Search Results for author: S. Shankar Sastry

Found 32 papers, 2 papers with code

Who Leads and Who Follows in Strategic Classification?

no code implementations NeurIPS 2021 Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan

In particular, by generalizing the standard model to allow both players to learn over time, we show that a decision-maker that makes updates faster than the agents can reverse the order of play, meaning that the agents lead and the decision-maker follows.

Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization

no code implementations16 Jun 2021 Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff

Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data.

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective

no code implementations27 Mar 2021 Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry

The widespread adoption of nonlinear Receding Horizon Control (RHC) strategies by industry has led to more than 30 years of intense research efforts to provide stability guarantees for these methods.

Maximum Likelihood Constraint Inference from Stochastic Demonstrations

no code implementations24 Feb 2021 David L. McPherson, Kaylene C. Stocking, S. Shankar Sastry

Stochastic models, however, can capture the uncertainty and risk tolerance that are often present in real systems of interest.

Expert Selection in High-Dimensional Markov Decision Processes

no code implementations26 Oct 2020 Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire Tomlin, S. Shankar Sastry

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings.

Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation

no code implementations20 Apr 2020 Oladapo Afolabi, Allen Y. Yang, S. Shankar Sastry

Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, retrieval, and completion.

3D Shape Classification 3D Shape Retrieval +1

Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning

no code implementations L4DC 2020 Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Claire J. Tomlin, S. Shankar Sastry, Koushil Sreenath

The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints.

Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning

no code implementations6 Apr 2020 Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry

This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules.

LESS is More: Rethinking Probabilistic Models of Human Behavior

no code implementations13 Jan 2020 Andreea Bobu, Dexter R. R. Scobee, Jaime F. Fisac, S. Shankar Sastry, Anca D. Dragan

A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward.

Persistency of Excitation for Robustness of Neural Networks

1 code implementation4 Nov 2019 Kamil Nar, S. Shankar Sastry

While training a neural network, the iterative optimization algorithm involved also creates an online learning problem, and consequently, correct estimation of the optimal parameters requires persistent excitation of the network weights.

Multi-Armed Bandits

Feedback Linearization for Unknown Systems via Reinforcement Learning

no code implementations29 Oct 2019 Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin

We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.

Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

no code implementations ICLR 2020 Dexter R. R. Scobee, S. Shankar Sastry

While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent's policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints.

Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games

no code implementations8 Jul 2019 Eric Mazumdar, Lillian J. Ratliff, Michael. I. Jordan, S. Shankar Sastry

In such games the state and action spaces are continuous and global Nash equilibria can be found be solving coupled Ricatti equations.

Cross-Entropy Loss Leads To Poor Margins

no code implementations ICLR 2019 Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran

In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.

Competitive Statistical Estimation with Strategic Data Sources

no code implementations29 Apr 2019 Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry

Recent work has explored mechanisms to ensure that the data sources share high quality data with a single data aggregator, addressing the issue of moral hazard.

Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples

no code implementations24 Jan 2019 Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran

We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset.

On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games

no code implementations3 Jan 2019 Eric V. Mazumdar, Michael. I. Jordan, S. Shankar Sastry

We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games.

Hierarchical Game-Theoretic Planning for Autonomous Vehicles

no code implementations13 Oct 2018 Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, Anca D. Dragan

This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology.

Autonomous Driving Decision Making +1

Step Size Matters in Deep Learning

2 code implementations NeurIPS 2018 Kamil Nar, S. Shankar Sastry

To elucidate the effects of the step size on training of neural networks, we study the gradient descent algorithm as a discrete-time dynamical system, and by analyzing the Lyapunov stability of different solutions, we show the relationship between the step size of the algorithm and the solutions that can be obtained with this algorithm.

On Gradient-Based Learning in Continuous Games

no code implementations16 Apr 2018 Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry

We formulate a general framework for competitive gradient-based learning that encompasses a wide breadth of multi-agent learning algorithms, and analyze the limiting behavior of competitive gradient-based learning algorithms using dynamical systems theory.

Multi-agent Reinforcement Learning

Generating Plans that Predict Themselves

no code implementations14 Feb 2018 Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan

We introduce $t$-\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan.

Pragmatic-Pedagogic Value Alignment

no code implementations20 Jul 2017 Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan

In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.

Decision Making

A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes

no code implementations18 Jul 2017 Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire Tomlin, S. Shankar Sastry

We formulate a multi-armed bandit (MAB) approach to choosing expert policies online in Markov decision processes (MDPs).

Systems and Control

Towards Verified Artificial Intelligence

no code implementations27 Jun 2016 Sanjit A. Seshia, Dorsa Sadigh, S. Shankar Sastry

Verified artificial intelligence (AI) is the goal of designing AI-based systems that that have strong, ideally provable, assurances of correctness with respect to mathematically-specified requirements.

Dissimilarity-based Sparse Subset Selection

no code implementations25 Jul 2014 Ehsan Elhamifar, Guillermo Sapiro, S. Shankar Sastry

The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering.

Recommendation Systems Time Series

Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment

no code implementations8 Feb 2014 Liansheng Zhuang, Tsung-Han Chan, Allen Y. Yang, S. Shankar Sastry, Yi Ma

In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class.

Face Alignment Face Recognition +1

Scalable Anomaly Detection in Large Homogenous Populations

no code implementations20 Sep 2013 Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, S. Shankar Sastry

The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests.

Anomaly Detection Combinatorial Optimization

Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

no code implementations CVPR 2013 Liansheng Zhuang, Allen Y. Yang, Zihan Zhou, S. Shankar Sastry, Yi Ma

To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.

Face Alignment Face Recognition +1

Compressive Shift Retrieval

no code implementations20 Mar 2013 Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry

The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.

Compressive Sensing

Fast L1-Minimization Algorithms For Robust Face Recognition

no code implementations21 Jul 2010 Allen Y. Yang, Zihan Zhou, Arvind Ganesh, S. Shankar Sastry, Yi Ma

L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.

Compressive Sensing Face Recognition +1

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