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.
New method designs fairer transport plans with uncertainty.
This paper optimizes DC pension plan investments using O-U process and loan.
Improves RL planning by proposing sub-goals hierarchically.
Olympus benchmarks optimization algorithms for noisy experiments.
Optimal withdrawal strategy for DC pension plans maximizes total withdrawals while managing risk.
Certified guidance ensures generative models always meet planning objectives.
Paper analyzes robust strategies in a pension plan game with ambiguous financial markets.
Study experiment planning with function approximation in contextual bandit problems.
Investigates risk measures for DC pension decumulation.
Investment strategy for DC pension plan with inflation risk and tail VaR constraint.
E2C separates planning and execution in LLMs, improving efficiency and performance.
The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a for…
Investment strategies in occupational pension plans are optimized for non-tradable income risk.
Random investment strategies outperform sensible ones, even with forecasts.
Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…
Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first m…
For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective plann…
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
Model trains agents to optimize saving and investment strategies for diverse retirement needs.
Paper identifies a shared toolkit of strategies for risk management across fields.
Vanguard uses AI to create personalized financial plans.
Time inconsistency leads to intra-personal conflict and reconciliation strategies.
Paper tackles pandemic resource allocation challenges.
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…
Inspired by Strotz's consistent planning strategy, we formulate the infinite horizon mean-variance stopping problem as a subgame perfect Nash equilibrium in order to determine time consistent strategies with no regret. Equilibria among stopping times or randomized stopping times may not exist. This motivates us to cons…
In this paper, we solve the arms exponential exploding issue in multivariate Multi-Armed Bandit (Multivariate-MAB) problem when the arm dimension hierarchy is considered. We propose a framework called path planning (TS-PP) which utilizes decision graph/trees to model arm reward success rate with m-way dimension interac…
New algorithm ensures fair matching in resource allocation.
A neural network approach solves optimal decumulation problems for pension plans.
Enhances PlaNet for better planning in uncertain environments.
New RL algorithm ensures stable, replicable policies.
In this paper, we consider the pricing of derivative products that involve dynamic hedging strategies and payments within the planning horizon. Equity-indexed annuities (EIAs), Guaranteed investment certificate (GIC), American and Barrier options are typical examples of these products. Our exploration involves evaluati…
In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuse…
Optimizes fund portfolio updates using linear programming and heuristic search.
Coordinating multiple interacting agents to achieve a common goal is a difficult task with huge applicability. This problem remains hard to solve, even when limiting interactions to be mediated via a static interaction-graph. We present a novel approximate solution method for multi-agent Markov decision problems on gra…
A quantum financial approach to finite games of strategy is addressed, with an extension of Nash's theorem to the quantum financial setting, allowing for an entanglement of games of strategy with two-period financial allocation problems that are expressed in terms of: the consumption plans' optimization problem in pure…
GP-MRO discovers robust mixed strategies for unknown objectives.
The paper offers guidelines for validating data-driven models.
Language models predict inorganic synthesis conditions and temperatures.
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-augmented networks, that is inspired by the von Neumann and Harvard architectures of modern computers. This architecture enables the learnin…
We consider the portfolio choice problem for a long-run investor in a general continuous semimartingale model. We suggest to use path-wise growth optimality as the decision criterion and encode preferences through restrictions on the class of admissible wealth processes. Specifically, the investor is only interested in…
By investigating model-independent bounds for exotic options in financial mathematics, a martingale version of the Monge-Kantorovich mass transport problem was introduced in \cite{BeiglbockHenry LaborderePenkner,GalichonHenry-LabordereTouzi}. In this paper, we extend the one-dimensional Brenier's theorem to the present…
The paper optimizes insurance purchases for financial goals.
Optimal asset allocation strategy outperforms stochastic benchmark.
Managing investment portfolios is an old and well know problem in multiple fields including financial mathematics and financial engineering as well as econometrics and econophysics. Multiple different concepts and theories were used so far to describe methods of handling with financial assets, including differential eq…
Agent learns to navigate uncertain 3D maps using a hybrid planner.
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…
Rebellion Research's AI strategy outperformed the S&P 500 for 14 years.