New method solves uncertain control problems with model uncertainty.
problem Solving uncertain stochastic Markovian control problems in discrete time.
method Adaptive robust control approach using Bellman equation and recursive confidence regions.
result Success of the approach demonstrated through optimal portfolio allocation problem.
New robust control method for uncertain systems using bootstrapped noise.
problem Designing controllers robust to model uncertainties in finite data.
method Least-squares model estimator, bootstrap resampling, multiplicative noise LQR.
result Significantly outperforms certainty equivalent controllers in numerical tests.
Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.
problem Model uncertainty in stochastic processes.
method Adaptive robust control methodology using online learning and uncertainty reduction, empirical distribution, and Lagrangian duality.
result Nonparametric adaptive robust control approach is preferable to traditional robust frameworks.
Algorithm minimizes regret in adaptive control of unknown linear systems.
problem Adaptive control of unknown linear systems with quadratic costs.
method Provably polynomial time algorithm using recent developments in system estimation and robust controller synthesis.
result First algorithm with high probability guarantees of sub-linear regret.
A machine learning method optimizes portfolio and hedging under uncertain market parameters.
problem Optimizing portfolios and hedging under drift and volatility uncertainty.
method Machine learning approach solving adaptive robust control problems.
result Demonstrates financial advantages of adaptive robust framework.
New method handles robust and adaptive control of linear systems with non-convex costs.
problem Robust and adaptive control of linear systems with unknown parameters.
method Combining non-asymptotic linear regression, interval prediction, and tree-based planning.
result First end-to-end suboptimality analysis for robust and adaptive MPC with non-convex costs.
Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.
problem Lack of robustness and stability in adaptation for traffic signal control.
method Value-based Bayesian meta-reinforcement learning framework BM-DQN with fast-adaptation variation and DQN fast-update advantage.
result Framework adapts more quickly and robustly to new scenarios than previous methods.
Paper uses deep reinforcement learning for adaptive emergency control of power systems.
problem Traditional emergency control schemes are inadequate for modern power grids due to increasing uncertainties.
method Developed deep reinforcement learning (DRL) for adaptive emergency control of power systems.
result Demonstrated excellent performance and robustness of DRL-based emergency control schemes in various scenarios.
Study time-inconsistent control problems with model uncertainty, solving portfolio selection.
problem Time-inconsistent Markovian control problems under model uncertainty.
method Combining sub-game perfect strategies with adaptive robust stochastic methods.
result Solved numerically the mean-variance portfolio selection problem.
Paper develops a framework to test deep RL traffic controllers under various uncertainties.
problem Developing robust deep RL traffic controllers for dynamic urban areas.
method Open-source callback-based framework for evaluating deep RL configurations in a traffic simulation.
result Deep RL controllers perform well under demand surges, incidents, and sensor failures.
New method improves neural network robustness to adversarial attacks.
problem Improving adversarial robustness of neural networks.
method Inspired by adaptive control theory, the approach uses persistency of excitation to constrain gradient descent updates.
result Networks trained with the PoE-motivated learning rate schedule are significantly more robust to adversarial attacks.
Test the robustness of quantum-enhanced phase estimation under various noise conditions.
problem Evaluate the robustness of quantum-enhanced adaptive phase estimation (QEAPE) in noisy conditions.
method Simulated QEAPE under four phase-noise models and compared resource usage of evolutionary and Bayesian control policies.
result Demonstrated the effectiveness of both evolutionary and Bayesian control policies in noisy conditions.
New metric derived for robust optimization in stochastic control problems.
problem Non-parametric uncertainty in multiperiod stochastic control problems.
method Derived a new metric, adapted (p,∞)--Wasserstein distance, and used dynamic programming principle. result Dynamic programming principle for DRO problems with semi-separable cost functions.
The paper tackles robust control with uncertain dependence using data-driven methods.
problem Nonparametric robust control under dependence uncertainty in multi-period stochastic systems.
method Nonparametric adaptive robust control framework using stochastic gradient descent ascent algorithm.
result The controller benefits from knowing more about the uncertain model.
Prototype controls stochastic drug resistance in cells.
problem Emergence of drug-resistant cells from random mutations.
method Deep reinforcement learning for adaptive drug dosing.
result 100% success rate in suppressing cell proliferation.
Framework for robust RL in continuous control with model misspecification.
problem Model misspecification in reinforcement learning for continuous control.
method Integrates robustness into MPO algorithm through worst-case expected return objective and entropy regularization.
result Robust and soft-robust policies outperform non-robust policies in various domains.
Arcades uses deep learning to adapt smart-home behavior based on context.
problem Adaptive decision making in voice-controlled smart-home environments.
method Deep reinforcement learning with graphical representation of home automation system.
result Arcades promises long-term context-aware control of smart-home systems.
Deep RL improves space heating control with faster computation and robustness.
problem Suboptimal performance and inability to adapt to dynamic conditions in classical heating control methods.
method Deep reinforcement learning algorithm for optimal control of space heating systems.
result Outperforms rule-based control by 5-10% in simulated environments.
Study reveals adversarially robust domain adaptation is harder to generalize across domains.
problem Hardness of transferring adversarial robustness across different domains.
method Analysis of adversarial Rademacher complexity over symmetric difference hypothesis space.
result Adversarial Rademacher complexity is always greater than non-adversarial, indicating intrinsic hardness.
Proposes ARSK for robust and sparse clustering.
problem Outliers and high-dimensional noisy variables in K-means clustering.
method Introduces redundant error component and group sparse penalty for robustness, and weights and sparsity control penalty for noisy variables.
result Superior performance in identifying clusters without outliers and informative variables.
Optimizes investment model using LSTM for better risk control.
problem Enhancing risk control in multi-factor investment models.
method Combines LSTM with multi-factor investment model for factor selection and weight determination.
result LSTM model outperforms benchmark in risk control metrics.
AdaRL improves robust RL by adaptively adjusting policy complexity.
problem Handling epistemic uncertainty in environment dynamics.
method Bi-level optimization framework with adaptive rank adjustment.
result AdaRL outperforms existing methods on MuJoCo benchmarks.
Study robust control for systems with continuous states using adversarial perturbations.
problem Fragile policies in Markov control models under internal or external perturbations.
method Distributionally robust stochastic control with adaptive adversarial perturbations.
result Optimal robust policies for continuous state systems with uniform learning guarantees.
Paper proposes E/PD-Control for better neural network training.
problem Training efficiency and robustness of CNNs in online data flows.
method E/PD-Control combines feedback PD controller with exponential signal.
result Better learning efficiency and robustness demonstrated experimentally.
ADDIS improves power in online FDR control for conservative nulls.
problem Lack of power in adaptive FDR control algorithms for conservative nulls.
method ADDIS: adaptive discarding algorithm for online FDR control.
result ADDIS achieves best of both worlds: high power for conservative nulls and no loss for uniformly distributed nulls.
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
Adaptive sparseness enhances robust regression using MCC and ARD.
problem Developing a robust regression method with adaptive sparseness.
method Integrating MCC with ARD in a Bayesian framework using variational Bayesian inference.
result MCC-ARD regression outperforms existing methods in prediction and feature selection.
Tackles bridging machine learning and control theory for safety-critical systems.
problem Ensuring reliability and safety in machine learning applications for safety-critical systems.
method Review of recent advances in learning and control theory, historical context.
result Importance of control theorists joining the conversation on learning-related problems.
Paper tackles SMPC for linear systems with unknown noise distribution.
problem Stochastic MPC for linear systems with chance state constraints and unknown noise distribution.
method Reformulate chance constraints, design robust benchmark SMPC, and develop adaptive SMPC with online noise statistics learning.
result Adaptive SMPC guarantees time-uniform satisfaction of unknown reformulated state constraints with high probability.
Adaptive financial dataflow system improves model robustness in dynamic markets.
problem Static historical data leads to poor performance in dynamic financial markets.
method Drift-aware dataflow system with adaptive control and optimization.
result Enhanced model robustness and improved risk-adjusted returns.
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
The paper proposes a control strategy for systems with sparse parameters using compressed sensing.
problem Control of linear systems with unknown sparse parameters under disturbances.
method Sparse estimation using Recursive Least Squares, improved with Basis Pursuit Denoising, and reformulated probabilistic constraints.
result The proposed algorithm outperforms existing methods in control design for systems with sparse impulse response parameters.
A decentralized deep RL controller improves hexapod locomotion learning.
problem Deep RL struggles with real-world legged robot control.
method Decentralized deep RL on a hexapod robot.
result Decentralized approach learns better and faster.
Paper proposes SRA algorithm for online learning robustness and adaptivity.
problem Quantifying and evaluating tradeoff between robustness and adaptivity in online learning.
method SRA algorithm using biased stochastic approximation scheme with adaptive threshold.
result SRA algorithm provides superior performance in synthetic and real datasets.
Robust tests control type I error under data corruption.
problem Effective hypothesis testing under data corruption.
method General permutation tests using kernel MMD and HSIC metrics.
result Robust tests are minimax optimal and outperform private tests.
Paper proposes methods to help autonomous vehicles adapt to unexpected driving scenarios.
problem Autonomous vehicles struggle with unexpected driving conditions.
method Robust imitative planning (RIP) and adaptive robust imitative planning (AdaRIP) methods to detect and adapt to distribution shifts.
result Methods outperform current state-of-the-art approaches in nuScenes prediction challenge.
New method tackles model uncertainty in stochastic control using Bayesian nonparametrics.
problem Model uncertainty in stochastic control problems.
method Nonparametric Bayesian approach with Dirichlet process for unknown distributions, online learning, and Gaussian process surrogates.
result Demonstrates financial advantages of nonparametric Bayesian over parametric methods.
We develop the method of stochastic modified equations (SME), in which stochastic gradient algorithms are approximated in the weak sense by continuous-time stochastic differential equations. We exploit the continuous formulation together with optimal control theory to derive novel adaptive hyper-parameter adjustment po…
New algorithm improves deep learning stability with limited data.
problem Stability and robustness in reinforcement learning with scarce data.
method Uncertainty-aware trust region approach to policy optimization.
result Stable policy updates adapt to uncertainty levels during learning.
Adaptive framework predicts stock prices better during volatile periods.
problem Inability of standard prediction models to handle regime-dependent stock market behavior.
method Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control.
result 0.59% MAPE with adaptive system, compared to 0.80% for baseline.
A method to minimize regret in multi-agent control systems with adversarial disturbances.
problem Optimal control of dynamical systems with adversarial disturbances and multiple agents.
method Reduction from online convex optimization to a distributed algorithm for multi-agent control.
result The resulting distributed algorithm has low regret relative to the optimal precomputed joint policy.
Paper proposes an online adaptation algorithm for improving model performance.
problem Improving model fidelity in real-time for domain shift and time variance.
method Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch strategy.
result Proposed algorithm outperforms existing methods in experiments.
Introduces GFC for learning complex dynamical systems with geometric constraints.
problem Challenges in accurately modeling and predicting complex dynamical systems with geometric constraints.
method Geometric Contact Flows (GFC) using Riemannian and Contact geometry as inductive biases.
result Ensemble of contactomorphisms adapt the latent contact Hamiltonian model to target dynamics while preserving desirable properties.
We show LLMs can be locally linear, enabling better control of activations.
problem Suboptimal control of LLM activations during generation.
method Model LLM inference as a linear dynamical system, compute feedback controllers using Jacobians, and adapt classical control theory.
result Robust, fine-grained control of LLM activations across models and tasks.
A new control chart detects shifts in binary data streams quickly and reliably.
problem Early detection of small shifts in multiple binary data streams.
method Cumulative Standardized Binomial EWMA (CSB-EWMA) chart with exact variance derivation.
result Adaptive control limits ensure robust detection across different data distributions.
A framework integrates machine learning with robust control for safer, more reliable systems.
problem Combining machine learning with robust control for systems with stringent safety and reliability requirements.
method Integrates Gaussian Process Regression and state-of-the-art robust controller synthesis within a framework that provides rigorous guarantees.
result Demonstrated improved performance with more data while maintaining rigorous guarantees.
PPO-CMA improves PPO's exploration variance for faster RL progress.
problem Prematurely shrinking exploration variance in PPO leads to slow progress and local optima.
method Adapts exploration variance using covariance matrix adaptation from CMA-ES.
result Significantly improves performance in Roboschool benchmarks.
GRASP simplifies Bayesian regression with grouped predictors using an adaptive NBP prior.
problem Regression with grouped predictors and adaptive shrinkage.
method Normal Beta Prime (NBP) prior with tunable hyperparameters for flexible sparsity control.
result Empirical validation of robust and versatile GRASP across various sparsity and signal-to-noise ratios.