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.
RATE metrics evaluate treatment prioritization rules, subsuming existing methods.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researc…
A new method prioritizes project risks using Monte Carlo Simulation.
A framework uses proxies to prioritize treatment without estimating causal effects.
Introduces PIT-plot for prioritizing projects based on their impact.
We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of r…
A new method prioritizes and recycles experiences for better reinforcement learning.
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prio…
Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.
ReaPER improves learning efficiency by prioritizing reliable experiences.
Study uses healthcare claims data to identify Covid-19 risk factors without prior selection.
Designs for allocating resources to prioritize needy applicants while estimating treatment effects.
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more efficiently, researchers proposed prioritized experience replay (PER) which samples important transitions more frequently. In this paper, we propo…
This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candida…
A new approach to risk allocation balances asset and factor risks.
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
New AI models improve financial hedging by reducing shortfall and tail risk.
DSI improves tail-risk estimation in generative models by averaging checkpoints.
Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dep…
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-con…
New benchmarks focus on LLM risk in finance, not just accuracy.
PDTS improves robustness in sequential decision-making.
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based fr…
The paper tackles fair set-valued classification under demographic parity constraints.
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient …
In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introd…
The study evaluates how prediction helps identify the worst-off in welfare programs.
We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic "N-1" …
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
SAEs struggle with feature consistency across runs, hindering MI reliability.
DeePM is a deep-learning portfolio manager that outperforms classical strategies in diversified futures markets.
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spa…
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a factorization to approximate the value function. Our approach only requir…
Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.
Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessit…
Enhanced DQN model boosts trading performance with advanced techniques.
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodol…
New loss function equivalence reveals PER's uniform sampling can be improved.
Unified framework for portfolio optimization using multiple hypotheses.
This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.
The paper argues for prioritizing identifying structure over complex models for scientific discovery.
Exploration efficiency is a challenging problem in multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the collaborative approach among multiple agents. Another important problem is the less informative reward restricts the learning speed of MARL compared with the informative…
Automated scoring prioritizes risky driving behavior in telematic auto insurance policies.
Adambs adapts Adam to prioritize important training examples.