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.
Bayesian approach to optimal transport with stochastic costs.
New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.
The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level …
Investment strategies in occupational pension plans are optimized for non-tradable income risk.
We designed a grid world task to study human planning and re-planning behavior in an unknown stochastic environment. In our grid world, participants were asked to travel from a random starting point to a random goal position while maximizing their reward. Because they were not familiar with the environment, they needed…
Study integrates reliability constraints into generation planning models.
Optimal withdrawal strategy for DC pension plans maximizes total withdrawals while managing risk.
A neural network approach solves optimal decumulation problems for pension plans.
New algorithm minimizes worst-case regret in uncertain, time-varying dynamics.
Survey of integrating planning and learning in model-based reinforcement learning.
NOT learns optimal transport plans, kernel costs improve performance.
Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especiall…
This work clarifies the role of inference types in planning.
We study an asset allocation stochastic problem with restriction for a defined-contribution pension plan during the accumulation phase. We consider a financial market with stochastic interest rate, composed of a risk-free asset, a real zero coupon bond price, the inflation-linked bond and the risky asset. A plan member…
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planni…
This paper studies when particle filtering is efficient for planning in partially observed systems.
Paper uses NMT to predict solutions to stochastic optimization problems quickly.
Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the state and action spaces are large. Expectation models, on the other hand, are relatively easier to learn due to their compactness and have a…
E2C separates planning and execution in LLMs, improving efficiency and performance.
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be comput…
Study optimal investment under uncertain conditions.
Paper analyzes robust strategies in a pension plan game with ambiguous financial markets.
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself a…
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. …
New algorithms for planning with adversarial changes in costs.
Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regularization is added to the problem, the transportation plan can be…
Study finds optimal retirement timing in uncertain wage scenarios.
This work tackles maintenance planning with deep reinforcement learning under uncertainty.
In this paper we apply change of numeraire techniques to the optimal transport approach for computing model-free prices of derivatives in a two periods model. In particular, we consider the optimal transport plan constructed in \cite{HobsonKlimmek2013} as well as the one introduced in \cite{BeiglJuil} and further studi…
Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approxima…
Investigates risk measures for DC pension decumulation.
A moment constraint that limits the number of dividends in the optimal dividend problem is suggested. This leads to a new type of time-inconsistent stochastic impulse control problem. First, the optimal solution in the precommitment sense is derived. Second, the problem is formulated as an intrapersonal sequential dyna…
New algorithm models satiation in recommender systems.
This paper solves steady-state planning for multichain MDPs.
Study on limits of LLM-based multi-agent planning reliability.
A new method solves complex hydroelectricity planning problems.
Optimizes pension fund management under funding risks.
This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularize…
PS framework selects best policy from library for CSO problems.
We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example application to hydropower …
Develops a method to plan exploration that learns strong policies with fewer samples.
Unified approach to path planning using probabilistic inference on factor graphs.
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming where the…
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforcementlearning algorithm for learning in these domains and prove for certain environments the algorithm is probably approximately correct wit…
Optimal timing for borrowing from a 457(b) plan to maximize returns.
Improves RL planning by proposing sub-goals hierarchically.
New method designs fairer transport plans with uncertainty.
New clustering method uses Wasserstein distance to analyze simulation outputs.