Neural Information Processing Systems 25},
pages = {28--36},
pdf = {research/scherrer12feature.pdf},
url = {https://proceedings.neurips.cc/paper/2012}
}
@inproceedings{arora12deterministic,
author = {Raman Arora and Ofer Dekel and Ambuj Tewari},
title = {Deterministic {MDPs} with Adversarial Rewards and Bandit Feedback},
booktitle = {Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {93--101},
publisher = {AUAI Press},
url = {https://arxiv.org/abs/1210.4843},
pdf = {research/arora12deterministic.pdf}
}
@inproceedings{shukla12parallelizing,
author = {Shilpa Shukla and Matthew Lease and Ambuj Tewari},
title = {Parallelizing {L}ist{N}et Training using {S}park},
booktitle = {Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1127--1128},
year = {2012},
url = {http://dx.doi.org/10.1145/2348283.2348502},
pdf = {research/shukla12parallelizing.pdf}
}
@inproceedings{arora12online,
author = {Raman Arora and Ofer Dekel and Ambuj Tewari},
title = {Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {1503--1510},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042796},
pdf = {research/arora12online.pdf}
}
@inproceedings{kalyanakrishnan12pac,
author = {Shivaram Kalyanakrishnan and Ambuj Tewari and Peter Auer and Peter Stone},
title = {{PAC} Subset Selection in Stochastic Multi-armed Bandits},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {655--662},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042606},
pdf = {research/kalyanakrishnan12pac.pdf}
}
@inproceedings{scherrer12scaling,
author = {Chad Scherrer and Mahantesh Halappanavar and Ambuj Tewari and David Haglin},
title = {Scaling Up Coordinate Descent Algorithms for Large $l_1$ Regularization Problems},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {1407--1414},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042622},
pdf = {research/scherrer12scaling.pdf}
}
@article{kakade12regularization,
author = {Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari},
title = {Regularization Techniques for Learning with Matrices},
month = {June},
year = {2012},
journal = {Journal of Machine Learning Research},
volume = {13},
pages = {1865--1890},
pdf = {research/kakade12regularization.pdf},
url = {http://jmlr.csail.mit.edu/papers/v13/kakade12a.html}
}
@inproceedings{yang12perturbation,
author = {Eunho Yang and Ambuj Tewari and Pradeep Ravikumar},
title = {Perturbation based Large Margin Approach for Ranking},
booktitle = {Proceedings of the 15th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {22},
year = {2012},
pages = {1358--1366},
pdf = {research/yang12perturbation.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v22/}
}
@inproceedings{srebro11universality,
author = {Nathan Srebro and Karthik Sridharan and Ambuj Tewari},
title = {On the Universality of Online Mirror Descent},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {2645--2653},
note = {longer version available as arXiv:1107.4080},
pdf = {research/srebro11universality.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{jain11orthogonal,
author = {Prateek Jain and Ambuj Tewari and Inderjit S. Dhillon},
title = {Orthogonal Matching Pursuit with Replacement},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {1215--1223},
note = {longer version available as arXiv:1106.2774},
pdf = {research/jain11orthogonal.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{tewari11greedy,
author = {Ambuj Tewari and Pradeep Ravikumar and Inderjit S. Dhillon},
title = {Greedy Algorithms for Structurally Constrained High Dimensional Problems},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {882--890},
pdf = {research/tewari11greedy.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{dhillon11nearest,
author = {Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Nearest Neighbor based Greedy Coordinate Descent},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {2160--2168},
pdf = {research/dhillon11nearest.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{rakhlin11stochastic,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Stochastic, Constrained, and Smoothed Adversaries},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {1764--1772},
note = {longer (but older) version available as arXiv:1104.5070},
pdf = {research/rakhlin11stochastic.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{chiang11exploiting,
author = {Kai-Yang Chiang and Nagarajan Natarajan and Ambuj Tewari and Inderjit S. Dhillon},
title = {Exploiting Longer Cycles for Link Prediction in Signed Networks},
booktitle = {Proceedings of the 20th ACM Conference on Information and Knowledge Management},
year = {2011},
pages = {1157--1162},
pdf = {research/chiang11exploiting.pdf},
url = {http://dx.doi.org/10.1145/2063576.2063742}
}
@article{shalev-shwartz11stochastic,
author = {Shai Shalev-Shwartz and Ambuj Tewari},
title = {Stochastic Methods for $l_1$ Regularized Loss Minimization},
journal = {Journal of Machine Learning Research},
volume = {12},
month = {June},
pages = {1865--1892},
year = {2011},
pdf = {research/shalev-shwartz11stochastic.pdf},
url = {http://jmlr.csail.mit.edu/papers/v12/shalev-shwartz11a.html}
}
@inproceedings{rakhlin11online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Beyond Regret},
year = {2011},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
series = {JMLR Workshop and Conference Proceedings},
volume = {19},
pages = {559--594},
note = {Best Paper Award, longer version available as arXiv:1011.3168},
pdf = {research/rakhlin11online.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v19/}
}
@inproceedings{foster11complexity-based,
author = {Dean Foster and Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Complexity-Based Approach to Calibration with Checking Rules},
year = {2011},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
series = {JMLR Workshop and Conference Proceedings},
volume = {19},
pages = {293--314},
pdf = {research/foster11complexity-based.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v19/}
}
@inproceedings{ravikumar11ndcg,
author = {Pradeep Ravikumar and Ambuj Tewari and Eunho Yang},
title = {On {NDCG} Consistency of Listwise Ranking Methods},
booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {15},
year = {2011},
pages = {618--626},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v15/},
pdf = {research/ravikumar11ndcg.pdf}
}
@inproceedings{saha11improved,
author = {Ankan Saha and Ambuj Tewari},
title = {Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback},
booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {15},
year = {2011},
pages = {636-642},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v15/},
pdf = {research/saha11improved.pdf}
}
@inproceedings{rakhlin10online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Random Averages, Combinatorial Parameters, and Learnability},
year = {2010},
booktitle = {Advances in Neural Information Processing Systems 23},
pages = {1984--1992},
pdf = {research/rakhlin10online.pdf},
url = {https://proceedings.neurips.cc/paper/2010}
}
@inproceedings{srebro10smoothness,
author = {Nathan Srebro and Karthik Sridharan and Ambuj Tewari},
title = {Smoothness, Low Noise, and Fast Rates},
year = {2010},
booktitle = {Advances in Neural Information Processing Systems 23},
pages = {2199--2207},
pdf = {research/srebro10smoothness.pdf},
url = {https://proceedings.neurips.cc/paper/2010}
}
@inproceedings{duchi10composite,
author = {John Duchi and Shai Shalev-Shwartz and Yoram Singer and Ambuj Tewari},
title = {Composite Objective Mirror Descent},
year = {2010},
booktitle = {Proceedings of the 23rd Annual Conference on Learning Theory},
pages = {14--26},
publisher = {Omnipress},
url = {http://www.learningtheory.org/colt2010/conference-website/proceedings.html},
pdf = {research/duchi10composite.pdf}
}
@inproceedings{sridharan10convex,
author = {Karthik Sridharan and Ambuj Tewari},
title = {Convex Games in {B}anach Spaces},
year = {2010},
booktitle = {Proceedings of the 23rd Annual Conference on Learning Theory},
pages = {1--13},
publisher = {Omnipress},
url = {http://www.learningtheory.org/colt2010/conference-website/proceedings.html},
pdf = {research/sridharan10convex.pdf}
}
@inproceedings{kakade10learning,
author = {Sham M. Kakade and Ohad Shamir and Karthik Sridharan and Ambuj Tewari},
title = {Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity},
booktitle = {Proceedings of the 13th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {9},
year = {2010},
pages = {381--388},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v9/},
pdf = {research/kakade10learning.pdf}
}
@inproceedings{bartlett09regal,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {{REGAL}: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating {MDP}s},
booktitle = {Proceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {35--42},
publisher = {AUAI Press},
url = {http://www.auai.org/uai2009/papers/UAI2009_0115_adc2f3cd89e91f1bb9535973523e7dae.pdf},
pdf = {research/bartlett09regal.pdf}
}
@inproceedings{shalev-shwartz09stochastic,
author = {Shai Shalev-Shwartz and Ambuj Tewari},
title = {Stochastic Methods for $l_1$ Regularized Loss Minimization},
booktitle = {Proceedings of the 26th International Conference on Machine Learning},
pages = {929--936},
year = {2009},
publisher = {ACM Press},
url = {http://doi.acm.org/10.1145/1553374.1553493},
pdf = {research/shalev-shwartz09stochastic.pdf}
}
@inproceedings{kakade09generalization,
author = {Sham M. Kakade and Ambuj Tewari},
title = {On the Generalization Ability of Online Strongly Convex Programming Algorithms},
booktitle = {Advances in Neural Information Processing Systems 21},
pages = {801--808},
year = {2009},
publisher = {MIT Press},
url = {https://proceedings.neurips.cc/paper/2009},
pdf = {research/kakade09generalization.pdf}
}
@inproceedings{kakade09complexity,
author = {Sham M. Kakade and Karthik Sridharan and Ambuj Tewari},
title = {On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization},
booktitle = {Advances in Neural Information Processing Systems 21},
pages = {793--800},
year = {2009},
publisher = {MIT Press},
url = {https://proceedings.neurips.cc/paper/2009},
pdf = {research/kakade09complexity.pdf}
}
@inproceedings{bartlett08high-probability,
author = {Peter L. Bartlett and Varsha Dani and Thomas P. Hayes and Sham M. Kakade and Alexander Rakhlin and Ambuj Tewari},
title = {High-probability Regret Bounds for Bandit Online Linear Optimization},
booktitle = {Proceedings of the 21st Annual Conference on Learning Theory},
pages = {335--342},
year = {2008},
publisher = {Omnipress},
url = {http://colt2008.cs.helsinki.fi/programme.shtml},
pdf = {research/bartlett08high-probability.pdf}
}
@inproceedings{abernethy08optimal,
author = {Jacob Abernethy and Peter L. Bartlett and Alexander Rakhlin and Ambuj Tewari},
title = {Optimal Strategies and Minimax Lower Bounds for Online Convex Games},
booktitle = {Proceedings of the 21st Annual Conference on Learning Theory},
pages = {414--424},
year = {2008},
publisher = {Omnipress},
url = {http://colt2008.cs.helsinki.fi/programme.shtml},
pdf = {research/abernethy08optimal.pdf}
}
@inproceedings{kakade08efficient,
author = {Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari},
title = {Efficient Bandit Algorithms for Online Multiclass Prediction},
booktitle = {Proceedings of the 25th International Conference on Machine Learning},
year = {2008},
pages = {440--447},
publisher = {ACM Press},
url = {http://doi.acm.org/10.1145/1390156.1390212},
pdf = {research/kakade08efficient.pdf}
}
@inproceedings{tewari08optimistic,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Optimistic Linear Programming gives Logarithmic Regret for Irreducible {MDPs}},
booktitle = {Advances in Neural Information Processing Systems 20},
year = {2008},
publisher = {MIT Press},
pages = {1505--1512},
url = {https://proceedings.neurips.cc/paper/2008},
pdf = {research/tewari08optimistic.pdf}
}
@inproceedings{tewari07bounded,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Bounded Parameter {M}arkov Decision Processes with Average Reward Criterion},
booktitle = {Proceedings of the 20th Annual Conference on Learning Theory},
year = {2007},
pages = {263--277},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {4539},
pdf = {research/tewari07bounded.pdf},
url = {http://dx.doi.org/10.1007/978-3-540-72927-3_20}
}
@article{tewari07consistency,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {On the Consistency of Multiclass CLassification Methods},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {8},
month = {May},
pages = {1007--1025},
note = {(Invited paper)},
pdf = {research/tewari07consistency.pdf},
url = {http://jmlr.csail.mit.edu/papers/v8/tewari07a.html}
}
@article{bartlett07sparseness,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {8},
month = {Apr},
pages = {775--790},
pdf = {research/bartlett07sparseness.pdf},
url = {http://jmlr.csail.mit.edu/papers/v8/bartlett07a.html}
}
@inproceedings{bartlett07sample,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sample Complexity of Policy Search with Known Dynamics},
year = {2007},
pages = {97--104},
booktitle = {Advances in Neural Information Processing Systems 19},
publisher = {MIT Press},
pdf = {research/bartlett07sample.pdf},
url = {https://proceedings.neurips.cc/paper/2007}
}
@inproceedings{tewari05consistency,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {On the Consistency of Multiclass Classification Methods},
booktitle = {Proceedings of the 18th Annual Conference on Learning Theory},
year = {2005},
pages = {147--153},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {3559},
note = {Student Paper Award},
pdf = {research/tewari05consistency.pdf},
url = {http://dx.doi.org/10.1007/11503415_10}
}
@inproceedings{bartlett04sparseness,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sparseness versus Estimating Conditional Probabilities: Some Asymptotic Results},
booktitle = {Proceedings of the 17th Annual Conference on Learning Theory},
year = {2004},
pages = {564--578},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {3120},
pdf = {research/bartlett04sparseness.pdf},
url = {https://doi.org/10.1007/978-3-540-27819-1_39}
}
@inproceedings{tewari02parallel,
author = {Ambuj Tewari and Utkarsh Srivastava and Phalguni Gupta},
title = {A Parallel {DFA} Minimization Algorithm},
booktitle = {Proceedings of the 9th International Conference on High Performance Computing},
year = {2002},
pages = {34--40},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {2552},
pdf = {research/tewari02parallel.pdf},
url = {https://doi.org/10.1007/3-540-36265-7_4}
}
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