yusuke1997/dblp-id-title huggingface dataset:
Improving Gradient Computation for Differentiable Physics Simulation with Contacts
Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes
Certified Robustness via Locally Biased Randomized Smoothing
Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems
Linear Supervision for Nonlinear, High-Dimensional Neural Control and Differential Games
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Random features approximation for control-affine systems
Learning Temporal Logic Predicates from Data with Statistical Guarantees
Error bounds, PL condition, and quadratic growth for weakly convex functions, and linear convergences of proximal point methods
On the convergence of adaptive first order methods: proximal gradient and alternating minimization algorithms
Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors
Learning Biomolecular Models using Signal Temporal Logic
Safety filters for black-box dynamical systems by learning discriminating hyperplanes
Online decision making with history-average dependent costs
Bounding the Difference Between Model Predictive Control and Neural Networks
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian Processes
A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
Interpretable data-driven model predictive control of building energy systems using SHAP
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR
Rates for Offline Reinforcement Learning with Adaptively Collected Data
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning
Learning soft constrained MPC value functions: Efficient MPC design and implementation providing stability and safety guarantees
CACTO-SL: Using Sobolev learning to improve continuous actor-critic with trajectory optimization
Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear Dynamical Systems
Learning-based rigid tube model predictive control
Action-Conditioned Hamiltonian Generative Networks (AC-HGN) for Supervised and Reinforcement Learning
Distributed Stochastic Nash Equilibrium Learning in Locally Coupled Network Games with Unknown Parameters
Data-Enabled Gradient Flow as Feedback Controller: Regulation of Linear Dynamical Systems to Minimizers of Unknown Functions
Real-world fluid directed rigid body control via deep reinforcement learning
Feedback from Pixels: Output Regulation via Learning-based Scene View Synthesis
Deep model-free KKL observer: A switching approach
Learning Object-Centric Dynamic Modes from Video and Emerging Properties
Residual learning and context encoding for adaptive offline-to-online reinforcement learning
Adaptive Control of Positive Systems with Application to Learning SSP
6th Annual Learning for Dynamics & Control Conference, 15-17 July 2024, University of Oxford, Oxford, UK
A Pontryagin Perspective on Reinforcement Learning
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions
Learning-Enabled Robust Control with Noisy Measurements
Accelerating Trajectory Generation for Quadrotors Using Transformers
Competing Bandits in Time Varying Matching Markets
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret
Learning for Dynamics and Control Conference, L4DC 2022, 23-24 June 2022, Stanford University, Stanford, CA, USA
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
Learning Reversible Symplectic Dynamics
Physics-informed neural networks with unknown measurement noise
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021, 7-8 June 2021, Virtual Event, Switzerland
Adaptive Stochastic MPC under Unknown Noise Distribution
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Deep Off-Policy Iterative Learning Control
Controlling Participation in Federated Learning with Feedback
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
A unified framework for Hamiltonian deep neural networks
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Top-k data selection via distributed sample quantile inference
Finite Sample Analysis of Tensor Decomposition for Learning Mixtures of Linear Systems
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
Multi-agent Stochastic Bandits Robust to Adversarial Corruptions
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
Linear System Identification from Snapshot Data by Schrodinger bridge
Hamiltonian GAN
Imperative MPC: An End-to-End Self-Supervised Learning with Differentiable MPC for UAV Attitude Control
PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Games
Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
Automated Design of Grey-Box Recurrent Neural Networks For Fault Diagnosis using Structural Models and Causal Information
KPC: Learning-Based Model Predictive Control with Deterministic Guarantees
Parameter Optimization for Learning-based Control of Control-Affine Systems
Koopman Based Trajectory Optimization with Mixed Boundaries
Reinforcement Learning from Multi-level and Episodic Human Feedback
Traffic Forecasting using Vehicle-to-Vehicle Communication
Control of Unknown (Linear) Systems with Receding Horizon Learning
Outlier-Robust Linear System Identification Under Heavy-Tailed Noise
Asymptotics of Linear Regression with Linearly Dependent Data
Nonlinear state-space identification using deep encoder networks
Online No-regret Model-Based Meta RL for Personalized Navigation
Nonconvex scenario optimization for data-driven reachability
Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
SpOiLer: Offline reinforcement learning using scaled penalties
Towards bio-inspired control of aerial vehicle: Distributed aerodynamic parameters for state prediction
Bounded robustness in reinforcement learning via lexicographic objectives
Learning the model-free linear quadratic regulator via random search
Localized active learning of Gaussian process state space models
Cautious Bayesian Optimization for Efficient and Scalable Policy Search
Data-driven simulator for mechanical circulatory support with domain adversarial neural process
Planning from Images with Deep Latent Gaussian Process Dynamics
Data-Driven and Stealthy Deactivation of Safety Filters
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with $O(\sqrt{T})$ Regret
Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
Nonasymptotic regret analysis of adaptive linear quadratic control with model misspecification
Online Estimation and Control with Optimal Pathlength Regret
Data-Driven Distributed Predictive Control via Network Optimization
Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees
Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning
A Dynamic Penalization Framework for Online Rank-1 Semidefinite Programming Relaxations
PDE control gym: A benchmark for data-driven boundary control of partial differential equations
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models
Structured Variational Inference in Partially Observable UnstableGaussian Process State Space Models
Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
Pointwise-in-time diagnostics for reinforcement learning during training and runtime
Deep Source-Seekers with Obstacle Avoidance: Adaptive Hybrid Control with Transformers In-The-Loop
Deep Hankel matrices with random elements
Roll-Drop: accounting for observation noise with a single parameter
Learning to Plan via Deep Optimistic Value Exploration
A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes
Benchmarking Rigid Body Contact Models
Offline Model-Based Reinforcement Learning for Tokamak Control
Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers
Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
Filter-Aware Model-Predictive Control
Neuro-Symbolic Deadlock Resolution in Multi-Robot Systems
Global rewards in multi-agent deep reinforcement learning for autonomous mobility on demand systems
Understanding the difficulty of solving Cauchy problems with PINNs
Online Estimation of the Koopman Operator Using Fourier Features
Safe Control with Minimal Regret
Physics-Guided Active Learning of Environmental Flow Fields
Fast Stochastic Kalman Gradient Descent for Reinforcement Learning
Policy Evaluation in Distributional LQR
Concentration Phenomenon for Random Dynamical Systems: An Operator Theoretic Approach
Efficient Reinforcement Learning Through Trajectory Generation
Accelerating Model-Free Policy Optimization Using Model-Based Gradient: A Composite Optimization Perspective
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix
Data-driven strategy synthesis for stochastic systems with unknown nonlinear disturbances
Tracking and Planning with Spatial World Models
DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle Dynamics Modeling in Autonomous Racing
Learning Kolmogorov-Arnold Neural Activation Functions by Infinite-Dimensional Optimization
Compositional Learning-based Planning for Vision POMDPs
Multi-Agent Reinforcement Learning with Reward Delays
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems
Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road
Sequential Topological Representations for Predictive Models of Deformable Objects
Self-Supervised Learning of Long-Horizon Manipulation Tasks with Finite-State Task Machines
Learning Navigation Costs from Demonstrations with Semantic Observations
A Theoretical Analysis of Deep Q-Learning
Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy
Conditional Kernel Imitation Learning for Continuous State Environments
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control
Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation
Physics-informed Gaussian Processes as Linear Model Predictive Controller
Responding to Promises: No-regret learning against followers with memory
Learning Finite-Dimensional Representations For Koopman Operators
Input Convex Neural Networks for Building MPC
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Restless bandits with rewards generated by a linear Gaussian dynamical system
Data-Driven Near-Optimal Control of Nonlinear Systems Over Finite Horizon
Data-driven robust covariance control for uncertain linear systems
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?
Physically consistent modeling & identification of nonlinear friction with dissipative Gaussian processes
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems
Contrastive Example-Based Control
Submodular information selection for hypothesis testing with misclassification penalties
Robust Learning-Based Control via Bootstrapped Multiplicative Noise
Learning and deploying robust locomotion policies with minimal dynamics randomization
Learning true objectives: Linear algebraic characterizations of identifiability in inverse reinforcement learning
Resiliency of Perception-Based Controllers Against Attacks
Learning Trust Over Directed Graphs in Multiagent Systems
Learning Stability Attention in Vision-based End-to-end Driving Policies
Computing Quasi-Nash Equilibria via Gradient-Response Schemes
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training-Time Safety Violations
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing
PlanNetX: Learning an efficient neural network planner from MPC for longitudinal control
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
Learning to Correspond Dynamical Systems
Logarithmic Regret for Nonlinear Control
An investigation of time reversal symmetry in reinforcement learning
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation
Minimax Adaptive Control for a Finite Set of Linear Systems
Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories
Safe learning in nonlinear model predictive control
Data-driven memory-dependent abstractions of dynamical systems
Physics-enhanced Gaussian Process Variational Autoencoder
BIGE : Biomechanics-informed GenAI for Exercise Science
Learning with contextual information in non-stationary environments
Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples
Multi-Constraint Safe Reinforcement Learning via Closed-form Solution for Log-Sum-Exp Approximation of Control Barrier Functions
Neural processes with event triggers for fast adaptation to changes
Mapping back and forth between model predictive control and neural networks
Optimistic robust linear quadratic dual control
Localized Learning of Robust Controllers for Networked Systems with Dynamic Topology
7th Annual Learning for Dynamics & Control Conference, Ann Arbor, MI, USA, 4-6 June 2025
NAPI-MPC: Neural Accelerated Physics-Informed MPC for Nonlinear PDE Systems
Tracking object positions in reinforcement learning: A metric for keypoint detection
Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking
Learning Locomotion Skills from MPC in Sensor Space
Probabilistic Verification of ReLU Neural Networks via Characteristic Functions
Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System
Smart Forgetting for Safe Online Learning with Gaussian Processes
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints
Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models
A framework for evaluating human driver models using neuroimaging
DiffTune + : Hyperparameter-Free Auto-Tuning using Auto-Differentiation
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
When to stop value iteration: stability and near-optimality versus computation
Probabilistic Symmetry for Multi-Agent Dynamics
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models
The benefits of sharing: a cloud-aided performance-driven framework to learn optimal feedback policies
In vivo learning-based control of microbial populations density in bioreactors
Learning for Dynamics and Control Conference, L4DC 2023, 15-16 June 2023, Philadelphia, PA, USA
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
Balanced reward-inspired reinforcement learning for autonomous vehicle racing
Offset-free setpoint tracking using neural network controllers
Data-Driven Controller Design via Finite-Horizon Dissipativity
Stable Online Control of Linear Time-Varying Systems
Safe Learning in the Real World via Adaptive Shielding with Hamilton-Jacobi Reachability
Hybrid Systems Neural Control with Region-of-Attraction Planner
Data-Driven System Level Synthesis
Learning POMDP Models with Similarity Space Regularization: a Linear Gaussian Case Study
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
Robust Control of Uncertain Switched Affine Systems via Scenario Optimization
Sliding-Seeking Control: Model-Free Optimization with Safety Constraints
Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence
Regret Bound for Safe Gaussian Process Bandit Optimization
Sample-based Distributional Policy Gradient
Adaptive neural network based control approach for building energy control under changing environmental conditions
Realizable Continuous-Space Shields for Safe Reinforcement Learning
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
On the Sample Complexity of Stability Constrained Imitation Learning
Rademacher complexity of neural ODEs via Chen-Fliess series
Continual learning of multi-modal dynamics with external memory
QP Based Constrained Optimization for Reliable PINN Training
Periodic Q-Learning
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Learning Visually Guided Latent Actions for Assistive Teleoperation
Information Theoretic Model Predictive Q-Learning
Event-triggered safe Bayesian optimization on quadcopters
Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers
Optimal Pointing Sequences in Spacecraft Formation Flying Using Online Planning with Resource Constraints
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
Safely Learning Dynamical Systems from Short Trajectories
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?
Congestion Control of Vehicle Traffic Networks by Learning Structural and Temporal Patterns
Reachability Analysis-based Safety-Critical Control using Online Fixed-Time Reinforcement Learning
Verification of neural reachable tubes via scenario optimization and conformal prediction
Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty
Satellite Navigation and Coordination with Limited Information Sharing
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance
Data-Augmented Contact Model for Rigid Body Simulation
Data-Driven Safety Verification of Stochastic Systems via Barrier Certificates: A Wait-and-Judge Approach
Diffusion Predictive Control with Constraints
Abstraction-based branch and bound approach to Q-learning for hybrid optimal control
Parameterized fast and safe tracking (FaSTrack) using DeepReach
Gradient shaping for multi-constraint safe reinforcement learning
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions
Training deep residual networks for uniform approximation guarantees
Clustering-based Mode Reduction for Markov Jump Systems
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
MPC-inspired reinforcement learning for verifiable model-free control
Finite-time complexity of incremental policy gradient methods for solving multi-task reinforcement learning
Safe Reinforcement Learning Using Robust Action Governor
Adapting Image-based RL Policies via Predicted Rewards
Optimal Control with Learning on the Fly: System with Unknown Drift
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
Learning Dynamical Systems with Side Information
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
On exploration requirements for learning safety constraints
Recursively feasible shrinking-horizon MPC in dynamic environments with conformal prediction guarantees
End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions
CoVO-MPC: Theoretical analysis of sampling-based MPC and optimal covariance design
System-level safety guard: Safe tracking control through uncertain neural network dynamics models
Data-Driven Reachability Analysis Using Matrix Zonotopes
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
Neural Lyapunov Redesign
Actively Learning Gaussian Process Dynamics
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage
Multi-agent assignment via state augmented reinforcement learning
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
Domain Randomization is Sample Efficient for Linear Quadratic Control
Practical Critic Gradient based Actor Critic for On-Policy Reinforcement Learning
Accelerated Learning with Robustness to Adversarial Regressors
i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery
Safe Cooperative Multi-Agent Reinforcement Learning with Function Approximation
From raw data to safety: Reducing conservatism by set expansion
Non-conservative Design of Robust Tracking Controllers Based on Input-output Data
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot
Efficient Duple Perturbation Robustness in Low-rank MDPs
Data driven verification of positive invariant sets for discrete, nonlinear systems
Plan2Vec: Unsupervised Representation Learning by Latent Plans
Reinforcement learning-driven parametric curve fitting for snake robot gait design
STRiDE: STate-space Riemannian Diffusion for Equivariant Planning
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
Learning robust policies for uncertain parametric Markov decision processes
Safe online convex optimization with multi-point feedback
HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
ARDL - A Library for Adaptive Robotic Dynamics Learning
Data-efficient, explainable and safe box manipulation: Illustrating the advantages of physical priors in model-predictive control
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020, Online Event, Berkeley, CA, USA, 11-12 June 2020
Predictive Monitoring of Black-Box Dynamical Systems
Finite Sample Identification of Partially Observed Bilinear Dynamical Systems
On the Heterogeneity of Independent Learning Dynamics in Zero-sum Stochastic Games
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory
Safety-Aware Preference-Based Learning for Safety-Critical Control
Safe dynamic pricing for nonstationary network resource allocation
Stochastic Real-Time Deception in Nash Equilibrium Seeking for Games with Quadratic Payoffs
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach
Estimating Reachable Sets with Scenario Optimization
Data-driven Control of Unknown Linear Systems via Quantized Feedback
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
Learning Spatio-Temporal Specifications for Dynamical Systems
Disentangling Uncertainties by Learning Compressed Data Representation
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching