default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNathan Kallus
- > Home > Persons > Nathan Kallus
Publications
- 2025
[j22]Yichun Hu, Nathan Kallus
, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. Math. Oper. Res. 50(1): 633-655 (2025)- 2024
- [j19]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. J. Mach. Learn. Res. 25: 16:1-16:59 (2024) - [c68]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Preference-Based Reinforcement Learning. ICLR 2024 - 2023
- [c53]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT 2023: 2265 - [c52]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. COLT 2023: 2291-2318 - [c49]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML 2023: 34615-34641 - [c47]Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun:
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS 2023 - [c46]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS 2023 - [i74]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i72]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. CoRR abs/2302.05404 (2023) - [i70]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Reinforcement Learning with Human Feedback. CoRR abs/2305.14816 (2023) - [i67]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR abs/2307.13793 (2023) - 2022
- [j13]Nathan Kallus, Masatoshi Uehara:
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. Oper. Res. 70(6): 3282-3302 (2022) - [c37]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i58]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i57]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i55]Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun:
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR abs/2207.13081 (2022) - [i52]Masatoshi Uehara, Chengchun Shi, Nathan Kallus:
A Review of Off-Policy Evaluation in Reinforcement Learning. CoRR abs/2212.06355 (2022) - 2021
- [c35]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. COLT 2021: 2462 - [c33]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. ICML 2021: 5247-5256 - [i51]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. CoRR abs/2102.00479 (2021) - [i50]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i49]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. CoRR abs/2103.14029 (2021) - 2020
- [j5]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. J. Mach. Learn. Res. 21: 167:1-167:63 (2020) - [c25]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. ICML 2020: 5078-5088 - [c24]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. ICML 2020: 5089-5100 - [c23]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. NeurIPS 2020 - [i39]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. CoRR abs/2002.04014 (2020) - [i33]Nathan Kallus, Masatoshi Uehara:
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning. CoRR abs/2006.03886 (2020) - [i32]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. CoRR abs/2006.03900 (2020) - [i29]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. CoRR abs/2010.11002 (2020) - 2019
- [c18]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. NeurIPS 2019: 3320-3329 - [i18]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. CoRR abs/1906.03735 (2019) - [i15]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. CoRR abs/1908.08526 (2019) - [i13]Nathan Kallus, Masatoshi Uehara:
Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. CoRR abs/1909.05850 (2019) - [i12]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. CoRR abs/1912.12945 (2019)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-08-27 19:16 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
