Pricing in linear-quadratic dynamic games. - Lillian J. Ratliff; Samuel Coogan 001; Daniel J. Calderone; S. Shankar Sastry (Allerton Conference, 2012)

Energy disaggregation via adaptive filtering. - Roy Dong; Lillian J. Ratliff; Henrik Ohlsson; S. Shankar Sastry (Allerton, 2013)

Characterization and computation of local Nash equilibria in continuous games. - Lillian J. Ratliff; Samuel Burden; S. Shankar Sastry (Allerton, 2013)

Energy management via pricing in LQ dynamic games. - Samuel Coogan 001; Lillian J. Ratliff; Daniel J. Calderone; Claire J. Tomlin; S. Shankar Sastry (ACC, 2013)

Pricing design for robustness in linear quadratic games. - Daniel J. Calderone; Lillian J. Ratliff; S. Shankar Sastry (CDC, 2013)

A dynamical systems approach to energy disaggregation. - Roy Dong; Lillian J. Ratliff; Henrik Ohlsson; S. Shankar Sastry (CDC, 2013)

An inverse correlated equilibrium framework for utility learning in multiplayer, noncooperative settings. - Aaron M. Bestick; Lillian J. Ratliff; Posu Yan; Ruzena Bajcsy; S. Shankar Sastry (HiCoNS, 2013)

Blind Identification via Lifting. - Henrik Ohlsson; Lillian J. Ratliff; Roy Dong; S. Shankar Sastry (CoRR, 2013)

Behavior Modification and Utility Learning via Energy Disaggregation. - Lillian J. Ratliff; Roy Dong; Henrik Ohlsson; S. Shankar Sastry (CoRR, 2013)

Blind Identification of ARX Models with Piecewise Constant Inputs - Henrik Ohlsson; Lillian J. Ratliff; Roy Dong; S. Shankar Sastry (CoRR, 2013)

Social game for building energy efficiency: Incentive design. - Lillian J. Ratliff; Ming Jin 002; Ioannis C. Konstantakopoulos; Costas J. Spanos; S. Shankar Sastry (Allerton, 2014)

Genericity and structural stability of non-degenerate differential Nash equilibria. - Lillian J. Ratliff; Samuel A. Burden; S. Shankar Sastry (ACC, 2014)

Privacy and customer segmentation in the smart grid. - Lillian J. Ratliff; Roy Dong; Henrik Ohlsson; Alvaro A. Cárdenas; S. Shankar Sastry (CDC, 2014)

Fundamental limits of nonintrusive load monitoring. - Roy Dong; Lillian J. Ratliff; Henrik Ohlsson; S. Shankar Sastry (HiCoNS, 2014)

Energy efficiency via incentive design and utility learning. - Lillian J. Ratliff; Roy Dong; Henrik Ohlsson; S. Shankar Sastry (HiCoNS, 2014)

Quantifying the Utility-Privacy Tradeoff in the Smart Grid. - Roy Dong; Alvaro A. Cárdenas; Lillian J. Ratliff; Henrik Ohlsson; S. Shankar Sastry (CoRR, 2014)

Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis. - Ioannis C. Konstantakopoulos; Lillian J. Ratliff; Ming Jin 002; S. Shankar Sastry; Costas J. Spanos (CoRR, 2014)

Effects of Risk on Privacy Contracts for Demand-Side Management. - Lillian J. Ratliff; Carlos A. Barreto; Roy Dong; Henrik Ohlsson; Alvaro A. Cárdenas; S. Shankar Sastry (CoRR, 2014)

Analysis of the Godunov-Based Hybrid Model for Ramp Metering and Robust Feedback Control Design. - Pushkin Kachroo; Lillian J. Ratliff; Shankar Sastry 001 (IEEE Trans. Intell. Transp. Syst., 2014)

Lane pricing via decision-theoretic lane changing model of driver behavior. - Daniel J. Calderone; Lillian J. Ratliff; S. Shankar Sastry (CDC, 2015)

Nuclear norm minimization for blind subspace identification (N2BSID). - Dexter Scobee; Lillian J. Ratliff; Roy Dong; Henrik Ohlsson; Michel Verhaegen; S. Shankar Sastry (CDC, 2015)

REST: a reliable estimation of stopping time algorithm for social game experiments. - Ming Jin 002; Lillian J. Ratliff; Ioannis C. Konstantakopoulos; Costas J. Spanos; Shankar Sastry 001 (ICCPS, 2015)

Understanding the impact of parking on urban mobility via routing games on queue-flow networks. - Daniel J. Calderone; Eric Mazumdar; Lillian J. Ratliff; S. Shankar Sastry (CDC, 2016)

Inverse modeling of non-cooperative agents via mixture of utilities. - Ioannis C. Konstantakopoulos; Lillian J. Ratliff; Ming Jin 002; Costas J. Spanos; S. Shankar Sastry (CDC, 2016)

To observe or not to observe: Queuing game framework for urban parking. - Lillian J. Ratliff; Chase Dowling; Eric Mazumdar; Baosen Zhang (CDC, 2016)

On the Characterization of Local Nash Equilibria in Continuous Games. - Lillian J. Ratliff; Samuel A. Burden; S. Shankar Sastry (IEEE Trans. Autom. Control., 2016)

Leveraging correlations in utility learning. - Ioannis C. Konstantakopoulos; Lillian J. Ratliff; Ming Jin 002; Costas J. Spanos (ACC, 2017)

Optimizing curbside parking resources subject to congestion constraints. - Chase Dowling; Tanner Fiez; Lillian J. Ratliff; Baosen Zhang (CDC, 2017)

Gradient-based inverse risk-sensitive reinforcement learning. - Eric Mazumdar; Lillian J. Ratliff; Tanner Fiez; S. Shankar Sastry (CDC, 2017)

Learning prospect theory value function and reference point of a sequential decision maker. - Kamil Nar; Lillian J. Ratliff; Shankar Sastry 001 (CDC, 2017)

Statistical estimation with strategic data sources in competitive settings. - Tyler Westenbroek; Roy Dong; Lillian J. Ratliff; S. Shankar Sastry (CDC, 2017)

How Much Urban Traffic is Searching for Parking? - Chase Dowling; Tanner Fiez; Lillian J. Ratliff; Baosen Zhang (CoRR, 2017)

Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. - Lillian J. Ratliff; Eric Mazumdar (CoRR, 2017)

Data Driven Spatio-Temporal Modeling of Parking Demand. - Tanner Fiez; Lillian J. Ratliff; Chase Dowling; Baosen Zhang (ACC, 2018)

Uncertainty in Multi-Commodity Routing Networks: When does it help? - Shreyas Sekar; Liyuan Zheng; Lillian J. Ratliff; Baosen Zhang (ACC, 2018)

Koopman Operator Approach for Instability Detection and Mitigation in Signalized Traffic. - Esther Ling; Lillian J. Ratliff; Samuel Coogan 001 (ITSC, 2018)

Mobilytics- An Extensible, Modular and Resilient Mobility Platform. - Chinmaya Samal; Abhishek Dubey; Lillian J. Ratliff (SMARTCOMP, 2018)

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences. - Tanner Fiez; Shreyas Sekar; Liyuan Zheng; Lillian J. Ratliff (UAI, 2018)

Towards a Socially Optimal Multi-Modal Routing Platform. - Chinmaya Samal; Liyuan Zheng; Fangzhou Sun; Lillian J. Ratliff; Abhishek Dubey (CoRR, 2018)

Incentives in the Dark: Multi-armed Bandits for Evolving Users with Unknown Type. - Lillian J. Ratliff; Shreyas Sekar; Liyuan Zheng; Tanner Fiez (CoRR, 2018)

On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. - Eric Mazumdar; Lillian J. Ratliff (CoRR, 2018)

Quantifying the Utility-Privacy Tradeoff in the Internet of Things. - Roy Dong; Lillian J. Ratliff; Alvaro A. Cárdenas; Henrik Ohlsson; S. Shankar Sastry (ACM Trans. Cyber Phys. Syst., 2018)

A Robust Utility Learning Framework via Inverse Optimization. - Ioannis C. Konstantakopoulos; Lillian J. Ratliff; Ming Jin 002; S. Shankar Sastry; Costas J. Spanos (IEEE Trans. Control. Syst. Technol., 2018)

Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games. - Sarah H. Q. Li; Yue Yu 004; Daniel J. Calderone; Lillian J. Ratliff; Behçet Açikmese (ACC, 2019)

Multi-Dimensional Continuous Type Population Potential Games. - Daniel J. Calderone; Lillian J. Ratliff (CDC, 2019)

Sensitivity Analysis for Markov Decision Process Congestion Games. - Sarah H. Q. Li; Daniel J. Calderone; Lillian J. Ratliff; Behçet Açikmese (CDC, 2019)

Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. - Eric Mazumdar; Lillian J. Ratliff (CDC, 2019)

Incentive Design for Temporal Logic Objectives. - Yagiz Savas; Vijay Gupta 001; Melkior Ornik; Lillian J. Ratliff; Ufuk Topcu (CDC, 2019)

Distributed Learning in Network Games: a Dual Averaging Approach. - Shahriar Talebi; Siavash Alemzadeh; Lillian J. Ratliff; Mehran Mesbahi (CDC, 2019)

Sequential Experimental Design for Transductive Linear Bandits. - Tanner Fiez; Lalit Jain; Kevin G. Jamieson; Lillian J. Ratliff (NeurIPS, 2019)

Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies. - Chinmaya Samal; Abhishek Dubey; Lillian J. Ratliff (SMARTCOMP, 2019)

Convergence Analysis of Gradient-Based Learning in Continuous Games. - Benjamin Chasnov; Lillian J. Ratliff; Eric Mazumdar; Samuel Burden (UAI, 2019)

A Perspective on Incentive Design: Challenges and Opportunities. - Lillian J. Ratliff; Roy Dong; Shreyas Sekar; Tanner Fiez (Annu. Rev. Control. Robotics Auton. Syst., 2019)

Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings. - Benjamin Chasnov; Lillian J. Ratliff; Eric Mazumdar; Samuel A. Burden (CoRR, 2019)

Convergence of Learning Dynamics in Stackelberg Games. - Tanner Fiez; Benjamin Chasnov; Lillian J. Ratliff (CoRR, 2019)

Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. - Eric Mazumdar; Lillian J. Ratliff; Michael I. Jordan; S. Shankar Sastry (CoRR, 2019)

Online Constraint Satisfaction via Tolls in MDP Congestion Games. - Sarah H. Q. Li; Yue Yu 004; Daniel J. Calderone; Lillian J. Ratliff; Behçet Açikmese (CoRR, 2019)

Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games. - Jingjing Bu; Lillian J. Ratliff; Mehran Mesbahi (CoRR, 2019)

Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. - Eric Mazumdar; Lillian J. Ratliff; Michael I. Jordan; S. Shankar Sastry (AAMAS, 2020)

Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces. - Benjamin J. Chasnov; Daniel J. Calderone; Behçet Açikmese; Samuel A. Burden; Lillian J. Ratliff (CDC, 2020)

Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study. - Tanner Fiez; Benjamin Chasnov; Lillian J. Ratliff (ICML, 2020)

Constrained Upper Confidence Reinforcement Learning. - Liyuan Zheng; Lillian J. Ratliff (L4DC, 2020)

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. - Tanner Fiez; Nihar B. Shah; Lillian J. Ratliff (UAI, 2020)

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. - Tanner Fiez; Nihar B. Shah; Lillian J. Ratliff (CoRR, 2020)

Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation. - Tanner Fiez; Lillian J. Ratliff (CoRR, 2020)

Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces. - Benjamin J. Chasnov; Daniel J. Calderone; Behçet Açikmese; Samuel A. Burden; Lillian J. Ratliff (CoRR, 2020)

On Gradient-Based Learning in Continuous Games. - Eric Mazumdar; Lillian J. Ratliff; S. Shankar Sastry (SIAM J. Math. Data Sci., 2020)

Inverse Risk-Sensitive Reinforcement Learning. - Lillian J. Ratliff; Eric Mazumdar (IEEE Trans. Autom. Control., 2020)

Uncertainty in Multicommodity Routing Networks: When Does It Help? - Shreyas Sekar; Liyuan Zheng; Lillian J. Ratliff; Baosen Zhang (IEEE Trans. Autom. Control., 2020)

Competitive Statistical Estimation With Strategic Data Sources. - Tyler Westenbroek; Roy Dong; Lillian J. Ratliff; S. Shankar Sastry (IEEE Trans. Autom. Control., 2020)

Modeling Curbside Parking as a Network of Finite Capacity Queues. - Chase P. Dowling; Lillian J. Ratliff; Baosen Zhang (IEEE Trans. Intell. Transp. Syst., 2020)

Gaussian Mixture Models for Parking Demand Data. - Tanner Fiez; Lillian J. Ratliff (IEEE Trans. Intell. Transp. Syst., 2020)

Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games. - Stratis Skoulakis; Tanner Fiez; Ryann Sim; Georgios Piliouras; Lillian J. Ratliff (AAAI, 2021)

Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation. - Tanner Fiez; Lillian J. Ratliff (ICLR, 2021)

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks. - Liyuan Zheng; Yuanyuan Shi; Lillian J. Ratliff; Baosen Zhang (L4DC, 2021)

Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. - Tanner Fiez; Lillian J. Ratliff; Eric Mazumdar; Evan Faulkner; Adhyyan Narang (NeurIPS, 2021)

Online Learning in Periodic Zero-Sum Games. - Tanner Fiez; Ryann Sim; Stratis Skoulakis; Georgios Piliouras; Lillian J. Ratliff (NeurIPS, 2021)

Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization. - Chinmay Maheshwari; Chih-Yuan Chiu; Eric Mazumdar; S. Shankar Sastry; Lillian J. Ratliff (CoRR, 2021)

Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions. - Roy Dong; Lillian J. Ratliff (CoRR, 2021)

Improved rates for derivative free play in convex games. - Dmitriy Drusvyatskiy; Lillian J. Ratliff (CoRR, 2021)

Disturbance Decoupling for Gradient-Based Multi-Agent Learning With Quadratic Costs. - Sarah H. Q. Li; Lillian J. Ratliff; Behçet Açikmese (IEEE Control. Syst. Lett., 2021)

Adaptive Incentive Design. - Lillian J. Ratliff; Tanner Fiez (IEEE Trans. Autom. Control., 2021)

Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments. - Mitas Ray; Lillian J. Ratliff; Dmitriy Drusvyatskiy; Maryam Fazel (AAAI, 2022)

Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms. - Liyuan Zheng; Tanner Fiez; Zane Alumbaugh; Benjamin Chasnov; Lillian J. Ratliff (AAAI, 2022)

Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. - Chinmay Maheshwari; Chih-Yuan Chiu; Eric Mazumdar; Shankar Sastry 001; Lillian J. Ratliff (AISTATS, 2022)

Learning in Stochastic Monotone Games with Decision-Dependent Data. - Adhyyan Narang; Evan Faulkner; Dmitriy Drusvyatskiy; Maryam Fazel; Lillian J. Ratliff (AISTATS, 2022)

Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games∗. - Dmitriy Drusvyatskiy; Maryam Fazel; Lillian J. Ratliff (CDC, 2022)

Minimax Optimization with Smooth Algorithmic Adversaries. - Tanner Fiez; Chi Jin 001; Praneeth Netrapalli; Lillian J. Ratliff (ICLR, 2022)

Instance-optimal PAC Algorithms for Contextual Bandits. - Zhaoqi Li; Lillian J. Ratliff; Houssam Nassif; Kevin G. Jamieson; Lalit Jain (NeurIPS, 2022)

Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games. - Georgios Piliouras; Lillian J. Ratliff; Ryann Sim; Stratis Skoulakis (SAGT, 2022)

Variable demand and multi-commodity flow in Markovian network equilibrium. - Yue Yu 004; Daniel J. Calderone; Sarah H. Q. Li; Lillian J. Ratliff; Behçet Açikmese (Autom., 2022)

General sum stochastic games with networked information flows. - Sarah H. Q. Li; Lillian J. Ratliff; Peeyush Kumar (CoRR, 2022)

Multi-learner risk reduction under endogenous participation dynamics. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (CoRR, 2022)

Interactive Combinatorial Bandits: Balancing Competitivity and Complementarity. - Adhyyan Narang; Omid Sadeghi; Lillian J. Ratliff; Maryam Fazel; Jeff A. Bilmes (CoRR, 2022)

Koopman Operator Applications in Signalized Traffic Systems. - Esther Ling; Liyuan Zheng; Lillian J. Ratliff; Samuel Coogan 001 (IEEE Trans. Intell. Transp. Syst., 2022)

Approximate Regions of Attraction in Learning with Decision-Dependent Distributions. - Roy Dong; Heling Zhang; Lillian J. Ratliff (AISTATS, 2023)

Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. - Arnab Maiti; Kevin G. Jamieson; Lillian J. Ratliff (AISTATS, 2023)

Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula. - Boling Yang; Liyuan Zheng; Lillian J. Ratliff; Byron Boots; Joshua R. Smith 001 (ICRA, 2023)

Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. - Lauren E. Conger; Franca Hoffmann; Eric Mazumdar; Lillian J. Ratliff (NeurIPS, 2023)

Adaptive constraint satisfaction for Markov decision process congestion games: Application to transportation networks. - Sarah H. Q. Li; Yue Yu 004; Nicolas I. Miguel; Daniel J. Calderone; Lillian J. Ratliff; Behçet Açikmese (Autom., 2023)

Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. - Chinmay Maheshwari; S. Shankar Sastry; Lillian J. Ratliff; Eric Mazumdar (CoRR, 2023)

Human adaptation to adaptive machines converges to game-theoretic equilibria. - Benjamin J. Chasnov; Lillian J. Ratliff; Samuel A. Burden (CoRR, 2023)

Consistent Conjectural Variations Equilibrium: Characterization & Stability for a Class of Continuous Games. - Daniel J. Calderone; Benjamin J. Chasnov; Samuel A. Burden; Lillian J. Ratliff (CoRR, 2023)

Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback. - Arnab Maiti; Kevin G. Jamieson; Lillian J. Ratliff (CoRR, 2023)

Coupled Gradient Flows for Strategic Non-Local Distribution Shift. - Lauren E. Conger; Franca Hoffmann; Eric Mazumdar; Lillian J. Ratliff (CoRR, 2023)

Query-Efficient Algorithms to Find the Unique Nash Equilibrium in a Two-Player Zero-Sum Matrix Game. - Arnab Maiti; Ross Boczar; Kevin G. Jamieson; Lillian J. Ratliff (CoRR, 2023)

Consistent Conjectural Variations Equilibria: Characterization and Stability for a Class of Continuous Games. - Daniel J. Calderone; Benjamin J. Chasnov; Samuel A. Burden; Lillian J. Ratliff (IEEE Control. Syst. Lett., 2023)

Multiplayer Performative Prediction: Learning in Decision-Dependent Games. - Adhyyan Narang; Evan Faulkner; Dmitriy Drusvyatskiy; Maryam Fazel; Lillian J. Ratliff (J. Mach. Learn. Res., 2023)

Emergent specialization from participation dynamics and multi-learner retraining. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (AISTATS, 2024)

Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. - Arnab Maiti; Ross Boczar; Kevin G. Jamieson; Lillian J. Ratliff (AISTATS, 2024)

Learning in Stochastic Stackelberg Games. - Pranoy Das; Benita Nortmann; Lillian J. Ratliff; Vijay Gupta 001; Thulasi Mylvaganam (ACC, 2024)

Follower Agnostic Learning in Stackelberg Games. - Chinmay Maheshwari; James Cheng; Shankar Sastry 001; Lillian J. Ratliff; Eric Mazumdar (CDC, 2024)

Initializing Services in Interactive ML Systems for Diverse Users. - Avinandan Bose; Mihaela Curmei; Daniel L. Jiang; Jamie H. Morgenstern; Sarah Dean; Lillian J. Ratliff; Maryam Fazel (NeurIPS, 2024)

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. - Adhyyan Narang; Andrew Wagenmaker; Lillian J. Ratliff; Kevin G. Jamieson (NeurIPS, 2024)

Efficient Interactive Maximization of BP and Weakly Submodular Objectives. - Adhyyan Narang; Omid Sadeghi; Lillian J. Ratliff; Maryam Fazel; Jeff A. Bilmes (UAI, 2024)

Distribution-Free Guarantees for Systems with Decision-Dependent Noise. - Heling Zhang; Lillian J. Ratliff; Roy Dong (CoRR, 2024)

Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game. - Jason T. Isa; Bohan Wu; Qirui Wang; Yilin Zhang; Samuel A. Burden; Lillian J. Ratliff; Benjamin J. Chasnov (CoRR, 2024)

Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents. - Junyan Liu; Lillian J. Ratliff (CoRR, 2024)

Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries. - Arnab Maiti; Zhiyuan Fan; Kevin G. Jamieson; Lillian J. Ratliff; Gabriele Farina (COLT, 2025)

Function Design for Improved Competitive Ratio in Online Resource Allocation with Procurement Costs. - Mitas Ray; Omid Sadeghi; Lillian J. Ratliff; Maryam Fazel (INFORMS J. Optim., 2025)

A Learning Algorithm That Attains the Human Optimum in a Repeated Human-Machine Interaction Game. - Jason T. Isa; Lillian J. Ratliff; Samuel A. Burden (CoRR, 2025)

S4S: Solving for a Diffusion Model Solver. - Eric Frankel; Sitan Chen; Jerry Li 001; Pang Wei Koh; Lillian J. Ratliff; Sewoong Oh (CoRR, 2025)

Stow: Robotic Packing of Items into Fabric Pods. - Nicolas Hudson; Josh Hooks; Rahul Warrier; Curt Salisbury; Ross Hartley; Kislay Kumar; Bhavana Chandrashekhar; Paul Birkmeyer; Bosch Tang; Matthew A. Frost; Shantanu Thakar; Tony Piaskowy; et al. (CoRR, 2025)

Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality. - Junyan Liu; Ziyun Chen; Kun Wang; Haipeng Luo; Lillian J. Ratliff (CoRR, 2025)

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals. - Junyan Liu; Arnab Maiti; Artin Tajdini; Kevin G. Jamieson; Lillian J. Ratliff (CoRR, 2025)