Machine Learning and Data Classification

Neueste Paper

• A Generalisation Signal Need Not Be a Model-Selection Signal

  Aditya Nagarsekar, M P Ashish Bhat, Aadi Nesarkar, Vrishti Godhwani, Rahul Yedida, Aditya Challa, Danda Sravan, Snehanshu Saha · 1. Oktober 2026

  Model selection in computational biology often relies on validation data drawn from the training regime, even when deployment lies outside it. When validation no longer preserves which model is best, a natural alternative is to rank candidates using properties of the trained network itself. We test …

• Signal-Routed Temperature Scaling: Low-Capacity Risk-Conditioned Calibration for Small Validation Budgets

  Wenhao Liang, Liangwei Nathan Zheng, Lin Yue, Wei Emma Zhang, Mingyu Guo, Olaf Maennel, Weitong Chen · 1. Oktober 2026

  When a classifier is recalibrated from only a few thousand held-out examples, the capacity of the calibration map becomes a statistical design choice rather than a purely architectural one: a scalar map can underfit structured residual miscalibration, while a highly adaptive map can be hard to estim…

• LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization

  Shihao Zhang, Weiting Liu, Siyu Shao, Yitian Chen, Jianfeng Feng, Dongdong Ge, Yinyu Ye · 30. September 2026

  Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are predominantly evaluated on small, self-contained textual problems and often commit to a solver-integrated paradigm, limiting their ability to handle…

• CPATTA: Conformal Supervision Allocation For Active Test-Time Adaptation

  Tingyu Shi, Fan Lyu, Haihua Zhu, Dadi Wang, Shaoliang Peng · 30. September 2026

  Active Test-Time Adaptation (ATTA) improves model robustness under domain shift by selectively querying human annotations at deployment, but existing methods use heuristic uncertainty measures and suffer from low data selection efficiency, wasting human annotation budget. We propose Conformal Predic…

• From Checkpoint Variation to Selection Gains in Supervised Fine-Tuning

  Yupeng Chang, Wenxuan Zhang, Yuan Wu · 30. September 2026

  Checkpoint selection is a routine decision in supervised fine-tuning (SFT): training produces multiple checkpoints, but only one is retained. Yet fixed-budget comparisons do not by themselves distinguish three empirical claims: whether more validation data improve checkpoint selection, whether a sel…

• When Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labeling

  Ping Guo, Zhiqi Huang, Xinran Li · 29. September 2026

  Pseudo-labeling has become a cornerstone of learning from unlabeled data in semantic segmentation. Yet its effectiveness drops sharply in real-world scenarios where strong imaging noise and long-tailed class distributions occur together. We trace this failure to a vicious cycle of pseudo-label degra…

• Refreshing Less, Selecting Better: Reusing Stale Gradient Features for Efficient Influence-Based Data Selection

  Jianchang Su, Yifan Zhang, Wei Zhang · 29. September 2026

  Gradient-based data selection methods such as LESS score each candidate by the alignment between its gradient and a target validation gradient, and recomputing per-example gradient features at every new checkpoint dominates their cost. Across three selection seeds, two model families, two candidate …

• A Solvable Theory of Pre-training Data Poisoning: Regime-Dependent Scaling Exponents

  Indranil Halder, Rastri Dey, Cengiz Pehlevan · 29. September 2026

  Pre-training data poisoning of large language models is usually studied using targeted backdoors and their survival through safety post-training, which leaves open a more basic question: how does a model's clean data performance degrade as the poison rate $\varepsilon$ grows? Motivated by our contro…

• GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation

  Seyedeh Sara Jalili Shani (Department of Computer Science, University of Alberta, Alberta, Canada), Rouhollah Ahmadian (Department of Mathematics and Computer Science, Amirkabir University of Technology, Iran), Amin Rahmani (Department of Mathematics and Computer Science, Amirkabir University of Technology, Iran), Mahdi Bideh (Department of Mathematics and Computer Science, Amirkabir University of Technology, Iran), Mehdi Ghatee (Department of Mathematics and Computer Science, Amirkabir University of Technology, Iran) · 29. September 2026

  In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems. During augmentation, synthetic license plate images are generated and transformed using stochastic noise to simulate real-world conditions. However,…

• SpecRegMatch: Robust Semi-Supervised Regression for Vehicle Interior Noise Prediction

  Sejin Sim, Jinsoo Bae, Seoung Bum Kim · 29. September 2026

  The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive industry. However, the collection of labeled data for training models in this context involves significant costs. Previous studies in semi-supervised …

• Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

  Qinwu Xu · 28. September 2026

  We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method searches for an interpretable conjunction of feature--threshold rules that corrects residual false-positive or false-negative errors while explicitly co…

• Teacher-Anchored Selection of Post-Training Quantized Models under Domain Shift

  Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong · 28. September 2026

  Compressing a trained model yields a family of deployment candidates, and under domain shift the most compressed one need not be the one to deploy. We study selection over such a family, with candidates and teacher fixed and target labels absent or scarce. Two findings organize the label-free case. …

• Let Training Guide Selection: Online Synthetic Data Filtering via Real-Anchored Utility

  Yanran Wu, Sana Lakdawala, Renzo Tassara Miller, Chongyang Bai, Sharath Ciddu, Shivendra Pratap Singh, Kungang Li, Sandeep Pandey, Chunwei Liu · 25. September 2026

  Synthetic data can scale training supervision when real-world data are limited, but noise and distribution mismatch can reduce its value. Existing synthetic data selection methods often emphasize fidelity or diversity rather than the learner's evolving needs. We propose FROST, an online framework th…

• Speculative Evaluation of Stochastic LLMs

  Qianli Shen, Xiang Li, Ruomeng Ding, Yanxi Chen, Daoyuan Chen, Yaliang Li · 25. September 2026

  Evaluating a stochastic large language model is costly: benchmark scores estimate expected performance from randomized rollouts, yet uniform repetition ignores sharp differences in task-level rollout variance. We ask how to minimize the variance of a fixed-benchmark mean under an exact rollout budge…

• Right Choice of Classification Algorithms Based on Reinforcement Learning for Prediction of Non-Alcoholic Fatty Liver

  Hasan Samadbin, Arman Daliri · 25. September 2026

  There are many complex issues in the world of artificial intelligence. Some of these problems are solved using other artificial intelligence methods, which are called artificial intelligence for artificial intelligence. Finding an appropriate classifier algorithm is a time-consuming task. For this r…

• NPBoost: Neural Processes with Gradient-Boosted Fixed Effects

  Andrea Nava, Ken R\"olli, Armin Begic, Fabio Sigrist · 24. September 2026

  Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most extensions of NPs focus on improving the neural network architecture. We instead develop an extension motivated by the shared hierarchical interpreta…

• LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels

  Zeming Liu, Hang Lyu, Jingtao Zhang, Yuan Xie · 24. September 2026

  Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment settings, however, labels come from weak annotators, historical decisions, heuristics, or distant supervision, so the same label noise that corrupts …

• CORE-STACK+: Meta-Learning for Deep Stacked Generalization

  Noor Islam S. Mohammad · 24. September 2026

  Stacking heterogeneous vision backbones (CNNs, ViTs, and hybrids) is the de facto recipe for accuracy, calibration, and robustness, yet two coupled pathologies limit its returns. Prediction-space multicollinearity ill-conditions the meta-learner's Gram matrix, inflating weight variance and producing…

• Modular Norm RandOpt: Population-Efficient Ensembling through Architecture-Aware Perturbations

  Kirato Yoshihara, Hiroaki Hamade · 23. September 2026

  RandOpt samples weight-perturbed language models and ensembles top-ranked candidates through plurality voting, but its global perturbation scale ignores heterogeneous module geometry. We propose \mbox{\textbf{\emph{Modular Norm RandOpt}}}, an architecture-aware sampling method using module-wise natu…

• Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models

  Alex Kipnis, Marcel Binz, Eric Schulz · 22. September 2026

  Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty but requires MCMC; the No-U-Turn Sampler (NUTS) is the gold standard but is slow and must restart f…

• A Shared Learning Rate Is Not a Neutral Control in Selective On-Policy Distillation

  Chencheng Zhu · 22. September 2026

  Selective on-policy distillation trains a student only at the token positions a selector scores highest, and the literature compares selectors under a single shared learning rate--a control chosen to be neutral. We show it is not. Under LoRA on GSM8K (Qwen2.5-1.5B student, 7B teacher), across an 8x …

• Are Coreset Selection Methods Worth Their Cost?

  Yangze Liu, Zhongyi Han · 22. September 2026

  Coreset selection picks a representative subset of the labeled training set to make training cheaper. However, it is usually evaluated by downstream accuracy at a fixed subset size, ignoring both the time spent selecting the subset and the training recipe behind each reported number. We introduce an…

• 1% of Tokens Can Be Enough: On Gradient Estimation in On-Policy Distillation

  Huanxin Sheng, Zhiling Ye, Haonan Wang, Jian Wang, Jinjie Gu, Jian Kang · 22. September 2026

  Sparse on-policy distillation (OPD) allocates teacher supervision to a small subset of tokens in student-generated trajectories. However, useful teacher guidance can yield a noisy update when its gradient is estimated from a sampled next token. We study this estimation problem at a fixed prefix in i…

• Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds

  Vishnu Bindu Balachandran · 17. September 2026

  Every production model is updated, by retraining, fine-tuning, quantization, or a silent vendor swap, and each update risks being worse than what it replaced. We formalize update promotion as certified paired risk-difference auditing. Our starting point is a support identity: the risk difference bet…

• TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models

  Zeyi Shao, Haowen Hua, Jiaxin Zhang, John See, Zeyd Boukhers, Cong Yang · 16. September 2026

  Prompt learning adapts vision-language models, such as CLIP, by adjusting a small set of context tokens. However, under few-shot supervision, even moderate label noise can disrupt prompt optimization. To address this issue, we propose TecoPrompt, a closed-loop robust prompt-learning framework that r…