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Found 33 papers, 3 papers with code
Who Leads and Who Follows in Strategic Classification?
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
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
Crucially, this guarantee requires that state costs applied to the planning problems are in a certain sense `compatible' with the global geometry of the system, and a simple counter-example demonstrates the necessity of this condition.
Maximum Likelihood Constraint Inference from Stochastic Demonstrations
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
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
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.
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
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
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
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
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.
Feedback Linearization for Unknown Systems via Reinforcement Learning
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
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
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
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
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
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
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
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.
Step Size Matters in Deep Learning
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
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.
Generating Plans that Predict Themselves
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.
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications.
Pragmatic-Pedagogic Value Alignment
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.
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes
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
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
The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering.
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
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.
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
As in robust PCA, it can be problematic to find a suitable regularization parameter.
Scalable Anomaly Detection in Large Homogenous Populations
The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests.
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
Compressive Shift Retrieval
The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
