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We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where…

Machine Learning · Computer Science 2024-05-16 Stefan Pricopie , Richard Allmendinger , Manuel Lopez-Ibanez , Clyde Fare , Matt Benatan , Joshua Knowles

The Jarzynski Equality is a well-known and widely used identity, relating the free energy difference between two states of a system to the work done over some arbitrary, nonequilibrium transformation between the two states. Despite being…

Statistical Mechanics · Physics 2018-12-18 Stuart J. Davie , Owen G. Jepps , Lamberto Rondoni , James C. Reid , Debra J. Searles

We consider a random walk in a random potential on a square lattice of arbitrary dimension. The potential is a function of an ergodic environment and some steps of the walk. The potential can be unbounded, but it is subject to a moment…

Probability · Mathematics 2013-02-12 Firas Rassoul-Agha , Timo Seppäläinen

Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an…

Machine Learning · Computer Science 2023-06-26 Phillip Si , Zeyi Chen , Subham Sekhar Sahoo , Yair Schiff , Volodymyr Kuleshov

This paper studies a finite-fuel two-dimensional degenerate singular stochastic control problem under regime switching that is motivated by the optimal irreversible extraction problem of an exhaustible commodity. A company extracts a…

Optimization and Control · Mathematics 2017-12-29 Giorgio Ferrari , Shuzhen Yang

We provide a formal, simple and intuitive theory of rational decision making including sequential decisions that affect the environment. The theory has a geometric flavor, which makes the arguments easy to visualize and understand. Our…

Machine Learning · Computer Science 2012-02-10 Peter Sunehag , Marcus Hutter

The proposed method of the free energy calculation is based on the approximation of the energy distribution in the microcanonical ensemble by the Gaussian distribution. We hope that our approach will be effective for the systems with…

Disordered Systems and Neural Networks · Physics 2015-06-23 Boris Kryzhanovsky , Leonid Litinskii

The present paper proposes an adaptive biasing potential for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells…

Mathematical Physics · Physics 2018-03-05 I. Bilionis , P. S. Koutsourelakis

How much free energy is irreversibly lost during a thermodynamic process? For deterministic protocols, lower bounds on energy dissipation arise from the thermodynamic friction associated with pushing a system out of equilibrium in finite…

Statistical Mechanics · Physics 2021-03-26 Samuel J. Bryant , Benjamin B. Machta

We consider the problem of optimally utilizing $N$ resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in…

Systems and Control · Computer Science 2017-05-18 Lorenzo Ferrari , Qing Zhao , Anna Scaglione

Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation. Using value function…

Machine Learning · Computer Science 2017-10-10 Yijie Peng , Edwin K. P. Chong , Chun-Hung Chen , Michael C. Fu

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often not exploitable by decision-makers who are preferably…

Optimization and Control · Mathematics 2022-06-01 Antoine Dubois , Damien Ernst

Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of…

Machine Learning · Computer Science 2020-08-13 Maggie Makar , Fredrik D. Johansson , John Guttag , David Sontag

Calculating free energy differences is a topic of substantial interest and has many applications including molecular docking and hydration, solvation, and binding free energies which is used in computational drug discovery. However, in…

Chemical Physics · Physics 2013-10-16 Asaf Farhi

We formulate a very general framework for optimal dynamic stochastic control problems which allows for a control-dependent informational structure. The issue of informational consistency is investigated. Bellman's principle is formulated…

Probability · Mathematics 2018-05-16 Saul Jacka , Matija Vidmar

Deregulated energy markets, demand forecasting, and the continuously increasing share of renewable energy sources call---among others---for a structured consideration of uncertainties in optimal power flow problems. The main challenge is to…

Optimization and Control · Mathematics 2018-08-24 Tillmann Mühlpfordt , Timm Faulwasser , Veit Hagenmeyer

We consider a general class of dynamic resource allocation problems within a stochastic optimal control framework. This class of problems arises in a wide variety of applications, each of which intrinsically involves resources of different…

Optimization and Control · Mathematics 2018-01-08 Xuefeng Gao , Yingdong Lu , Mayank Sharma , Mark S. Squillante , Joost W. Bosman

The critical behavior of a 3D Ising-like system is studied at the microscopic level of consideration. The free energy of ordering is calculated analytically as an explicit function of temperature, an external field and the initial…

Statistical Mechanics · Physics 2012-02-22 M. P. Kozlovskii , R. V. Romanik

Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent's control over the environment by encouraging visiting states with a large number of reachable next…

Machine Learning · Computer Science 2020-01-09 Felix Leibfried , Sergio Pascual-Diaz , Jordi Grau-Moya

In multi-period stochastic optimization problems, the future optimal decision is a random variable whose distribution depends on the parameters of the optimization problem. We analyze how the expected value of this random variable changes…

Optimization and Control · Mathematics 2020-01-28 Bar Light