28 papers · ranked by Valyu relevance
Pavankumar Koratikere, Leifur Leifsson
Bayesian Optimization (BO) is a widely used approach for blackbox optimization that leverages a Gaussian process (GP) model and an acquisition function to guide future sampling. While effective in low-dimensional settings, BO faces scalability challenges in high-dimensional spaces and with large number of function…
Jonathan Lorraine
Gradient-based optimization has been critical to the success of machine learning, updating a single set of parameters to minimize a single loss. A growing number of applications rely on a generalization of this, where we have a bilevel or nested optimization of which subsets of parameters update on different objectives…
Roumaissa Ghlib, Rania Bouhadouza, Faicel Hnaien
Designing compact and efficient quantum circuits that are compatible with Noisy Intermediate-Scale Quantum (NISQ) hardware remains a central challenge in quantum computing. Most existing optimization approaches rely on fidelity-based fitness functions that require computing the full unitary matrix of the circuit.…
C.S. Elder, Minh Hoang, Mohsen Ferdosi, Carl Kingsford
The Beltway and Turnpike problems entail the reconstruction of circular and linear one-dimensional point sets from unordered pairwise distances. These problems arise in computational biology when the measurements provide distances but do not associate those distances with the entities that gave rise to them. Such…
Rui Pan, Jipeng Zhang, Xingyuan Pan, Renjie Pi + 2 more
'Tong Zhang'] Bilevel optimization has shown its utility across various machine learning settings, yet most algorithms in practice require second-order information, making it challenging to scale them up. Only recently, a paradigm of first-order algorithms emerged, capable of effectively addressing bilevel optimization…
Dewmini Sudara Marakkalage, Eleonora Testa, Walter Lau Neto, Alan Mishchenko + 2 more
'Alan Mishchenko' 'Giovanni De Micheli' 'Luca Amarú'] Abstract—Sequential logic synthesis can provide better Power-Performance-Area (PPA) than combinational logic synthesis since it explores a larger solution space. As the gate cost in advanced technologies keeps rising, sequential logic synthesis provides a powerful…
Xiaozhi Du, Kai Chen, Hongyuan Du, Zongbin Qiao + 1 more
Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved sparrow search…
Qiuyi Zhang
Scalarization is a general, parallizable technique that can be deployed in any multiobjective setting to reduce multiple objectives into one, yet some have dismissed this versatile approach because linear scalarizations cannot explore concave regions of the Pareto frontier. To that end, we aim to find simple non-linear…
Paul Stapor, Leonard Schmiester, Christoph Wierling, Simon Merkt + 4 more
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become…
Zhiying Xu, Francis Y. Yan, Minlan Yu
Decompose" Authors: ['Zhiying Xu' 'Francis Y. Yan' 'Minlan Yu'] Resource allocation is fundamental for cloud systems to ensure efficient resource sharing among tenants. However, the scale of such optimization problems has outgrown the capabilities of commercial solvers traditionally employed in production. To scale up…
Chaoming Wang, Xingsi Dong, Jiedong Jiang, Zilong Ji + 2 more
Whole-brain simulation stands as one of the most ambitious endeavors of our time, yet it remains constrained by significant technical challenges. A critical obstacle in this pursuit is the absence of a scalable online learning framework capable of supporting the efficient training of complex, diverse, and large-scale…
Kirill P. Kalinin, Jannes Gladrow, Jiaqi Chu, James H. Clegg + 20 more
'Daniel Cletheroe' 'Douglas J. Kelly' 'Babak Rahmani' 'Grace Brennan' 'Burcu Canakci' 'Fabian Falck' 'Michael Hansen' 'Jim Kleewein' 'Heiner Kremer' 'Greg O’Shea' 'Lucinda Pickup' 'Saravan Rajmohan' 'Ant Rowstron' 'Victor Ruhle' 'Lee Braine' 'Shrirang Khedekar' 'Natalia G. Berloff' 'Christos Gkantsidis' 'Francesca…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Guido Schryen
In high performance computing environments, we observe an ongoing increase in the available number of cores. For example, the current TOP500 list reveals that nine clusters have more than 1 million cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the…
Wei Li
Optimization of binding affinities for antibody-drug conjugates (ADCs) is inextricably linked to their therapeutic efficacy and specificity, where the majority of ADCs are engineered to achieve equilibrium dissociation constants (K_d_ values) in the range of 10^−9^ to 10^−10^ M. Yet, there is a paucity of published…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Mary Pitman, David Hahn, Gary Tresadern, David Mobley
Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the…
Nisha Ann Viswan, Alexandre Tribut, Manvel Gasparyan, Ovidiu Radulescu + 2 more
'Ovidiu Radulescu' 'Upinder S. Bhalla' 'Anders Wallqvist'] Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and sparse data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical…
Rakbin Sung, Seongmi Woo, Dongmin Shin, Junil Kim + 2 more
The expression values of genes can be tracked through RNA sequencing. The process of bulk RNA sequencing involves the calculation of the mean expression value of a given gene across multiple cells. This results in low resolution for expression dynamics from each individual cell. Single-cell RNA sequencing (scRNA-seq)…
Amin Saberi, Bin Wan, Kevin J. Wischnewski, Kyesam Jung + 6 more
Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Zheyuan Hu, Yifei Shi
In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Sriram P Chockalingam, Maneesha Aluru, Srinivas Aluru
Integrative analysis of large-scale single cell data collected from diverse cell populations promises an improved understanding of complex biological systems. While several algorithms have been developed for single cell RNA-sequencing data integration, many lack scalability to handle large numbers of datasets and/or…
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…