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Machine Learning
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Newest papers, page 169
Copying Under Repetition: From Prefix Matching to Position Hashing
Frontier LLMs can copy random text almost flawlessly, yet they often overrun or truncate long blocks of repeated characters.Injection of Space Radiation Errors on Low-power NVM using Generative Deep Learning
We develop an error model for a Non-Volatile Memory (NVM) device that represents the effects of physical faults experienced in a CubeSat mission in Low Earth Orbit (LEO).Cross-Fitting for Neural Posterior Estimation
Neural Posterior Estimation (NPE) fits an amortized conditional density estimator to a simulated training set of complete-data realizations.CAVA: Complementary-Label-Aware Data Valuation
Complementary-label learning (CLL) trains a classifier from labels that name a class an instance does not belong to.Polar Switch: Fast Muon Orthogonalization with Polynomial Circuits
Muon, a 2024-born optimizer for language models, differs from the 2015-born spectral gradient descent mainly in its fast, GPU-friendly Newton--Schulz polynomial for orthogonalizing the gradient…recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models
Protein language models (pLMs) are powerful tools for protein annotation and mutation-effect prediction, but progress has largely depended on scaling model size and data, making them expensive and…Gradient descent reaches arbitrary accuracy in a fixed number of large steps on multiclass logistic regression
We show that gradient descent (GD) with step size $η$ can achieve $\varepsilon$ optimality gap on the multiclass logistic regression loss in a number of iterations $T$ that is independent of…Trajectory Replay: Continuous-Time Mixture Transport for Lifelong Generative Models
Adapting pre-trained continuous-time generative models to sequentially incoming data without forgetting earlier tasks is a central challenge in lifelong learning.Simulation Priors for Rapid Adaptation in Model-Based RL for Autonomous Racing
Reinforcement learning enables learning high-performance control policies, but its reliance on extensive interaction limits deployment on physical systems where data collection is costly, and…HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offs
Harness development plays a crucial role in building capable AI agents.Topological Sheaves of Irreducibility: A Theoretical Framework for Multi-Agent Task Obstructions
Multi-agent systems can differ under identical agents and communication budgets when they support different forms of joint interaction.Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books
A market maker provides liquidity by quoting prices to buy and sell an asset, earning the spread while managing inventory risk.Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization
Training a machine learning (ML) model is an optimization process that typically requires a set of examples with ground-truth labels beforehand.On Data Scaling in Tabular ML: More Data, Better Generalization?
Recent work in tabular machine learning has emphasized the importance of diverse and realistic evaluation settings.Two hidden layer suffice: the caterpillar net
Every continuous piecewise linear (CPWL) function is a finite signed sum of maxima of affine functions, making maximum representation a central problem in the study of the expressiveness of ReLU…Teacher Supervision, Fixed Representations and Calibration: Exact Risk Calculations for Candidate-Scoring Decision Models
Candidate-scoring decision models return a probability distribution over a presented set of answers.Learning to Refer: Inference-Time Collaboration in Decentralized Federated Learning
In decentralized federated learning, heterogeneous clients may develop complementary expertise, yet each query is typically evaluated only by the client where it arrives.Scaling Parameter-Efficient Fine-Tuning for Brain Encoding
Brain encoding models aim to predict neural responses to stimuli.Next Best Action: Causal Constrained Offline RL for Business Action Targeting
Next Best Action (NBA) systems must select among customer interventions (e.g., discounts, promotions, and lifecycle actions), maximize long-term economic value, satisfy complex operational…MemT-1.0: Towards Memory-Native Evolution
Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information.