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ORCIDLillian J. Ratliff
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2020 – today
- 2024
[c62]Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel:
Emergent specialization from participation dynamics and multi-learner retraining. AISTATS 2024: 343-351- [c61]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. AISTATS 2024: 2602-2610 - [c60]Pranoy Das, Benita Nortmann, Lillian J. Ratliff, Vijay Gupta, Thulasi Mylvaganam:
Learning in Stochastic Stackelberg Games. ACC 2024: 3557-3562 - [i62]Heling Zhang, Lillian J. Ratliff, Roy Dong:
Distribution-Free Guarantees for Systems with Decision-Dependent Noise. CoRR abs/2403.01072 (2024) - [i61]Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin G. Jamieson:
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. CoRR abs/2406.06856 (2024) - [i60]Jason T. Isa, Bohan Wu, Qirui Wang, Yilin Zhang, Samuel A. Burden, Lillian J. Ratliff, Benjamin J. Chasnov:
Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game. CoRR abs/2408.14640 (2024) - 2023
- [j18]Sarah H. Q. Li, Yue Yu
, Nicolas I. Miguel, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Adaptive constraint satisfaction for Markov decision process congestion games: Application to transportation networks. Autom. 151: 110879 (2023) - [j17]Daniel J. Calderone, Benjamin J. Chasnov, Samuel A. Burden, Lillian J. Ratliff:
Consistent Conjectural Variations Equilibria: Characterization and Stability for a Class of Continuous Games. IEEE Control. Syst. Lett. 7: 2743-2748 (2023) - [j16]Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Multiplayer Performative Prediction: Learning in Decision-Dependent Games. J. Mach. Learn. Res. 24: 202:1-202:56 (2023) - [c59]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. AISTATS 2023: 9429-9469 - [c58]Roy Dong, Heling Zhang, Lillian J. Ratliff:
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions. AISTATS 2023: 11172-11184 - [c57]Boling Yang, Liyuan Zheng, Lillian J. Ratliff, Byron Boots, Joshua R. Smith:
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula. ICRA 2023: 5501-5507 - [c56]Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff:
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. NeurIPS 2023 - [i59]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) - [i58]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. CoRR abs/2303.10565 (2023) - [i57]Benjamin J. Chasnov, Lillian J. Ratliff, Samuel A. Burden:
Human adaptation to adaptive machines converges to game-theoretic equilibria. CoRR abs/2305.01124 (2023) - [i56]Boling Yang, Liyuan Zheng, Lillian J. Ratliff, Byron Boots, Joshua R. Smith:
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula. CoRR abs/2305.03735 (2023) - [i55]Daniel J. Calderone, Benjamin J. Chasnov, Samuel A. Burden, Lillian J. Ratliff:
Consistent Conjectural Variations Equilibrium: Characterization & Stability for a Class of Continuous Games. CoRR abs/2305.11866 (2023) - [i54]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback. CoRR abs/2306.13233 (2023) - [i53]Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff:
Coupled Gradient Flows for Strategic Non-Local Distribution Shift. CoRR abs/2307.01166 (2023) - [i52]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Query-Efficient Algorithms to Find the Unique Nash Equilibrium in a Two-Player Zero-Sum Matrix Game. CoRR abs/2310.16236 (2023) - [i51]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. CoRR abs/2310.16252 (2023) - [i50]Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern, Sarah Dean, Lillian J. Ratliff, Maryam Fazel:
Initializing Services in Interactive ML Systems for Diverse Users. CoRR abs/2312.11846 (2023) - 2022
- [j15]Yue Yu, Daniel J. Calderone, Sarah H. Q. Li, Lillian J. Ratliff, Behçet Açikmese:
Variable demand and multi-commodity flow in Markovian network equilibrium. Autom. 140: 110224 (2022) - [j14]Esther Ling, Liyuan Zheng, Lillian J. Ratliff, Samuel Coogan:
Koopman Operator Applications in Signalized Traffic Systems. IEEE Trans. Intell. Transp. Syst. 23(4): 3214-3225 (2022) - [c55]Mitas Ray, Lillian J. Ratliff, Dmitriy Drusvyatskiy, Maryam Fazel:
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments. AAAI 2022: 8081-8088 - [c54]Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff:
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms. AAAI 2022: 9217-9224 - [c53]Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Learning in Stochastic Monotone Games with Decision-Dependent Data. AISTATS 2022: 5891-5912 - [c52]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 - [c51]Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games∗. CDC 2022: 3403-3408 - [c50]Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff:
Minimax Optimization with Smooth Algorithmic Adversaries. ICLR 2022 - [c49]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS 2022 - [c48]Georgios Piliouras, Lillian J. Ratliff, Ryann Sim, Stratis Skoulakis:
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games. SAGT 2022: 383-399 - [i49]Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Multiplayer Performative Prediction: Learning in Decision-Dependent Games. CoRR abs/2201.03398 (2022) - [i48]Mitas Ray, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments. CoRR abs/2204.08281 (2022) - [i47]Sarah H. Q. Li, Lillian J. Ratliff, Peeyush Kumar:
General sum stochastic games with networked information flows. CoRR abs/2205.02760 (2022) - [i46]Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel:
Multi-learner risk reduction under endogenous participation dynamics. CoRR abs/2206.02667 (2022) - [i45]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR abs/2207.02357 (2022) - [i44]Adhyyan Narang, Omid Sadeghi, Lillian J. Ratliff, Maryam Fazel, Jeff A. Bilmes:
Interactive Combinatorial Bandits: Balancing Competitivity and Complementarity. CoRR abs/2207.03091 (2022) - [i43]Georgios Piliouras, Lillian J. Ratliff, Ryann Sim, Stratis Skoulakis:
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games. CoRR abs/2207.08426 (2022) - 2021
- [j13]Sarah H. Q. Li, Lillian J. Ratliff, Behçet Açikmese:
Disturbance Decoupling for Gradient-Based Multi-Agent Learning With Quadratic Costs. IEEE Control. Syst. Lett. 5(1): 223-228 (2021) - [j12]Lillian J. Ratliff, Tanner Fiez:
Adaptive Incentive Design. IEEE Trans. Autom. Control. 66(8): 3871-3878 (2021) - [c47]Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian J. Ratliff:
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games. AAAI 2021: 11343-11351 - [c46]Tanner Fiez, Lillian J. Ratliff:
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation. ICLR 2021 - [c45]Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang:
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks. L4DC 2021: 336-347 - [c44]Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian J. Ratliff:
Online Learning in Periodic Zero-Sum Games. NeurIPS 2021: 10313-10325 - [c43]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 - [i42]Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff:
Minimax Optimization with Smooth Algorithmic Adversaries. CoRR abs/2106.01488 (2021) - [i41]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) - [i40]Roy Dong, Lillian J. Ratliff:
Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions. CoRR abs/2107.00055 (2021) - [i39]Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff:
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms. CoRR abs/2109.12286 (2021) - [i38]Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian J. Ratliff:
Online Learning in Periodic Zero-Sum Games. CoRR abs/2111.03377 (2021) - [i37]Dmitriy Drusvyatskiy, Lillian J. Ratliff:
Improved rates for derivative free play in convex games. CoRR abs/2111.09456 (2021) - 2020
- [j11]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) - [j10]Lillian J. Ratliff, Eric Mazumdar:
Inverse Risk-Sensitive Reinforcement Learning. IEEE Trans. Autom. Control. 65(3): 1256-1263 (2020) - [j9]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Competitive Statistical Estimation With Strategic Data Sources. IEEE Trans. Autom. Control. 65(4): 1537-1551 (2020) - [j8]Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff, Baosen Zhang:
Uncertainty in Multicommodity Routing Networks: When Does It Help? IEEE Trans. Autom. Control. 65(11): 4600-4615 (2020) - [j7]Chase P. Dowling, Lillian J. Ratliff, Baosen Zhang:
Modeling Curbside Parking as a Network of Finite Capacity Queues. IEEE Trans. Intell. Transp. Syst. 21(3): 1011-1022 (2020) - [j6]Tanner Fiez, Lillian J. Ratliff:
Gaussian Mixture Models for Parking Demand Data. IEEE Trans. Intell. Transp. Syst. 21(8): 3571-3580 (2020) - [c42]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 - [c41]Benjamin J. Chasnov, Daniel J. Calderone, Behçet Açikmese, Samuel A. Burden, Lillian J. Ratliff:
Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces. CDC 2020: 3543-3548 - [c40]Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff:
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study. ICML 2020: 3133-3144 - [c39]Liyuan Zheng, Lillian J. Ratliff:
Constrained Upper Confidence Reinforcement Learning. L4DC 2020: 620-629 - [c38]Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff:
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. UAI 2020: 580-589 - [i36]Liyuan Zheng, Lillian J. Ratliff:
Constrained Upper Confidence Reinforcement Learning. CoRR abs/2001.09377 (2020) - [i35]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) - [i34]Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang:
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks. CoRR abs/2003.09488 (2020) - [i33]Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff:
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. CoRR abs/2007.07079 (2020) - [i32]Sarah H. Q. Li, Lillian J. Ratliff, Behçet Açikmese:
Disturbance Decoupling for Gradient-based Multi-Agent Learning with Quadratic Costs. CoRR abs/2007.07228 (2020) - [i31]Tanner Fiez, Lillian J. Ratliff:
Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation. CoRR abs/2009.14820 (2020) - [i30]Benjamin J. Chasnov, Daniel J. Calderone, Behçet Açikmese, Samuel A. Burden, Lillian J. Ratliff:
Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces. CoRR abs/2011.03650 (2020) - [i29]Benjamin J. Chasnov, Daniel J. Calderone, Behçet Açikmese, Samuel A. Burden, Lillian J. Ratliff:
Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces. CoRR abs/2011.05562 (2020) - [i28]Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian J. Ratliff:
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games. CoRR abs/2012.08382 (2020) - [i27]Mitas Ray, Omid Sadeghi, Lillian J. Ratliff, Maryam Fazel:
Function Design for Improved Competitive Ratio in Online Resource Allocation with Procurement Costs. CoRR abs/2012.12457 (2020)
2010 – 2019
- 2019
- [j5]Lillian J. Ratliff, Roy Dong, Shreyas Sekar, Tanner Fiez:
A Perspective on Incentive Design: Challenges and Opportunities. Annu. Rev. Control. Robotics Auton. Syst. 2: 305-338 (2019) - [c37]Sarah H. Q. Li, Yue Yu, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games. ACC 2019: 1238-1243 - [c36]Sarah H. Q. Li, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Sensitivity Analysis for Markov Decision Process Congestion Games. CDC 2019: 1301-1306 - [c35]Yagiz Savas, Vijay Gupta, Melkior Ornik, Lillian J. Ratliff, Ufuk Topcu:
Incentive Design for Temporal Logic Objectives. CDC 2019: 2251-2258 - [c34]Daniel J. Calderone, Lillian J. Ratliff:
Multi-Dimensional Continuous Type Population Potential Games. CDC 2019: 5138-5143 - [c33]Shahriar Talebi, Siavash Alemzadeh, Lillian J. Ratliff, Mehran Mesbahi:
Distributed Learning in Network Games: a Dual Averaging Approach. CDC 2019: 5544-5549 - [c32]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 - [c31]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. NeurIPS 2019: 10666-10676 - [c30]Chinmaya Samal, Abhishek Dubey, Lillian J. Ratliff:
Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies. SMARTCOMP 2019: 283-291 - [c29]Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden:
Convergence Analysis of Gradient-Based Learning in Continuous Games. UAI 2019: 935-944 - [i26]Sarah H. Q. Li, Yue Yu, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games. CoRR abs/1903.00747 (2019) - [i25]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Competitive Statistical Estimation with Strategic Data Sources. CoRR abs/1904.12768 (2019) - [i24]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) - [i23]Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff:
Convergence of Learning Dynamics in Stackelberg Games. CoRR abs/1906.01217 (2019) - [i22]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. CoRR abs/1906.08399 (2019) - [i21]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) - [i20]Sarah H. Q. Li, Yue Yu, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Online Constraint Satisfaction via Tolls in MDP Congestion Games. CoRR abs/1907.08912 (2019) - [i19]Sarah H. Q. Li, Daniel J. Calderone, Lillian J. Ratliff, Behçet Açikmese:
Sensitivity Analysis for Markov Decision Process Congestion Games. CoRR abs/1909.04167 (2019) - [i18]Jingjing Bu, Lillian J. Ratliff, Mehran Mesbahi:
Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games. CoRR abs/1911.04672 (2019) - 2018
- [j4]Roy Dong, Lillian J. Ratliff, Alvaro A. Cárdenas, Henrik Ohlsson, S. Shankar Sastry:
Quantifying the Utility-Privacy Tradeoff in the Internet of Things. ACM Trans. Cyber Phys. Syst. 2(2): 8:1-8:28 (2018) - [j3]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, S. Shankar Sastry, Costas J. Spanos:
A Robust Utility Learning Framework via Inverse Optimization. IEEE Trans. Control. Syst. Technol. 26(3): 954-970 (2018) - [c28]Tanner Fiez, Lillian J. Ratliff, Chase Dowling, Baosen Zhang:
Data Driven Spatio-Temporal Modeling of Parking Demand. ACC 2018: 2757-2762 - [c27]Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff, Baosen Zhang:
Uncertainty in Multi-Commodity Routing Networks: When does it help? ACC 2018: 6553-6558 - [c26]Esther Ling, Lillian J. Ratliff, Samuel Coogan:
Koopman Operator Approach for Instability Detection and Mitigation in Signalized Traffic. ITSC 2018: 1297-1302 - [c25]Chinmaya Samal, Abhishek Dubey, Lillian J. Ratliff:
Mobilytics- An Extensible, Modular and Resilient Mobility Platform. SMARTCOMP 2018: 356-361 - [c24]Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff:
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences. UAI 2018: 693-703 - [i17]Chinmaya Samal, Liyuan Zheng, Fangzhou Sun, Lillian J. Ratliff, Abhishek Dubey:
Towards a Socially Optimal Multi-Modal Routing Platform. CoRR abs/1802.10140 (2018) - [i16]Lillian J. Ratliff, Shreyas Sekar, Liyuan Zheng, Tanner Fiez:
Incentives in the Dark: Multi-armed Bandits for Evolving Users with Unknown Type. CoRR abs/1803.04008 (2018) - [i15]Eric Mazumdar, Lillian J. Ratliff:
On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. CoRR abs/1804.05464 (2018) - [i14]Lillian J. Ratliff, Tanner Fiez:
Adaptive Incentive Design. CoRR abs/1806.05749 (2018) - [i13]Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff:
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences. CoRR abs/1807.02297 (2018) - 2017
- [c23]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, Costas J. Spanos:
Leveraging correlations in utility learning. ACC 2017: 5249-5256 - [c22]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Statistical estimation with strategic data sources in competitive settings. CDC 2017: 4994-4999 - [c21]Chase Dowling, Tanner Fiez, Lillian J. Ratliff, Baosen Zhang:
Optimizing curbside parking resources subject to congestion constraints. CDC 2017: 5080-5085 - [c20]Kamil Nar, Lillian J. Ratliff, Shankar Sastry:
Learning prospect theory value function and reference point of a sequential decision maker. CDC 2017: 5770-5775 - [c19]Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry:
Gradient-based inverse risk-sensitive reinforcement learning. CDC 2017: 5796-5801 - [i12]Chase Dowling, Tanner Fiez, Lillian J. Ratliff, Baosen Zhang:
How Much Urban Traffic is Searching for Parking? CoRR abs/1702.06156 (2017) - [i11]Lillian J. Ratliff, Eric Mazumdar:
Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. CoRR abs/1703.09842 (2017) - [i10]Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry:
Statistical Estimation with Strategic Data Sources in Competitive Settings. CoRR abs/1704.01195 (2017) - [i9]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, S. Shankar Sastry, Costas J. Spanos:
A Robust Utility Learning Framework via Inverse Optimization. CoRR abs/1704.07933 (2017) - [i8]Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff, Baosen Zhang:
Uncertainty in Multi-Commodity Routing Networks: When does it help? CoRR abs/1709.08441 (2017) - 2016
- [j2]Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry:
On the Characterization of Local Nash Equilibria in Continuous Games. IEEE Trans. Autom. Control. 61(8): 2301-2307 (2016) - [c18]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 - [c17]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, Costas J. Spanos, S. Shankar Sastry:
Inverse modeling of non-cooperative agents via mixture of utilities. CDC 2016: 6327-6334 - [c16]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 - [i7]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) - 2015
- [c15]Dexter Scobee, Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, Michel Verhaegen, S. Shankar Sastry:
Nuclear norm minimization for blind subspace identification (N2BSID). CDC 2015: 2127-2132 - [c14]Daniel J. Calderone, Lillian J. Ratliff, S. Shankar Sastry:
Lane pricing via decision-theoretic lane changing model of driver behavior. CDC 2015: 3457-3462 - [c13]Ming Jin, Lillian J. Ratliff, Ioannis C. Konstantakopoulos, Costas J. Spanos, Shankar Sastry:
REST: a reliable estimation of stopping time algorithm for social game experiments. ICCPS 2015: 90-99 - 2014
- [j1]Pushkin Kachroo, Lillian J. Ratliff, Shankar Sastry:
Analysis of the Godunov-Based Hybrid Model for Ramp Metering and Robust Feedback Control Design. IEEE Trans. Intell. Transp. Syst. 15(5): 2132-2142 (2014) - [c12]Lillian J. Ratliff, Ming Jin, Ioannis C. Konstantakopoulos, Costas J. Spanos, S. Shankar Sastry:
Social game for building energy efficiency: Incentive design. Allerton 2014: 1011-1018 - [c11]Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry:
Genericity and structural stability of non-degenerate differential Nash equilibria. ACC 2014: 3990-3995 - [c10]Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, Alvaro A. Cárdenas, S. Shankar Sastry:
Privacy and customer segmentation in the smart grid. CDC 2014: 2136-2141 - [c9]Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry:
Fundamental limits of nonintrusive load monitoring. HiCoNS 2014: 11-18 - [c8]Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, S. Shankar Sastry:
Energy efficiency via incentive design and utility learning. HiCoNS 2014: 57-58 - [i6]Roy Dong, Alvaro A. Cárdenas, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry:
Quantifying the Utility-Privacy Tradeoff in the Smart Grid. CoRR abs/1406.2568 (2014) - [i5]Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin, S. Shankar Sastry, Costas J. Spanos:
Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis. CoRR abs/1407.0727 (2014) - [i4]Lillian J. Ratliff, Carlos A. Barreto, Roy Dong, Henrik Ohlsson, Alvaro A. Cárdenas, S. Shankar Sastry:
Effects of Risk on Privacy Contracts for Demand-Side Management. CoRR abs/1409.7926 (2014) - 2013
- [c7]Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry:
Energy disaggregation via adaptive filtering. Allerton 2013: 173-180 - [c6]Lillian J. Ratliff, Samuel Burden, S. Shankar Sastry:
Characterization and computation of local Nash equilibria in continuous games. Allerton 2013: 917-924 - [c5]Samuel Coogan, Lillian J. Ratliff, Daniel J. Calderone, Claire J. Tomlin, S. Shankar Sastry:
Energy management via pricing in LQ dynamic games. ACC 2013: 443-448 - [c4]Daniel J. Calderone, Lillian J. Ratliff, S. Shankar Sastry:
Pricing design for robustness in linear quadratic games. CDC 2013: 4349-4354 - [c3]Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry:
A dynamical systems approach to energy disaggregation. CDC 2013: 6335-6340 - [c2]Aaron M. Bestick, Lillian J. Ratliff, Posu Yan, Ruzena Bajcsy, S. Shankar Sastry:
An inverse correlated equilibrium framework for utility learning in multiplayer, noncooperative settings. HiCoNS 2013: 9-16 - [i3]Henrik Ohlsson, Lillian J. Ratliff, Roy Dong, S. Shankar Sastry:
Blind Identification of ARX Models with Piecewise Constant Inputs. CoRR abs/1303.6719 (2013) - [i2]Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, S. Shankar Sastry:
Behavior Modification and Utility Learning via Energy Disaggregation. CoRR abs/1312.1394 (2013) - [i1]Henrik Ohlsson, Lillian J. Ratliff, Roy Dong, S. Shankar Sastry:
Blind Identification via Lifting. CoRR abs/1312.2060 (2013) - 2012
- [c1]Lillian J. Ratliff, Samuel Coogan, Daniel J. Calderone, S. Shankar Sastry:
Pricing in linear-quadratic dynamic games. Allerton Conference 2012: 1798-1805
Coauthor Index
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