25 papers · ranked by Valyu relevance
Jahidul Arafat, Kh. M. Moniruzzaman, Hossain, Shamim + 1 more
Modern distributed systems employ aggressive optimization strategies that create latent risks—hidden vulnerabilities where exceptional performance under normal conditions masks catastrophic fragility when optimizations fail. Cache layers achieving 99% hit rates can obscure database bottlenecks until cache failures…
Wenjie Huang, William B. Haskell
We develop a stochastic approximation-type algorithm to solve finite state/action, infinite-horizon, risk-aware Markov decision processes. Our algorithm has two loops. The inner loop computes the risk by solving a stochastic saddle-point problem. The outer loop performs Q-learning to compute an optimal risk-aware…
Jukka Isohätälä, William B. Haskell
We present a probabilistic formulation of risk aware optimal control problems for stochastic differential equations. Risk awareness is in our framework captured by objective functions in which the risk neutral expectation is replaced by a risk function, a nonlinear functional of random variables that account for the…
Xiaoguang Huo, Feng Fu
Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under…
Marco Bianchetti, Gabriele D'Acunto, Gianmarco De Francisci Morales, Yuko Kuroki + 2 more
'Yuko Kuroki' 'Marco Scaringi' 'Fabio Vitale'] We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading…
Yanzheng Wang, Boyue Wang, Tianyu Shi, Jie Fu + 2 more
Antibody design is a time-consuming and expensive process that often requires extensive experimentation to identify the best candidates. To address this challenge, we propose an efficient and risk-aware antibody design framework that leverages protein language models (PLMs) and batch Bayesian optimization (BO). Our…
Ali Baheri
— In the dynamic and uncertain environments where reinforcement learning (RL) operates, risk management becomes a crucial factor in ensuring reliable decision-making. Traditional RL approaches, while effective in reward optimization, often overlook the landscape of potential risks. In response, this paper pioneers the…
Noah Patton, Jihwan Jeong, Michael Gimelfarb, Scott Sanner
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize actions. However…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Michal Kobiela, Diego A. Oyarzún, Michael U. Gutmann
Synthetic biology aims to engineer biological systems with specified functions. This requires navigating an extensive design space, which is challenging to achieve with wet lab experiments alone. To expedite the design process, mathematical modelling is typically employed to predict circuit function in silico ahead of…
Rishab Balasubramanian, Lifeng Zhou, Pratap Tokekar, P. B. Sujit
— We introduce a risk-aware variant of the Traveling Salesperson Problem (TSP), where the robot tour cost and reward have to be optimized simultaneously, while being subjected to uncertainty in both. We study the case where the rewards and the costs exhibit diminishing marginal gains, i.e., are submodular. Since the…
Aowabin Rahman, Salman Shuvo, Samrat Chatterjee, Mahantesh Halappanavar + 1 more
Autonomous navigation in dynamic high-consequence environments, such as search and rescue (SAR) missions, often relies on multiagent robotic systems that need to learn and adapt to changing conditions. Adversarial risks can introduce further challenges in such a setting where an autonomous agent may exhibit deviations…
Authors not listed
We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
Eric Schulz, Charley M. Wu, Quentin J.M. Huys, Andreas Krause + 1 more
How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate participants’ search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires participants to not only balance exploration and…
Panagiotis Vamvakas, Eirini Eleni Tsiropoulou, Symeon Papavassiliou
Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile…
Vicent Girbés-Juan, Joaquín Moll, Antonio Sala, Leopoldo Armesto + 1 more
'Ka-Veng Yuen'] In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We…
Jon Ahlinder, Patrik Waldmann
Optimum contribution selection (OCS) balances genetic gain and inbreeding by optimizing parental contributions to the next generation, but current implementations rely on point estimates of breeding values that discard the uncertainty inherent in genetic evaluations. We introduce CVaR-OCS, a novel formulation that…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Yuxuan Wang, Bowen Xiao, Jingyi Kang, Huizi Cui + 4 more
Drug resistance remains a recurrent failure mode in targeted anticancer and antiviral therapy, and resistance evidence often enters only after compound selection. ResistAgent is an evidence-constrained framework that converts mutational liabilities into design-time objectives through site- and combo-aware resistance…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Alex Luedtke, Marco Carone, Noah Simon, Oleg Sofrygin
Optimal statistical procedures are constructed using deep adversarial learning to optimize play in a two-player game.
Viet Thanh Duy Nguyen, Phuc Pham, Truong-Son Hy
Complex diseases, such as cancer and neurodegeneration, feature interconnected pathways, making single-target therapies ineffective due to pathway redundancy and compensatory mechanisms. Polypharmacy, which combines multiple drugs to target distinct proteins, addresses this but often leads to drug-drug interactions…
Yan Wu, Tianqi Xia, Adam Jatowt, Haoran Zhang + 3 more
'Ryosuke Shibasaki' 'Kyoung-Sook Kim'] Background Heatstroke is becoming an increasingly serious threat to outdoor activities, especially, at the time of large events organized during summer, including the Olympic Games or various types of happenings in amusement parks like Disneyland or other popular venues. The risk…
Xini Fang, Zhe Liu
To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…