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The rapid adoption of machine learning (ML) has underscored the importance of serving ML models with high throughput and resource efficiency. Traditional approaches to managing increasing query demands have predominantly focused on hardware…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-08 Sohaib Ahmad , Hui Guan , Ramesh K. Sitaraman

Although the use of metric fluents is fundamental to many practical planning problems, the study of heuristics to support fully automated planners working with these fluents remains relatively unexplored. The most widely used heuristic is…

Artificial Intelligence · Computer Science 2014-02-05 Amanda Jane Coles , Andrew Ian Coles , Maria Fox , Derek Long

With the advancement of large language models (LLMs), solving complex reasoning tasks has gained increasing attention. Inference-time computation methods (e.g., Best-of-N, beam search, et al.) are particularly valuable as they can enhance…

Artificial Intelligence · Computer Science 2025-02-18 Fan Liu , Wenshuo Chao , Naiqiang Tan , Hao Liu

Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solutions fit a flexible approximation with stochastic gradient…

Machine Learning · Statistics 2017-07-13 Joseph Sakaya , Arto Klami

Datacenters are increasingly becoming heterogeneous, and are starting to include specialized hardware for networking, video processing, and especially deep learning. To leverage the heterogeneous compute capability of modern datacenters, we…

Machine Learning · Computer Science 2023-08-03 Yassine Ghannane , Mohamed S. Abdelfattah

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture,…

Programming Languages · Computer Science 2020-12-04 Chris Cummins , Hugh Leather , Zacharias Fisches , Tal Ben-Nun , Torsten Hoefler , Michael O'Boyle

Hyperparameter optimisation (HPO) is crucial for achieving strong performance in reinforcement learning (RL), as RL algorithms are inherently sensitive to hyperparameter settings. Probabilistic Curriculum Learning (PCL) is a curriculum…

Machine Learning · Computer Science 2025-04-10 Llewyn Salt , Marcus Gallagher

In recent years, large language models have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, deploying these models for real-world applications often requires efficient inference solutions…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-06-13 Ditto PS , Jithin VG , Adarsh MS

Large language models (LLMs) offer remarkable capabilities, yet their high inference costs restrict wider adoption. While increasing parameter counts improves accuracy, it also broadens the gap between state-of-the-art capabilities and…

This paper proposes Scalene, a profiler specialized for Python. Scalene combines a suite of innovations to precisely and simultaneously profile CPU, memory, and GPU usage, all with low overhead. Scalene's CPU and memory profilers help…

Programming Languages · Computer Science 2023-03-24 Emery D. Berger , Sam Stern , Juan Altmayer Pizzorno

Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-data performance of the full tune-then-deploy procedure. We…

Machine Learning · Statistics 2026-05-08 Yang Xu , Jiefu Zhang , Haixiang Sun , Zihan Zhou , Tianyu Cao , Vaneet Aggarwal

Hallucinations in Large Language Models (LLMs) -- generations that are plausible but factually unfaithful -- remain a critical barrier to high-stakes deployment. Current detection methods typically rely on computationally expensive external…

Artificial Intelligence · Computer Science 2026-01-23 Manish Bhatt

Inference-time methods that aggregate and prune multiple samples have emerged as a powerful paradigm for steering large language models, yet we lack any principled understanding of their accuracy-cost tradeoffs. In this paper, we introduce…

Writing high-performance code requires significant expertise in the programming language, compiler optimizations, and hardware knowledge. This often leads to poor productivity and portability and is inconvenient for a non-programmer…

Performance · Computer Science 2020-09-01 Ajitesh Srivastava , Naifeng Zhang , Rajgopal Kannan , Viktor K. Prasanna

To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high-accuracy large language models (LLMs) and vision language models (VLMs), jointly referred to as xLMs, on client…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-04-30 Aditya Ukarande , Deep Shekhar , Marc Blackstein , Ram Rangan

Programmable Logic Controllers (PLCs) provide a prominent choice of implementation platform for safety-critical industrial control systems. Formal verification provides ways of establishing correctness guarantees, which can be quite…

Programming Languages · Computer Science 2019-02-15 Luis Garcia , Stefan Mitsch , Andre Platzer

We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of…

Artificial Intelligence · Computer Science 2010-06-03 Nan Li , William Cushing , Subbarao Kambhampati , Sungwook Yoon

Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD, combines statistical and logical knowledge representation and inference. Inference in these…

Artificial Intelligence · Computer Science 2012-03-21 Muhammad Asiful Islam , C. R. Ramakrishnan , I. V. Ramakrishnan

Deep reinforcement learning (DRL) has led to a wide range of advances in sequential decision-making tasks. However, the complexity of neural network policies makes it difficult to understand and deploy with limited computational resources.…

Machine Learning · Computer Science 2023-11-07 Jiaming Guo , Rui Zhang , Shaohui Peng , Qi Yi , Xing Hu , Ruizhi Chen , Zidong Du , Xishan Zhang , Ling Li , Qi Guo , Yunji Chen

Probabilistic programs provide an expressive representation language for generative models. Given a probabilistic program, we are interested in the task of posterior inference: estimating a latent variable given a set of observed variables.…

Machine Learning · Computer Science 2022-09-01 Mike Wu , Noah Goodman