Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
arXiv:2603.05917v1 Announce Type: new Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market environments characterized by noise, non-stationarity, and behavioral dynamics. Traditional forecasting methods often fail to capture the intricate patterns and...
Design Experiments to Compare Multi-armed Bandit Algorithms
arXiv:2603.05919v1 Announce Type: new Abstract: Online platforms routinely compare multi-armed bandit algorithms, such as UCB and Thompson Sampling, to select the best-performing policy. Unlike standard A/B tests for static treatments, each run of a bandit algorithm over $T$ users produces...
Weak-SIGReg: Covariance Regularization for Stable Deep Learning
arXiv:2603.05924v1 Announce Type: new Abstract: Modern neural network optimization relies heavily on architectural priorssuch as Batch Normalization and Residual connectionsto stabilize training dynamics. Without these, or in low-data regimes with aggressive augmentation, low-bias architectures like Vision Transformers (ViTs) often suffer...
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
arXiv:2603.06009v1 Announce Type: new Abstract: Plateaus, where an agent's performance stagnates at a suboptimal level, are a common problem in deep on-policy RL. Focusing on PPO due to its widespread adoption, we show that plateaus in certain regimes arise not...
Agnostic learning in (almost) optimal time via Gaussian surface area
arXiv:2603.06027v1 Announce Type: new Abstract: The complexity of learning a concept class under Gaussian marginals in the difficult agnostic model is closely related to its $L_1$-approximability by low-degree polynomials. For any concept class with Gaussian surface area at most $\Gamma$,...
Dynamic Momentum Recalibration in Online Gradient Learning
arXiv:2603.06120v1 Announce Type: new Abstract: Stochastic Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization, yet the underlying dynamics of their gradient behavior remain insufficiently understood. In this work, we reinterpret gradient updates through the...
Partial Policy Gradients for RL in LLMs
arXiv:2603.06138v1 Announce Type: new Abstract: Reinforcement learning is a framework for learning to act sequentially in an unknown environment. We propose a natural approach for modeling policy structure in policy gradients. The key idea is to optimize for a subset...
Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
arXiv:2603.06153v1 Announce Type: new Abstract: Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with...
OpenAI robotics lead Caitlin Kalinowski quits in response to Pentagon deal
Hardware executive Caitlin Kalinowski announced today that in response to OpenAI's controversial agreement with the Department of Defense, she’s resigned from her role leading the company's robotics team.
OpenAI delays ChatGPT’s ‘adult mode’ again
The feature, which will give verified adult users access to erotica and other adult content, had already been delayed from December.
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Proceedings of the Natural Legal Language Processing Workshop 2023
This talk situates the rising field of NLLP in the context of legal scholarship and practice.It will examine how the field relates to existing inquiries in computational law, AI and Law, and computational/empirical legal studies.Similarities, differences, and opportunities for cross-fertilization...
Algorithmic Bias in Hiring Algorithms: A Kenyan Perspective
The use of machine learning algorithms has permeated into nearly all aspects of life. With this steady integration, tasks previously handled by humans are increasingly falling into the ‘hands’ of machines. Ideally this would be celebrated as a great improvement...
Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?
We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier...
Headnotes Archive - Minnesota Law Review
Volume 109 Fall Issue Spring Issue Volume 108 Fall Issue Spring Issue Symposium Supplement Volume 107 Fall Issue Spring Issue Volume 106 Fall Issue Spring Issue Volume 105 Fall Issue Spring Issue Volume 104 Compendium: Election Law and in the...
LL.M. Program
What Sets Vanderbilt's LL.M. Program Apart? At Vanderbilt Law, you can customize your legal education, prepare for a bar exam, and improve your language skills. Course Tracks and Customizable Curriculums At Vanderbilt, students have the power to choose what they...
Online Courts and the Future of Justice
In Online Courts and the Future of Justice, Richard Susskind, the world’s most cited author on the future of legal services, shows how litigation will be transformed by technology and proposes a solution to the global access-to-justice problem. In most...
Closed for Business – Open for Litigation?
Can a business-closure regulation of commercial property in a pandemic be a taking? In the midst of a pandemic, it generally falls to government to enact laws and regulations in an effort to curtail the spread of disease. For example,...
Office of Culture & Community
Our community brings together those with diverse backgrounds, perspectives, identities, and preferences, and each member contributes to school life through their own distinctive set of viewpoints, experiences, and ideas.We celebrate this diversity. We cherish it.We believe that it can fully...
AI ethics and data governance in the geospatial domain of Digital Earth
Digital Earth applications provide a common ground for visualizing, simulating, and modeling real-world situations. The potential of Digital Earth applications has increased significantly with the evolution of artificial intelligence systems and the capacity to collect and process complex amounts of...
Scholarship@Vanderbilt Law
<div id="homepage-intro"> <p>Vanderbilt Law School's national reputation has its basis in our rigorous academic program, which is developed and delivered by our faculty of top legal scholars. A Vanderbilt legal education prepares you for the complete spectrum of career opportunities...
The intellectual property road to the knowledge economy: remarks on the readiness of the UAE Copyright Act to drive AI innovation
Copyright law in the United Arab Emirates (UAE) has the capacity to address the challenges associated with artificial intelligence (AI)-generated literary, artistic and scientific works. Under UAE copyright law, AI-generated works may qualify as copyright subject matter despite the non-human...
The intersection of AI and legal expertise: Transforming knowledge work in the legal profession
This article explores the transformative impact of artificial intelligence on legal knowledge work, examining the evolution from traditional document-centric processes to sophisticated AI-augmented workflows. The article shows the technological foundations of legal AI systems, highlighting the capabilities and limitations of...
2025-26 Symposium - Minnesota Law Review
The Minnesota Law Review invites you to attend the Vol. 110 Symposium, "The Battle Will Not Be Over": 60 Years of the Voting Rights Act. As Lyndon B. Johnson signed the historic Voting Rights Act of 1965, he warned that...
Natural Language Processing for Legal Texts
Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law. Natural language processing converts unstructured text into a formal representation that computers can understand and analyze. This technology...
How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem
As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s...
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law
Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI) systems have recently received increased attention from both legal and computer science scholars. Yet, the degree of overlap between notions of algorithmic bias and fairness on the one...
AI copyright policy considerations for Botswana and South Africa – Compensation for starving artists feeding generative AI
The balancing act which domestic intellectual property policy is now challenged to strike is between fostering growth in technological innovation and incentivising creative labour. Ordinarily, these two considerations should not be mutually exclusive, but generative artificial intelligence (Gen AI) has...
Gradient Legal Personhood for AI Systems—Painting Continental Legal Shapes Made to Fit Analytical Molds
What I propose in the present article are some theoretical adjustments for a more coherent answer to the legal “status question” of artificial intelligence (AI) systems. I arrive at those by using the new “bundle theory” of legal personhood, together...
Prediction, persuasion, and the jurisprudence of behaviourism
There is a growing literature critiquing the unreflective application of big data, predictive analytics, artificial intelligence, and machine-learning techniques to social problems. Such methods may reflect biases rather than reasoned decision making. They also may leave those affected by automated...