Machine Learning

cs.LGComputer Science60 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 cs.LG

Showing 21–40 of 60

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 Sciences260423.0031

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 Sciences260423.0033

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 Sciences260423.0035

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 Sciences260423.0045

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.

Apr 22, 2026Formal Sciences260423.0048

Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]

Huaiyu Jia, Luofeng Zhou, Wentao Zhang, Lin William Cong +2 more

This paper introduces the first comprehensive dataset suite covering the complete lifecycle of decentralized prediction markets on Polymarket, integrating fragmented on-chain and off-chain data from October 2020 to March 2026. The unified dataset enables systematic analysis of market dynamics and supports downstream applications like outcome calibration and economic expectation reconstruction.

Apr 22, 2026Formal Sciences260423.0049

Scalable AI Inference: Performance Analysis and Optimization of AI Model Serving

Hung Cuong Pham, Fatih Gedikli

This paper evaluates and optimizes a BentoML-based AI inference system for scalable model serving, testing it under realistic workloads and applying multi-level optimizations to improve latency and throughput. The work bridges the gap between AI model research and practical deployment by providing concrete performance analysis and optimization strategies.

Apr 22, 2026Formal Sciences260423.0050

Calibrating conditional risk

Andrey Vasilyev, Yikai Wang, Xiaocheng Li, Guanting Chen

This paper introduces conditional risk calibration, the problem of estimating a model's expected loss conditional on input features, and shows it is equivalent to regression while being distinct from probability calibration. The work provides theoretical analysis and demonstrates practical applications in learning to defer frameworks.

Apr 20, 2026Formal Sciences260421.0039

Sessa: Selective State Space Attention

Liubomyr Horbatko

Sessa is a new sequence model that places attention inside a recurrent feedback path, enabling power-law memory decay instead of exponential or 1/length dilution. This architecture achieves superior long-context performance while remaining competitive on short sequences.

Apr 20, 2026Formal Sciences260421.0040

Bounded Ratio Reinforcement Learning

Yunke Ao, Le Chen, Bruce D. Lee, Assefa S. Wahd +4 more

This paper introduces Bounded Ratio Reinforcement Learning (BRRL), a theoretical framework that bridges the gap between trust region methods and PPO's clipped objective, leading to a new algorithm called Bounded Policy Optimization (BPO) that provides monotonic improvement guarantees while matching or exceeding PPO's performance. The framework also extends to Group-relative BPO (GBPO) for large language model fine-tuning.

Apr 20, 2026Formal Sciences260421.0041

When Can LLMs Learn to Reason with Weak Supervision?

Salman Rahman, Jingyan Shen, Anna Mordvina, Hamid Palangi +2 more

This paper investigates when reinforcement learning with verifiable rewards (RLVR) enables large language models to generalize under weak supervision (scarce data, noisy rewards, or self-supervised signals). The key finding is that models generalize when they exhibit prolonged pre-saturation training dynamics, which is predicted by reasoning faithfulness—the degree to which intermediate reasoning steps logically support final answers.

Apr 20, 2026Formal Sciences260421.0044

Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering

Manan Gupta, Dhruv Kumar

LPSR is an inference-time error correction method that monitors internal model activations to detect reasoning mistakes, then rolls back generation and steers the model using cached corrections—no training required. It improves an 8B model's math accuracy from 28.8% to 44.0%, outperforming prompted self-correction and even larger 70B models.

Apr 20, 2026Formal Sciences260421.0047

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work

Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson, Michael Mitzenmacher +2 more

This note demonstrates that TurboQuant, a recent quantization method, is a suboptimal special case of the earlier EDEN/DRIVE quantization schemes. EDEN consistently outperforms TurboQuant across all tested scenarios, often by more than one bit of precision.

Apr 20, 2026Formal Sciences260421.0048

Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems

William Lavery, Jodie A. Cochrane, Christian Olesen, Dagim S. Tadele +2 more

This paper extends biologically-informed neural networks (BINNs) from 1D to 2D spatial domains for learning reaction-diffusion equations from data, combining neural network training with symbolic regression to discover closed-form equations. The method is demonstrated on real lung cancer cell microscopy data, successfully recovering interpretable 2D+time reaction-diffusion models.

Apr 20, 2026Formal Sciences260421.0050

Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk

Feras Al Taha, Eilyan Bitar

This paper develops a distributionally robust estimation method that minimizes worst-case conditional value-at-risk (CVaR) of estimation error when the true distribution is uncertain but lies within a Wasserstein ball. The method can be computed via tractable semidefinite programming and outperforms existing approaches on electricity price forecasting.

Apr 20, 2026Formal Sciences260421.0052

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis +6 more

IDOBE is a standardized benchmark dataset containing over 10,000 infectious disease outbreak segments from a century of surveillance data across 13 diseases, designed to evaluate and compare epidemic forecasting methods. The authors test 11 baseline models and find MLP-based methods perform most robustly, with statistical methods excelling in pre-peak phases.