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.
Paper proposes adaptive control for unknown systems using reinforcement learning.
problem Adaptive control for unknown, linearizable systems.
method On-policy reinforcement learning for discrete-time, stochastic systems.
result Stability and tracking errors concentrate near zero with high probability.
A new framework for adaptive behavior using reusable value profiles.
problem Adaptive behavior in changing environments requires switching among value-control regimes, but maintaining separate parameters for each situation is impractical.
method Introduces value profiles: reusable bundles of parameters assigned to hidden states, allowing for state-conditional strategy recruitment without independent parameters for each context.
result Profile-based models outperform simpler alternatives in probabilistic reversal learning, suggesting belief-dependent control of adaptive behavior.
New bounds for adaptive control in high dimensions without fixed state space.
problem Adaptive control of linear systems in high or infinite dimensions.
method Novel perturbation bound for certainty equivalence, scaling with prediction error.
result First regret bounds for LQR in infinite dimensional systems, independent of ambient dimension.
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.
Controller seeks informative system observations to predict nonlinear dynamics.
problem Predicting nonlinear dynamics with uncertain parameters.
method Expected free energy minimization for balancing goal state and informative observations.
result Controller improves performance in uncertain parameter scenarios.
An adaptive time-stepping controller improves stability and accuracy of ResNets.
problem Improving stability and performance of ResNets using adaptive time stepping.
method Developed an adaptive time-stepping controller based on Runge-Kutta-Fehlberg method.
result Demonstrated improved stability and accuracy of ResNets without additional overhead.
LqgOpt learns optimal control in unknown LQG systems with minimal regret.
problem Adaptive control in partially observable linear quadratic Gaussian systems with unknown dynamics.
method Optimism in the face of uncertainty, predictor state evolution, closed-loop system identification, confidence bounds.
result Proves a regret upper bound of i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) for LQG systems. 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.
Designs adaptive controller for networked control systems with wireless data transmission.
problem Adaptive control in networked systems with unreliable wireless channels.
method Upper Confidence Bounds for Networked Control Systems (UCB-NCS) learning rule.
result Non-asymptotic performance guarantees with a regret bound of O(C√T).
Paper tackles non-Markovian control problems with new learning methods.
problem Non-Markovian stochastic control problems with unknown parameters.
method Off-model training and importance sampling for deep neural network approximation.
result Quantitative error bounds for adaptive learning under model uncertainty.
Cross-regularization adapts model complexity during training.
problem Manual tuning of model complexity for overfitting prevention.
method Directly adapts regularization parameters through validation gradients during training.
result Organic emergence of architecture-specific regularization during training.
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.
Develops adaptive SGD algorithms using SME for robust performance.
problem Designing robust stochastic gradient algorithms for varying models and datasets.
method Stochastic modified equations (SME) and optimal control theory.
result Novel adaptive hyper-parameter adjustment policies.
Optimal control strategy uses random noise to adaptively control systems with unknown parameters.
problem Online adaptive control of linear quadratic regulator with unknown system parameters.
method Certainty equivalent control with exploratory random noise, refined estimates of system matrices.
result Achieves optimal regret scaling as Θ(√(d_u^2 d_x T)) with self-bounding ODE method.
The study sets limits on how well systems can be controlled adaptively.
problem Learning to control unknown linear Gaussian systems with quadratic costs.
method Combining ideas from experiment design, estimation theory, and perturbation bounds of information matrices.
result Regret lower bounds of the order of T \sqrt{T} T in the time horizon T T T accurately capture control-theoretic parameters. 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.
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.
Paper analyzes GANs training difficulties and proposes a control framework.
problem Difficulties in training GANs, especially for financial time series.
method Stochastic control framework for hyper-parameters tuning.
result Explicit forms for optimal adaptive learning rate and batch size derived.
Improved quantum control fidelity for noisy systems using differential evolution.
problem Stagnation in non-convex optimization for noisy quantum dynamics.
method Employed differential evolution algorithms to optimize quantum control parameters.
result Achieved superior fidelity and scalability in quantum phase estimation and gate design.
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.
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.
TAD efficiently finds optimal settings for advanced manufacturing.
problem Optimizing high-dimensional process control parameters for optimal design features.
method TAD uses Gaussian process surrogate models and optimizes log-predictive likelihood to find optimal settings.
result TAD efficiently locates optimal settings with quantified uncertainty.
Adaptive l1-regularization controls short-selling in portfolio selection.
problem Financial markets' restrictions on short-selling and sparsity in portfolio solutions.
method Updating rule for l1-penalty parameter in Bregman iteration.
result Approach preserves properties of original l1-regularization and controls both sparsity and short positions.
NOVAS uses adaptive stochastic search for non-convex optimization in deep networks.
problem Non-convex optimization challenges in deep neural networks.
method Adaptive stochastic search for non-convex optimization.
result NOVAS outperforms existing alternatives in a structured prediction task.
BOFFIN TTS optimizes hyper-parameters for new speaker adaptation.
problem Fine-tuning a pre-trained TTS model for a new speaker with limited data.
method Bayesian optimization to efficiently find optimal hyper-parameters.
result Average 30% improvement in speaker similarity over standard techniques.
TIDBD adapts step sizes online for better robotic predictions.
problem Choosing appropriate learning parameters for online prediction-learning.
method Temporal-Difference Incremental Delta-Bar-Delta (TIDBD) for step-size adaptation.
result TIDBD performs comparably to classic TD learning and detects sensor failures.
A new method for solving many lasso problems with a fixed regularization parameter.
problem Solving many lasso problems with a fixed regularization parameter when the dictionary is large.
method Feedback-controlled sequential screening to adaptively select problems.
result The method can solve many lasso problems efficiently with a fixed regularization parameter.
Adaptive conformal inference without data exchangeability assumptions.
problem Real-world scenarios often violate the data exchangeability assumption for conformal prediction.
method Parameter-free online convex optimization for adaptive conformal inference.
result Controls long-term miscoverage frequency at a nominal level empirically.
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.
NeAda solves nonconvex minimax optimization by balancing primal and dual variables adaptively.
problem Nonconvex minimax optimization challenges with parameter-agnostic adaptive algorithms.
method Nested Adaptive (NeAda) framework with inner and outer loops for primal and dual variables.
result Achieves near-optimal convergence rates for nonconvex-strongly-concave problems.
IG-RL learns adaptive traffic signals for any network, outperforming existing methods.
problem Adaptive traffic signal control for large networks with combinatorial state and action spaces.
method Graph-Convolutional Networks for decentralized, flexible control.
result IG-RL generalizes to new networks and traffic conditions without additional training.
Improved neural ODEs learn adaptable flows.
problem Neural ODEs struggle with expressive power and adaptability.
method Introduce N-CODE modules with dynamic parameters controlled by a trainable map.
result N-CODE modules enhance expressivity of neural ODEs.
Proposes a new method for nonparametric regression and adaptive control.
problem Estimating continuous functions with bounded observational errors.
method Online estimation of Hoelder constant, applying it to Kinky Inference rule.
result Strong universal approximation guarantees for continuous functions.
DMFAW improves multi-view clustering with adaptive weights and feature selection.
problem Lack of effective feature selection and empirical hyperparameter selection in existing deep matrix factorization methods.
method Introduces Deep Matrix Factorization with Adaptive Weights (DMFAW) for multi-view clustering, incorporating feature selection and dynamically updating weights using Control Theory.
result DMFAW outperforms state-of-the-art methods in clustering performance.
Alpha-trimming prunes trees in random forests to improve predictive performance.
problem Improving predictive performance of random forests by locally adaptive tree pruning.
method Alpha-trimming is a fast pruning algorithm that prunes trees in a random forest based on signal-to-noise ratio, controlled by a tuning parameter.
result Alpha-trimming often lowers mean squared prediction error compared to fully grown random forests.
A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear stochastic systems involves solving complex multi-dimensional integrals that are not amenable to analytical solution. While basic sequentia…
TensorGuide improves LoRA efficiency and expressivity through joint tensor-train optimization.
problem Limited expressivity and generalization of standard LoRA.
method TensorGuide uses a unified tensor-train structure with controlled Gaussian noise to generate correlated low-rank matrices.
result TensorGuide achieves superior accuracy and scalability with fewer parameters compared to standard LoRA and TT-LoRA.
LaLoRA prevents forgetting in LoRA fine-tuning.
problem Catastrophic forgetting in fine-tuned models.
method LaLoRA applies Laplace approximation to LoRA weights for regularization.
result Improved learning-forgetting trade-off with controllable regularization strength.
Paper models buildings' thermal characteristics with a Bayesian approach.
problem Modeling buildings' heat dynamics with various factors.
method Bayesian state-space model incorporating prior knowledge.
result Bayesian approach provides similar parameters as MCMC but faster.
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.
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.
This paper solves a Bayes sequential impulse control problem for a diffusion, whose drift has an unobservable parameter with a change point. The partially-observed problem is reformulated into one with full observations, via a change of probability measure which removes the drift. The optimal impulse controls can be ex…
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
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.
New method controls false discoveries in online testing with deadlines.
problem Controlling false discoveries in online hypothesis testing with decision deadlines.
method Benjamini-Hochberg-type procedure over a moving window of hypotheses with adaptive threshold parameters.
result Controls false discovery rate at every stage and adaptively chosen stopping times.