Mixed-Integer Optimization for Responsible Machine Learning

• URL: http://arxiv.org/abs/2505.05857v1
• Date: Fri, 09 May 2025 07:51:36 GMT
• Title: Mixed-Integer Optimization for Responsible Machine Learning
• Authors: Nathan Justin, Qingshi Sun, Andrés Gómez, Phebe Vayanos,
• Abstract summary: Mixed-integer optimization (MIO) offers a powerful framework for embedding responsible ML considerations directly into the learning process.<n>This tutorial paper provides an accessible and comprehensive introduction to this topic discussing both theoretical and practical aspects.
• Score: 5.282840081123424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice and finance. But its deployment in increasingly sophisticated, critical, and sensitive areas affecting individuals, the groups they belong to, and society as a whole raises critical concerns around fairness, transparency, robustness, and privacy, among others. As the complexity and scale of ML systems and of the settings in which they are deployed grow, so does the need for responsible ML methods that address these challenges while providing guaranteed performance in deployment. Mixed-integer optimization (MIO) offers a powerful framework for embedding responsible ML considerations directly into the learning process while maintaining performance. For example, it enables learning of inherently transparent models that can conveniently incorporate fairness or other domain specific constraints. This tutorial paper provides an accessible and comprehensive introduction to this topic discussing both theoretical and practical aspects. It outlines some of the core principles of responsible ML, their importance in applications, and the practical utility of MIO for building ML models that align with these principles. Through examples and mathematical formulations, it illustrates practical strategies and available tools for efficiently solving MIO problems for responsible ML. It concludes with a discussion on current limitations and open research questions, providing suggestions for future work.

Related papers

• Planning without Search: Refining Frontier LLMs with Offline Goal-Conditioned RL [62.984693936073974]
  Large language models (LLMs) excel in tasks like question answering and dialogue.<n>Complex tasks requiring interaction, such as negotiation and persuasion, require additional long-horizon reasoning and planning.<n>We propose a novel approach that uses goal-conditioned value functions to guide the reasoning of LLM agents.
  arXiv  Detail & Related papers  (2025-05-23T16:51:54Z)
• A Call for New Recipes to Enhance Spatial Reasoning in MLLMs [85.67171333213301]
  Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks.<n>Recent studies have exposed critical limitations in their spatial reasoning capabilities.<n>This deficiency in spatial reasoning significantly constrains MLLMs' ability to interact effectively with the physical world.
  arXiv  Detail & Related papers  (2025-04-21T11:48:39Z)
• Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models [91.24296813969003]
  This paper advocates integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML.<n>We argue that a causal approach is essential for balancing multiple competing objectives in both trustworthy ML and foundation models.
  arXiv  Detail & Related papers  (2025-02-28T14:57:33Z)
• An Overview of Large Language Models for Statisticians [109.38601458831545]
  Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)<n>This paper explores potential areas where statisticians can make important contributions to the development of LLMs.<n>We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
  arXiv  Detail & Related papers  (2025-02-25T03:40:36Z)
• Benchmarking Large and Small MLLMs [71.78055760441256]
  Large multimodal language models (MLLMs) have achieved remarkable advancements in understanding and generating multimodal content.<n>However, their deployment faces significant challenges, including slow inference, high computational cost, and impracticality for on-device applications.<n>Small MLLMs, exemplified by the LLava-series models and Phi-3-Vision, offer promising alternatives with faster inference, reduced deployment costs, and the ability to handle domain-specific scenarios.
  arXiv  Detail & Related papers  (2025-01-04T07:44:49Z)
• A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
  Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks.<n>LLMs have demonstrated emergent abilities extending beyond their core functions.<n>This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
  arXiv  Detail & Related papers  (2025-01-03T21:04:49Z)
• Analyzing Fairness of Computer Vision and Natural Language Processing Models [1.0923877073891446]
  Machine learning (ML) algorithms play a crucial role in decision making across diverse fields such as healthcare, finance, education, and law enforcement.<n>Despite their widespread adoption, these systems raise ethical and social concerns due to potential biases and fairness issues.<n>This study focuses on evaluating and improving the fairness of Computer Vision and Natural Language Processing (NLP) models applied to unstructured datasets.
  arXiv  Detail & Related papers  (2024-12-13T06:35:55Z)
• Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach [0.0]
  In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. In practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment.
  arXiv  Detail & Related papers  (2024-10-28T09:34:08Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• A Reality check of the benefits of LLM in business [1.9181612035055007]
  Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks. This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
  arXiv  Detail & Related papers  (2024-06-09T02:36:00Z)
• The Impossibility of Fair LLMs [59.424918263776284]
  The need for fair AI is increasingly clear in the era of large language models (LLMs) We review the technical frameworks that machine learning researchers have used to evaluate fairness. We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• From Understanding to Utilization: A Survey on Explainability for Large Language Models [27.295767173801426]
  This survey underscores the imperative for increased explainability in Large Language Models (LLMs) Our focus is primarily on pre-trained Transformer-based LLMs, which pose distinctive interpretability challenges due to their scale and complexity. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement.
  arXiv  Detail & Related papers  (2024-01-23T16:09:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.