AI-generated summaries

Today's ML research,
without the noise.

Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.

24 Papers today
8h Update frequency
7 Days of history
Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection
Pongpisit Thanasutives, Yoshinobu Kawahara
Theory
  • Weak-Pareto combines weak formulations with Pareto-based selection for robust FPDE discovery.
  • The framework effectively mitigates noise amplification issues associated with fractional differentiation.
  • Weak-Pareto outperforms traditional methods in recovering equations from noisy data.
  • The methodology allows for continuous-order optimization, avoiding discretization biases.
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Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli
Generative Models Graph Learning
  • Synthetic data generation can mitigate challenges in accessing high-quality transcriptomic data.
  • Integrating biological knowledge through gene graphs enhances the realism and utility of synthetic data.
  • MK-TGAN outperforms other generative models in terms of data realism and biological coherence.
  • The study highlights the importance of explicit knowledge integration mechanisms in generative models.
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A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings
Hei Ting (Una) Chan, Chenwei Wu, Xueshen Liu, Zesen Zhao, Boyuan Zheng, Luis Filipe Nakayama, Michael G. Morley, Liyue Shen, Jiasi Chen, Z. Morley Mao
Multimodal Efficient ML Interpretability
  • Introduces a cloud-edge collaborative architecture for multimodal clinical screening.
  • Utilizes lightweight edge models for data processing and a cloud LLM for reasoning.
  • Achieves high diagnostic accuracy and factual grounding while minimizing data transmission.
  • Demonstrates the system's effectiveness under various simulated bandwidth conditions.
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When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation
Shuhan Wang, Yilin Luo, Nan Xu, Chi Wang Cheung
NLP Large Language Models Optimization
  • Introduces a local benchmark for RoPE-commuting orthogonal transformations, focusing on independent pairwise rotations.
  • Demonstrates that the head-shared pairwise configuration yields higher perplexity than full-head mixing in dynamic quantization settings.
  • Establishes that the optimization of a surrogate does not guarantee improved quantization accuracy due to misalignment with quantizer statistics.
  • Highlights the relationship between mixing support size and quantization error, suggesting broader mixing leads to better performance.
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Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Larissa Xu, King Bi, William Chang
Reinforcement Learning Theory Robotics
  • Introduces decentralized multi-player Q-learning under information asymmetry.
  • Proposes algorithms mQ-learning and mQ-learning-intervals for common rewards and independent rewards scenarios.
  • Demonstrates that decentralized learning can achieve regret bounds similar to centralized benchmarks.
  • Highlights the challenges posed by exponential growth in joint action space with increasing players.
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A Contract-Grade Verifier for LLM-Generated GPU Kernels, and a Native Blackwell Backward for the Gated-Linear-Recurrence Family
Rishi Shah, Rishav Shrestha
Large Language Models Theory Optimization
  • Introduction of a twelve-gate contract-grade verifier for GPU kernels.
  • Audit of 2,638 machine-generated kernels revealing significant correctness issues.
  • Development of the first native Blackwell tcgen05 training backward for the GDN family.
  • Demonstration of the verifier's effectiveness through independent validation methods.
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Exploring Oversmoothing with Householder Matrices
Bhaskar Karol
Graph Learning
  • Introduces Householder Graph Neural Network (HouseGNN) to combat oversmoothing in GNNs.
  • Uses Householder matrices for node embedding updates, preserving Euclidean norms.
  • Proves properties of Householder reflectors that contribute to maintaining node representation diversity.
  • Offers a novel perspective on the oversmoothing issue by utilizing orthogonal transformations.
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A Compositional Theory of Curvature in Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay, Yasir Z, Sriraam Natarajan, Narayanan Chatapuram Krishnan
Generative Models Optimization Theory
  • Probabilistic Circuits (PCs) allow for exact inference and tractable curvature measures unlike deep neural networks.
  • Global sharpness regularization can lead to underfitting in PCs due to the compositional nature of their curvature.
  • The contribution of each sum node to the Hessian trace can be decomposed into contextual usage and local curvature.
  • An adaptive sharpness-aware regularizer improves generalization by focusing on nodes with high intrinsic local curvature.
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Latent On-Policy Self-Distillation
Guibin Zhang, Jiayang Lyu, Ran Sun, Xinlei Yu, Haoyu Zhao, Qibing Ren, Shuicheng Yan
Reinforcement Learning Large Language Models Robotics
  • Introduces a learnable privileged context for on-policy self-distillation.
  • Demonstrates superior performance over existing OPSD methods.
  • Achieves high learning efficiency with reduced rollout budget.
  • Employs a privileged-margin objective to enhance teacher-student dynamics.
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CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
Zihao Ye, Yingyi Huang, Hongyi Jin, Bohan Hou, Junru Shao, Zhongming Yu, Jinqi Chen, Meghan Cowan, Shiyi Cao, Shanli Xing, Hanfeng Chen, Vinod Grover, Tianqi Chen, Luis Ceze
Optimization Efficient ML Theory
  • CAKE integrates compiler and agent co-design to improve GPU kernel evolution.
  • The framework uses a typed intermediate representation (IR) for better hardware-specific control.
  • Localized correctness and performance diagnostics are provided to agents, enhancing decision-making.
  • Significant performance improvements were observed in benchmarks compared to traditional methods.
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Defensive Boosting for Online Probabilistic Forecasting
Georgy Noarov, Aaron Roth
Theory Efficient ML Optimization
  • The Defensive Booster algorithm achieves both competitive Brier scores and low classification error under varying conditions.
  • It operates efficiently with only one weak-class learner, unlike previous methods that require multiple learners.
  • The algorithm provides local hard-core certificates, enhancing its adaptability to different time intervals.
  • Experimental results show substantial improvements in predictive performance and runtime efficiency over existing methods.
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Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
Yuxiao Wen
Theory Efficient ML Optimization
  • Introduces Tree-Coupled A/B Testing (TCAB) for efficient multi-policy comparisons.
  • Reduces the number of reward-bearing interactions by sharing outcomes among policies.
  • Maintains each policy's standalone trajectory distribution despite shared feedback.
  • Achieves conditionally optimal query costs compared to traditional independent A/B/n testing.
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Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization
Fin Amin, Sounak Dutta, Paul D. Franzon
Optimization
  • TTARO introduces an online adaptation mechanism for circuit representations during Bayesian optimization.
  • The framework allows for continuous learning and adjustment of circuit embeddings based on newly observed performance metrics.
  • TTARO outperforms traditional fixed representation methods and DKL in terms of optimization efficiency.
  • The approach is compatible with various acquisition functions and Gaussian-process kernels.
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Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia
Efficient ML
  • UltraIR is a foundation model for IR spectroscopy with over 100 million parameters.
  • The model utilizes simulation-to-real transfer learning to improve chemical inference from IR spectra.
  • UltraIR outperforms traditional methods in various chemical sensing tasks and real-world applications.
  • The approach allows for effective adaptation using limited labeled experimental spectra.
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Structure-preserving uncertainty quantification for GENERIC dynamics
Zequn He, Celia Reina
Theory
  • Introduction of S-PENNs for uncertainty quantification in hard-constrained models.
  • Preservation of thermodynamic consistency in stochastic realizations.
  • Significant reduction in computational costs compared to deep ensembles.
  • Validation through multiple numerical examples demonstrating effectiveness.
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Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
Tadeusz Dziarmaga, Witold Sikora, Łukasz Struski, Jacek Tabor, Marcin Mazur
Efficient ML Optimization Theory
  • Prof-K provides a fast, scalable, and distribution-agnostic solution for top-k selection.
  • The algorithm guarantees high-probability correctness and buffer size through a probabilistic filtering approach.
  • Empirical results show 1.5×–10× speedups over existing top-k implementations, particularly beneficial for large datasets.
  • Prof-K allows for flexible accuracy-speed trade-offs, enabling users to tune performance based on their requirements.
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Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration
Sabin Roman, Ljupco Todorovski, Saso Dzeroski
Theory
  • SORT provides a framework for estimating sparse expansions in orthonormal bases from noisy data.
  • The technique is particularly effective for discovering ordinary differential equations and supports nonlinear approximation.
  • SORT demonstrates improved stability under sparse sampling and noisy conditions compared to traditional methods.
  • The method allows for order-consistent model growth, maintaining low-order coefficients as model complexity increases.
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H-VAEP and H-xT: Valuing Offensive On-the-Ball Actions in Handball by Estimating Probabilities
Julius Broermann, Oliver Müller, Michael Döring, Jochen Baumeister
Theory
  • Introduction of H-xT and H-VAEP frameworks tailored for handball analytics.
  • Development of a handball-native court zoning layout for improved action valuation.
  • Demonstration of the robustness of H-xT compared to traditional rectangular grids.
  • H-VAEP yields stable and intuitive player ratings that highlight contributions to build-up play.
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HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models
Fangzhou Chen, Shiji Zhao, Mengyang Wang, Qihui Zhu, Ranjie Duan, Maoxun Yuan, Xingxing Wei
NLP Large Language Models Efficient ML
  • HiRoute separates category-agnostic safety control from category-specific response guidance.
  • The framework uses a hierarchical router to dynamically compose fine-grained prompts based on input risk.
  • HiRoute maintains high safety rates while preserving the helpfulness of responses.
  • The approach reduces over-refusal of benign inputs compared to traditional methods.
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Scaling Automatic Research Agents via World Models
Xiyuan Yang, Sheikh Sarwar, Jingru Cheng, Zhan Shi, Duanshun Li, Huiyuan Chen, Haiyang Zhang, Chenlei Guo, Jingrui He, Zhenyu Liao
Reinforcement Learning Large Language Models Efficient ML
  • Introduction of World Model RL (WMRL) to replace expensive environment execution in AutoResearch agents.
  • Development of Online Debiasing and Inverse-Variance Denoising mechanisms to enhance the performance of the world model.
  • Theoretical grounding of the framework, proving improved convergence guarantees.
  • Empirical validation showing 3-4x acceleration in training and superior performance compared to larger models.
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Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion
Vincenzo Di Vito, Mehdi Taghizadeh, Deepjyoti Deka, Kaarthik Sundar, Ferdinando Fioretto
Optimization Generative Models Graph Learning
  • Introduction of Constrained Graph Diffusion (CGD) for MIPs, which enforces feasibility during the decision-making process.
  • Decomposition of mixed-integer nonlinear programming (MINLP) problems into discrete and continuous components to improve optimization efficiency.
  • Demonstrated effectiveness of CGD on diverse applications, achieving substantial improvements in solution quality and feasibility.
  • Achieved significant computational speedups compared to traditional numerical solvers.
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The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use
Joyjeet Singh
Reinforcement Learning Robotics Optimization
  • The predictor is not the bottleneck; it remains informative at long horizons.
  • The planning objective can saturate and invert, affecting planning success.
  • Long-horizon success is inversely related to one-step prediction accuracy.
  • Reachability is a more effective objective than proximity for planning.
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Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws
Liu Ziyin, Yizhou Xu, Tomaso Poggio, Isaac Chuang
Theory Optimization
  • Introduces Neural Quadratic Forms (NQF) as a minimal model for understanding sudden learning and scaling laws in neural networks.
  • Demonstrates that neural architectures exhibit a symmetry that allows for a unified representation of different models through a structure matrix A(x).
  • Establishes a connection between training dynamics and collective variables, leading to predictions about feature acquisition timing and power law behaviors.
  • Validates the theoretical predictions through numerical experiments across diverse neural network architectures and training methodologies.
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ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
Qianqian Wang, Yunshan Li, Dawei Huang, Wenwu Gong, Lili Yang
Theory Optimization
  • ProME aligns environment construction and model selection with the deployed predictor without requiring training-group labels.
  • The framework introduces a median split of prototype margins to create balanced environments for invariant learning.
  • Theoretical bounds on worst risks are established, showing transferability to oracle groups under alignment conditions.
  • ProME demonstrates superior performance in worst-group accuracy compared to existing methods across multiple benchmarks.
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