Machine Learning

81 papers

Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.

Latest in Machine Learning

Showing 21–40 of 81

Apr 22, 2026Formal Sciences260423.0023

MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment

Andor Vári-Kakas, Ji Won Park, Natasa Tagasovska

MGDA-Decoupled is a geometry-based multi-objective optimization algorithm for aligning LLMs that balances conflicting objectives like helpfulness and truthfulness without requiring reinforcement learning or explicit reward models. It operates within the Direct Preference Optimization framework and achieves higher win rates than existing methods by accounting for each objective's convergence dynamics.

Apr 22, 2026Formal Sciences260423.0024

Variance Is Not Importance: Structural Analysis of Transformer Compressibility Across Model Scales

Samuel Salfati

This paper systematically studies transformer compression across GPT-2 and Mistral 7B, identifying five structural properties that explain why certain compression techniques fail, particularly showing that high-variance activations are not predictively important and that block-level approximations suffer from distribution shift. The findings suggest fundamental limits to static compression and motivate adaptive, per-token approaches instead.

Apr 22, 2026Formal Sciences260423.0025

Improving clinical interpretability of linear neuroimaging models through feature whitening

Sara Petiton, Antoine Grigis, Raphaël Vock, Edouard Duchesnay

This paper proposes a feature whitening approach that decorrelates anatomically grouped brain regions to improve the interpretability of linear neuroimaging models while maintaining predictive performance. The method helps disentangle overlapping information across correlated brain measures, making model weights more clinically meaningful for identifying biomarkers in psychiatric disorders.

Apr 22, 2026Formal Sciences260423.0026

GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning

Jingyi Wang, Lei Zhu, Tengjin Weng, Song-Li Wu +7 more

This paper introduces GRPO-VPS, which enhances Group Relative Policy Optimization by adding verifiable process supervision that tracks the model's confidence in correct answers throughout reasoning steps. This enables more targeted credit assignment and sample-efficient policy updates without requiring critic models or auxiliary supervision.

Apr 22, 2026Formal Sciences260423.0028

Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning

Aravind Venugopal, Jiayu Chen, Xudong Wu, Chongyi Zheng +2 more

This paper proposes Occupancy Reward Shaping (ORS), a method that extracts temporal and geometric information from learned world models to create reward functions that improve credit assignment in offline goal-conditioned reinforcement learning. The approach uses optimal transport on occupancy measures and demonstrates 2.2x performance improvements across diverse tasks including real-world Tokamak control.

Apr 22, 2026Formal Sciences260423.0029

pAI/MSc: ML Theory Research with Humans on the Loop

Mahmoud Abdelmoneum, Pierfrancesco Beneventano, Tomaso Poggio

pAI/MSc is an open-source multi-agent system designed to assist researchers in ML theory by automating the conversion of hypotheses into manuscript drafts, significantly reducing manual steering effort. The system emphasizes human-in-the-loop collaboration rather than fully autonomous research.

Apr 22, 2026Formal Sciences260423.0030

Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge

Naizhong Xu

SmartVector augments vector embeddings with temporal awareness, confidence decay, and relational properties to improve retrieval-augmented generation accuracy on versioned knowledge. The framework doubles accuracy on versioned queries and reduces stale answers by 62% compared to standard cosine similarity retrieval.

Apr 22, 2026Formal Sciences260423.0031

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

Jie Xu, Haaris Mehmood, Rogier Van Dalen, Karthikeyan Saravanan +1 more

PINA is a differentially private clustered federated learning framework that uses low-rank adapters and compressed sketches for privacy-preserving initialization, followed by normality-driven aggregation to improve convergence. It achieves 2.9% higher accuracy than state-of-the-art DP-FL methods while maintaining formal privacy guarantees.

Apr 22, 2026Formal Sciences260423.0032

An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling

Anif N. Shikder, Ramit Dey, Sayantan Auddy, Luisa Liboni +5 more

This paper establishes a mathematical correspondence between state space models (like S4) and nonlinear oscillator networks, deriving an exact operator expression for S4D's forward pass. This provides analytical interpretability for modern sequence models by revealing how information propagates as waves in a ring network topology.

Apr 22, 2026Formal Sciences260423.0033

A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs

Patrick Wilk, Ethan Cantor, Yikui Liu, Jie Li

This paper proposes a hierarchical multi-agent deep reinforcement learning framework that enables distributed energy resources to participate in both peer-to-peer retail trading and wholesale electricity markets through coordinated prosumer aggregation. The approach uses a Stackelberg game to optimize market performance and grid flexibility.

Apr 22, 2026Formal Sciences260423.0034

Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis

Fariz Ikhwantri, Dusica Marijan

This paper proposes a graph neural network framework to analyze assurance cases (structured safety argument documents) for link prediction between argument elements and detection of LLM-generated versus human-authored cases. The work reveals that LLM-generated assurance cases have distinct structural patterns and highlights limitations in GNN explanation methods.

Apr 22, 2026Formal Sciences260423.0035

Amortized Vine Copulas for High-Dimensional Density and Information Estimation

Houman Safaai

This paper proposes Vine Denoising Copula (VDC), an amortized approach to vine copulas that trains a single bivariate denoising model and reuses it across all vine edges, enabling fast and tractable high-dimensional density and information estimation. The method combines neural flexibility with classical copula interpretability while achieving significant computational speedups.

Apr 22, 2026Formal Sciences260423.0038

CHASM: Unveiling Covert Advertisements on Chinese Social Media

Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun +4 more

This paper introduces CHASM, a dataset of 4,992 annotated instances from Chinese social media for detecting covert advertisements disguised as regular posts. The study reveals that current multimodal large language models fail to reliably identify these deceptive advertisements, highlighting a critical gap in content moderation capabilities.

Apr 22, 2026Formal Sciences260423.0039

Explicit Dropout: Deterministic Regularization for Transformer Architectures

Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis

This paper reformulates dropout as a deterministic regularization term added directly to the training loss instead of using stochastic masking, providing fine-grained control over regularization in Transformers. Experiments show the approach matches or outperforms standard dropout across image, video, and audio tasks.

Apr 22, 2026Formal Sciences260423.0040

Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees

Xueyan Li, Johannes Zenn, Ekaterina Fadeeva, Guinan Su +2 more

DLE is a deterministic decoding method that systematically explores distinct reasoning paths in a truncated decoding tree instead of sampling with replacement, improving inference efficiency and performance on math, coding, and reasoning tasks.

Apr 22, 2026Formal Sciences260423.0041

Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks

Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo, Antonino Nocera +1 more

This paper proposes a certifiably robust malware detection framework using randomized smoothing through feature ablation and noise injection to provide formal guarantees against adversarial evasion attacks. The approach achieves provable robustness without modifying the underlying ML classifier.

Apr 22, 2026Formal Sciences260423.0043

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

Georgios Anyfantis, Pere Barlet-Ros

This paper proposes a Graph Neural Network model to predict network flow-level traffic (NetFlow) by representing network traffic as heterogeneous graphs with IP, Port, and Connection nodes. The approach demonstrates strong performance in identifying connection endpoints while maintaining competitive feature reconstruction compared to traditional forecasting baselines.

Apr 22, 2026Formal Sciences260423.0044

Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models

Shelly Francis-Meretzki, Mirco Mutti, Yaniv Romano, Aviv Tamar

This paper proposes temporal-difference calibration for vision-language-action models in robotics, connecting uncertainty calibration to reinforcement learning by showing that minimizing a sequential Brier score recovers the value function. The method improves calibration of task-success confidence in sequential decision-making tasks.

Apr 22, 2026Formal Sciences260423.0045

Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory

Roman Remme, Fred A. Hamprecht

This paper introduces surrogate functionals, machine-learned energy functionals for orbital-free density functional theory that are optimized to produce correct ground-state densities rather than matching a physical reference. The approach requires only ground-state density data and achieves competitive accuracy while improving computational efficiency.

Apr 22, 2026Formal Sciences260423.0046

The Origin of Edge of Stability

Elon Litman

This paper explains why full-batch gradient descent on neural networks drives the largest Hessian eigenvalue toward the threshold 2/η (the Edge of Stability phenomenon) through a novel functional called edge coupling. The work provides a unified theoretical explanation for this self-regulating behavior that has previously resisted complete understanding.