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.
Financial planners helped preserve and increase household net financial assets during the Great Recession.
Study optimal investment decisions for diverse risk-tolerant agents.
The paper optimizes pension policies with guarantees and sustainability constraints.
Semi-analytical approach for optimal wealth management contributions.
Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent devel…
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…
We introduce a variant of Farber's topological complexity, defined for smooth compact orientable Riemannian manifolds, which takes into account only motion planners with the lowest possible "average length" of the output paths. We prove that it never differs from topological complexity by more than , thus showing th…
New research shows exponential lower bounds for planning in MDPs with linearly-realizable optimal action-value functions.
Modeling default contagion and systemic risk using a balls-and-bins approach.
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters gener…
The study shows how probability weighting can lead to betting in a risk-averse economy.
Adaptive financial dataflow system improves model robustness in dynamic markets.
Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.
A reinforcement learning framework combining value function and tree search planner for strategic and tactical decisions.
Investigates optimal pension policies in PAYG systems with forward utility and ageing population.
Agent learns to navigate uncertain 3D maps using a hybrid planner.
In this paper we show how to hedge a zero coupon bond with a smaller amount of initial capital than required by the classical risk neutral paradigm, whose (trivial) hedging strategy does not suggest to invest in the risky assets. Long dated zero coupon bonds we derive, invest first primarily in risky securities and whe…
Study presents a low-cost local motion planner for vineyard navigation.
Algorithm learns fair division from noisy feedback in uncertain markets.
Improved model-based reinforcement learning for interactive dialogue tasks reduces sample needs and improves performance.
XLVINs improve data efficiency in implicit planning by leveraging latent space.
End-to-end learnable network for safer self-driving with interpretable intermediate representations.
Our goal is for agents to optimize the right reward function, despite how difficult it is for us to specify what that is. Inverse Reinforcement Learning (IRL) enables us to infer reward functions from demonstrations, but it usually assumes that the expert is noisily optimal. Real people, on the other hand, often have s…
This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scenarios. The case study investigated is motivated by a setup where an aerial robot acts as a "scout" for a ground robot. This is useful when t…
Model analyzes optimal interbank networks during liquidity shocks, revealing core-periphery structures and co-investment requirements.
New approach improves black-box planning efficiency by discovering focused macros.
New algorithm learns FMDP structure while minimizing regret.
Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These scale well with large domains, but are sample inefficient and time-consuming to train from scratch for each new problem. To mitigate this, recent work has studied neural transfer learning, so that a generic planner trained on othe…
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…
DDPD separates generation into planning and denoising for improved efficiency.
TIM framework uses LLMs and domain experts to infer DeFi user transaction intents.
Investment strategies for rank-dependent utility agents are derived in a continuous-time market.
In this paper we study continuous-time stochastic control problems with both monotone and classical controls motivated by the so-called public good contribution problem. That is the problem of n economic agents aiming to maximize their expected utility allocating initial wealth over a given time period between private …
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, model-free reinforcement learning (RL) can acquire behaviors from l…
With a point of departure in the concept "uncomfortable knowledge," this article presents a case study of how the American Planning Association (APA) deals with such knowledge. APA was found to actively suppress publicity of malpractice concerns and bad planning in order to sustain a boosterish image of planning. In th…
Neural A* uses machine learning to improve path planning efficiency.
New algorithm for traffic routing in congested conditions.
Study optimal stopping for group with diverse discount rates using an attitude function.
This paper presents a general framework for studying diverse beliefs in dynamic economies. Within this general framework, the characterization of a central-planner general equilbrium turns out to be very easy to derive, and leads to a range of interesting applications. We show how for an economy with log investors hold…
Recent research in economic theory attempts to study optimal economic growth and spatial location of economic activity in a unified framework. So far, the key result of this literature - asymptotic convergence, even in the absence of decreasing returns to capital - relies on specific assumptions about the objective of …
We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that t…
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local a…
Optimal scheme minimizes deviation in federated transfer learning for kernel regression.
In this paper, we consider the problem of optimal reinsurance design, when the risk is measured by a distortion risk measure and the premium is given by a distortion risk premium. First, we show how the optimal reinsurance design for the ceding company, the reinsurance company and the social planner can be formulated i…
Study shows how multiple traders can trade together without excessive price impact.
Naive investors make riskier choices than optimal strategies in continuous-time finance.
We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions obtain aroun…