On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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13 results for “Safe-Optimization”

Better safe than sorry: Risky function exploitation through safe optimizationstat.AP

Safe-Optimization algorithm shows participants' preference for safe outcomes over optimal points.

problem Exploration-exploitation in functions with safety constraints.

method Safe-Optimization algorithm based on Gaussian Processes, tested in two experiments.

result Participants prioritize safety over optimization, showing a homeostatic strategy.

Safe-Optimization Gaussian Processes Exploration-Exploitation Function Optimization Safety Constraints

2016-02-02abs ↗pdf ↗

Safe Learning under Uncertain Objectives and Constraintscs.LG

Algorithm ensures safe optimization under unknown constraints.

problem Optimization under unknown safety constraints.

method Reliable Frank-Wolfe (Reliable-FW) algorithm for non-convex functions.

result Algorithm finds approximate first-order stationary points safely.

safe optimization non-convex optimization Frank-Wolfe algorithm unknown constraints stochastic gradient

2020-06-23abs ↗pdf ↗

Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learningstat.ML

SODA-RL learns diverse treatment options for hypotension from data.

problem Identifying the best treatment for acute hypotension from observational data.

method SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning.

result SODA-RL identifies distinct, plausible treatment options from observational data.

Reinforcement Learning Hypotension Critical Care Electronic Health Records Treatment Options

2020-01-09abs ↗pdf ↗

Meta-active Learning in Probabilistically-Safe Optimizationcs.LG

Meta-active learning optimizes control of safety-critical systems by efficiently learning dynamics and configurations.

problem Efficiently learning system dynamics and optimal configurations for safety-critical systems like deep brain stimulation.

method Meta-learning an acquisition function using LSTM, cast as meta-learning, with a mixed-integer linear program policy.

result Achieved a 46% increase in information gain and a 20% speedup in computation time over baselines.

Meta-active learning Probabilistically-safe optimization Control systems Deep brain stimulation Active learning

2020-07-07abs ↗pdf ↗

Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Frameworkmath.OC

Paper optimizes industrial refrigeration using adaptive exploration.

problem Challenges in optimizing real-time industrial processes with unknown characteristics and safety constraints.

method Adaptive and explorative real-time optimization framework with Gaussian process uncertainty quantification.

result Approach increases energy efficiency of refrigeration process, approximating complete information solutions.

real-time optimization industrial refrigeration adaptive control explorative framework Gaussian processes

2022-11-21abs ↗pdf ↗

Safe Model-based Reinforcement Learning with Stability Guaranteesstat.ML

Safe reinforcement learning with stability guarantees for real-world systems.

problem Lack of safety guarantees in reinforcement learning for real-world applications.

method Combines control theory with statistical models to ensure stability and safety.

result Proven ability to safely optimize neural network policies without system failure.

reinforcement learning stability guarantees control theory Lyapunov stability data collection

2017-05-23abs ↗pdf ↗

Benefits of Monotonicity in Safe Exploration with Gaussian Processesstat.ML

Monotonicity helps in safely optimizing unknown functions.

problem Sequentially maximizing an unknown function with safety constraints.

method Gaussian process with monotonicity assumption, inspired by GP-UCB and SafeOpt.

result The proposed M-SafeUCB algorithm achieves theoretical guarantees and safety.

monotonicity safe exploration Gaussian process UCB regret

2022-11-03abs ↗pdf ↗

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescs.LG

LineBO tackles high-dimensional Bayesian optimization by solving 1D subproblems.

problem Bayesian optimization struggles in high dimensions due to complex acquisition steps.

method LineBO restricts high-dimensional problems to 1D subproblems iteratively solved efficiently.

result LineBO converges globally and achieves a fast local rate for strongly convex functions.

Bayesian optimization high dimensions one-dimensional subspaces safe optimization strong convexity

2019-02-08abs ↗pdf ↗

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluationcs.LG

ESRL uses uncertainty quantification to learn safe, optimal policies in offline RL.

problem Challenges in interpreting and measuring uncertainty of learned policies in offline RL.

method Expert-Supervised Reinforcement Learning (ESRL) framework that uses hypothesis testing and posterior distributions.

result The framework can learn safe and optimal policies with theoretical guarantees and independent sample efficiency.

Reinforcement Learning Offline Policy Learning Uncertainty Quantification Posterior Sampling Risk Aversion

2020-06-23abs ↗pdf ↗

Stagewise Safe Bayesian Optimization with Gaussian Processescs.LG

StageOpt efficiently optimizes safe decisions by separating safety and utility stages.

problem Optimizing unknown utility with safety constraints in sequential decisions.

method Develops StageOpt, a two-stage safe Bayesian optimization algorithm.

result StageOpt is more efficient and applicable to broader problems than existing methods.

Bayesian optimization safe decision making Gaussian processes sequential decision making safety constraints

2018-06-20abs ↗pdf ↗

Towards Safe Multi-Task Bayesian Optimizationcs.LG

Bayesian optimization sped up with model approximations for safe online system optimization.

problem Efficiently optimize systems with safety guarantees under noisy conditions.

method Incorporate reduced physical models into Bayesian optimization, using Markov chain Monte Carlo for robust safety bounds.

result Significant acceleration of optimization for expensive functions with robust safety guarantees.

Bayesian optimization multi-task safety guarantees reduced models Markov chain Monte Carlo

2023-12-12abs ↗pdf ↗

Safety in safe Bayesian optimization and its ramifications for controleess.SY

Safe Bayesian optimization tackles safety constraints in control engineering.

problem Handling safety constraints in parameter tuning of control systems.

method Lipschitz-only Safe Bayesian Optimization (LoSBO) and LoS-GP-UCB.

result SafeBO algorithms can violate safety constraints due to unreliable uncertainty bounds.

Bayesian optimization safe optimization control engineering parameter tuning uncertainty bounds

2025-01-23abs ↗pdf ↗

Safe global optimization of expensive noisy black-box functions in the

δ

-Lipschitz frameworkmath.OC

This paper tackles safe global optimization of noisy functions with a Lipschitz condition.

problem Safe global maximization of expensive, noisy, Lipschitz functions.

method Develops a δ-Lipschitz framework and two algorithms to ensure safety constraints are met.

result The proposed methods ensure safety constraints are met before evaluating noisy functions.

safe optimization noisy functions Lipschitz condition δ-Lipschitz framework global maximization

2019-08-15abs ↗pdf ↗