arXiv:2602.05012v1 Announce Type: new Abstract: In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks with only a small set of examples at inference time, thereby avoiding task-specific fine-tuning. However, in-context examples may contain privacy-sensitive information that should not be revealed through model outputs. Existing differential privacy (DP) approaches to ICL are either computationally expensive or rely on heuristics with limited effectiveness, including context oversampling, synthetic data generation, or unnecessary thresholding. We reformulate private ICL […]

Ver mais

We consider a supervised learning setting in which the main cost of learning is the number of training labels and one can obtain a single label for a bag of examples, indicating only if a positive example exists in the bag, as in Multi-Instance Learning. We thus propose to create a training sample of bags, and to use the obtained labels to learn to classify individual examples. We provide a theoretical analysis showing how to select the bag […]

Ver mais

arXiv:2602.03876v1 Announce Type: new Abstract: Standard reinforcement learning from human feedback (RLHF) trains a reward model on pairwise preference data and then uses it for policy optimization. However, while reward models are optimized to capture relative preferences, existing policy optimization techniques rely on absolute reward magnitudes during training. In settings where the rewards are non-verifiable such as summarization, instruction following, and chat completion, this misalignment often leads to suboptimal performance. We introduce Group Ordinal Policy Optimization (GOPO), a […]

Ver mais

Deep learning, despite its remarkable successes, is a young field – perhaps ten years old. While models called artificial neural networks have been studied for decades, much of that work seems only tenuously connected to modern results. It’s often the case that young fields start in a very ad-hoc manner. Later, the mature field is understood very differently than it was understood by its early practitioners. It seems quite likely that deep learning is in this ad-hoc state… […]

Ver mais

arXiv:2602.22277v1 Announce Type: new Abstract: AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based XAI solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both […]

Ver mais

The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

Ver mais

The Early Days of Infrastructure Operations Imagine you’re the person responsible for managing and maintaining the servers for a company’s website or application. This used to be a simple job, but with technological advancements, infrastructure management has become more complex. In the early days, system administrators often logged into servers manually via SSH (Secure Shell) to configure servers, install software, or solve problems. SSH: The Initial Solution SSH changed everything in the 1990s. Before SSH, people had to […]

Ver mais

arXiv:2601.19755v1 Announce Type: new Abstract: We propose a framework to construct practical kernel-based two-sample tests from the family of $f$-divergences. The test statistic is computed from the witness function of a regularized variational representation of the divergence, which we estimate using kernel methods. The proposed test is adaptive over hyperparameters such as the kernel bandwidth and the regularization parameter. We provide theoretical guarantees for statistical test power across our family of $f$-divergence estimates. While our test covers a […]

Ver mais

Anthropic CEO Dario Amodei said Thursday that he “cannot in good conscience accede” to the Pentagon’s demands to give the military unrestricted access to its AI systems.

Ver mais