Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDEric Mazumdar
Person information
- affiliation: California Institute of Technology, Pasadena, CA, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2023
[c22]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. ICML 2023: 12570-12586- [c21]Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray:
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows. ICRA 2023: 12430-12436 - [c20]Lauren E. Conger, Sydney Vernon, Eric Mazumdar:
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. L4DC 2023: 420-430 - [i24]Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar:
Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. CoRR abs/2302.01421 (2023) - [i23]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. CoRR abs/2302.04262 (2023) - [i22]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR abs/2303.03100 (2023) - [i21]Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff:
Coupled Gradient Flows for Strategic Non-Local Distribution Shift. CoRR abs/2307.01166 (2023) - 2022
- [c19]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c18]Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff:
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. AISTATS 2022: 6702-6734 - [c17]Lauren E. Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton:
Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC 2022: 3846-3852 - [c16]Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar:
Langevin Monte Carlo for Contextual Bandits. ICML 2022: 24830-24850 - [c15]Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. NeurIPS 2022 - [i20]Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. CoRR abs/2206.02344 (2022) - [i19]Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar:
Langevin Monte Carlo for Contextual Bandits. CoRR abs/2206.11254 (2022) - [i18]Tijana Zrnic, Eric Mazumdar:
A Note on Zeroth-Order Optimization on the Simplex. CoRR abs/2208.01185 (2022) - [i17]Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray:
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows. CoRR abs/2210.10304 (2022) - 2021
- [c14]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? NeurIPS 2021: 15257-15269 - [c13]Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang:
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. NeurIPS 2021: 29049-29063 - [i16]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i15]Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff:
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization. CoRR abs/2106.09082 (2021) - [i14]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? CoRR abs/2106.12529 (2021) - 2020
- [j2]Eric Mazumdar
, Lillian J. Ratliff, S. Shankar Sastry:
On Gradient-Based Learning in Continuous Games. SIAM J. Math. Data Sci. 2(1): 103-131 (2020) - [j1]Lillian J. Ratliff, Eric Mazumdar:
Inverse Risk-Sensitive Reinforcement Learning. IEEE Trans. Autom. Control. 65(3): 1256-1263 (2020) - [c12]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS 2020: 860-868 - [c11]Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry:
Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CDC 2020: 118-125 - [c10]Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CDC 2020: 3604-3610 - [c9]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c8]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c7]Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin:
Feedback Linearization for Uncertain Systems via Reinforcement Learning. ICRA 2020: 1364-1371 - [i13]Eric Mazumdar, Lillian J. Ratliff:
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CoRR abs/2002.01007 (2020) - [i12]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i11]Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry:
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CoRR abs/2004.02766 (2020) - [i10]Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CoRR abs/2010.15599 (2020)
2010 – 2019
- 2019
- [c6]Eric Mazumdar, Lillian J. Ratliff:
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CDC 2019: 6899-6904 - [c5]Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden:
Convergence Analysis of Gradient-Based Learning in Continuous Games. UAI 2019: 935-944 - [i9]Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry:
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR abs/1901.00838 (2019) - [i8]Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden:
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings. CoRR abs/1906.00731 (2019) - [i7]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR abs/1907.03712 (2019) - [i6]Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin:
Feedback Linearization for Unknown Systems via Reinforcement Learning. CoRR abs/1910.13272 (2019) - 2018
- [c4]Margaret P. Chapman, Eric V. Mazumdar, Ellen M. Langer, Rosalie C. Sears, Claire J. Tomlin:
On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems. CDC 2018: 3503-3509 - [i5]Eric Mazumdar, Lillian J. Ratliff:
On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. CoRR abs/1804.05464 (2018) - 2017
- [c3]Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry:
Gradient-based inverse risk-sensitive reinforcement learning. CDC 2017: 5796-5801 - [i4]Roy Dong, Eric Mazumdar, S. Shankar Sastry:
Optimal Causal Imputation for Control. CoRR abs/1703.07049 (2017) - [i3]Lillian J. Ratliff, Eric Mazumdar:
Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. CoRR abs/1703.09842 (2017) - [i2]Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire J. Tomlin, S. Shankar Sastry:
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes. CoRR abs/1707.05714 (2017) - 2016
- [c2]Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang:
To observe or not to observe: Queuing game framework for urban parking. CDC 2016: 5286-5291 - [c1]Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry:
Understanding the impact of parking on urban mobility via routing games on queue-flow networks. CDC 2016: 7605-7610 - [i1]Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang:
To Observe or Not to Observe: Queuing Game Framework for Urban Parking. CoRR abs/1603.08995 (2016)
Coauthor Index
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 2023-09-27 22:54 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:
