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Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge. Unlike the typical SGD, private datasets cannot be used many times for hyperparameter search in DPSGD; e.g., via a grid…

Machine Learning · Computer Science 2021-08-10 Aman Priyanshu , Rakshit Naidu , Fatemehsadat Mireshghallah , Mohammad Malekzadeh

Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain. In this paper, we propose a…

Computer Vision and Pattern Recognition · Computer Science 2017-05-25 Lingkun Luo , Xiaofang Wang , Shiqiang Hu , Liming Chen

Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems…

Decision trees are a widely used method for classification, both by themselves and as the building blocks of multiple different ensemble learning methods. The Max-Cut decision tree involves novel modifications to a standard, baseline model…

Machine Learning · Computer Science 2020-06-26 Jonathan Bodine , Dorit S. Hochbaum

A computational revolution unleashed the power of artificial neural networks. At the heart of that revolution is automatic differentiation, which calculates the derivative of a performance measure relative to a large number of parameters.…

Quantitative Methods · Quantitative Biology 2023-12-27 Steven A. Frank

Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on manual design, this LLM-Driven Heuristic Design (LHD) process leverages LLMs to iteratively…

Machine Learning · Computer Science 2026-04-17 Rongzheng Wang , Yihong Huang , Muquan Li , Jiakai Li , Di Liang , Bob Simons , Pei Ke , Shuang Liang , Ke Qin

Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications, especially in training deep neural networks. ADAM in…

Machine Learning · Computer Science 2026-01-30 Ruiqi Wang , Diego Klabjan

We propose Differentiable Satisfiability and Differentiable Answer Set Programming (Differentiable SAT/ASP) for multi-model optimization. Models (answer sets or satisfying truth assignments) are sampled using a novel SAT/ASP solving…

Artificial Intelligence · Computer Science 2019-01-01 Matthias Nickles

Self-adjusting computation is an approach for automatically producing dynamic algorithms from static ones. The approach works by tracking control and data dependencies, and propagating changes through the dependencies when making an update.…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-05-17 Daniel Anderson , Guy E. Blelloch , Anubhav Baweja , Umut A. Acar

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges…

Machine Learning · Computer Science 2023-09-15 Yafei Wang , Jianguo Liu

Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different. Current theoretical…

Machine Learning · Statistics 2017-08-01 Ievgen Redko , Amaury Habrard , Marc Sebban

The Rust programming language is an attractive choice for robotics and related fields, offering highly efficient and memory-safe code. However, a key limitation preventing its broader adoption in these domains is the lack of high-quality,…

Robotics · Computer Science 2025-04-23 Chen Liang , Qian Wang , Andy Xu , Daniel Rakita

Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different models to transfer knowledge to evolving data distributions.…

Synchronizations of processing elements (PEs) in massively parallel simulations, which arise due to communication or load imbalances between PEs, significantly affect the scalability of scientific applications. We have recently proposed a…

Computational Physics · Physics 2018-08-16 Konduri Aditya , Diego A. Donzis

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functionals with block…

Image and Video Processing · Electrical Eng. & Systems 2026-04-21 Luke Lozenski , Michael T. McCann , Brendt Wohlberg

Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have…

Machine Learning · Computer Science 2018-07-16 Richard Liaw , Eric Liang , Robert Nishihara , Philipp Moritz , Joseph E. Gonzalez , Ion Stoica

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences…

Artificial Intelligence · Computer Science 2017-11-29 Saeid Tizpaz-Niari , Pavol Cerny , Bor-Yuh Evan Chang , Ashutosh Trivedi

Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (LMs). The self-improving mechanism often employs random…

Machine Learning · Computer Science 2025-10-07 Woosung Koh , Wonbeen Oh , Jaein Jang , MinHyung Lee , Hyeongjin Kim , Ah Yeon Kim , Joonkee Kim , Junghyun Lee , Taehyeon Kim , Se-Young Yun

Foundation models achieve state-of-the-art performance across different tasks, but their size and computational demands raise concerns about accessibility and sustainability. Existing efficiency methods often require additional retraining…

Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on individual tasks. Agents trained with Deep Reinforcement…

Machine Learning · Computer Science 2024-07-19 Carolin Benjamins , Gjorgjina Cenikj , Ana Nikolikj , Aditya Mohan , Tome Eftimov , Marius Lindauer