4474 papers
This paper introduces Astromer 2, an enhanced foundational model for light curve analysis that leverages self-supervised pre-training on 1.5 million MACHO survey observations and weighted per-sample embeddings to significantly outperform its predecessor and prior models in classification tasks, particularly when trained with limited labeled data.
This paper proposes a unified framework to evaluate how personalization affects both prediction accuracy and explanation clarity in high-stakes domains, deriving statistical bounds to determine when such effects are detectable given specific dataset characteristics.
This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.
This paper presents an end-to-end Real2Sim2Real framework for deformable linear object manipulation that employs likelihood-free inference to estimate physical parameter distributions for domain-randomized reinforcement learning, enabling zero-shot deployment of visuomotor policies from simulation to the real world.
This paper introduces Feature Importance Rescaling (FIR), a theoretically grounded method that improves the reliability of cluster validity indices in noisy, high-dimensional Gaussian mixtures by attenuating irrelevant features, thereby strengthening the correlation between unsupervised evaluation metrics and ground truth.
This perspective proposes the concept of "functional units" as a critical bridge to reconcile traditional structure-property correlations with emerging data-driven AI paradigms, thereby advancing the understanding of material design and knowledge inheritance across diverse systems.
The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.
This paper presents mixed-integer linear programming formulations to construct globally optimal model trees with linear support vector machines at the leaves, demonstrating through extensive experiments that they achieve competitive accuracy with significantly smaller, more interpretable structures compared to greedy model trees and other standard machine learning algorithms.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.
This paper introduces CDGLT, a training-efficient framework for multimodal metaphor identification that leverages Concept Drift via Spherical Linear Interpolation and adapted LayerNorm tuning to achieve state-of-the-art performance on the MET-Meme benchmark while significantly reducing computational costs compared to existing generative methods.
This paper introduces Stepwise Guided Policy Optimization (SGPO), a framework that enhances Group Relative Policy Optimization (GRPO) by utilizing a step-wise judge model to provide learning signals from all-negative sample groups, thereby enabling large language models to learn from incorrect reasoning and improving performance across various reasoning benchmarks.
This paper introduces the Gaussian-Multinoulli Restricted Boltzmann Machine (GM-RBM), a generative model that extends the standard GB-RBM by employing q-state Potts hidden units to better capture discrete, structured representations, demonstrating competitive performance on analogical recall and memory benchmarks while offering a scalable alternative to binary latent models.
The paper introduces JULI, a black-box jailbreaking technique that manipulates top-5 token log probabilities via a lightweight plug-in called BiasNet to effectively bypass safety alignment in API-accessible Large Language Models without requiring access to model weights or the generation process.
This paper introduces a novel framework for discovering symbolic differential equations from data by utilizing symmetry invariants as atomic building blocks, thereby ensuring that the resulting equations inherently respect physical laws while improving the accuracy and efficiency of existing discovery methods.
The paper introduces UltraEdit, a training-, subject-, and memory-free approach for lifelong language model editing that achieves unprecedented scalability and efficiency by computing parameter shifts in a single step, enabling 7B models to be edited on consumer GPUs with over 2 million updates while outperforming existing methods in speed, memory usage, and accuracy.
This paper presents a systematic evaluation of on-device Large Language Models across various sizes and quantization methods, revealing that heavily quantized larger models outperform smaller high-precision ones beyond a 3.5 bits-per-weight threshold while identifying a shift from communication to computational constraints as model size decreases.
The paper introduces Saturn, a reinforcement learning framework that leverages Boolean Satisfiability (SAT) problems to overcome scalability, verifiability, and difficulty control limitations in training large language models, resulting in significant reasoning improvements across SAT, math, and programming benchmarks.
The paper introduces FrontierCO, a large-scale benchmark utilizing real-world and competition-grade datasets across eight combinatorial optimization problems to rigorously evaluate ML solvers against classical methods, revealing a persistent performance gap on extreme-scale instances while identifying specific scenarios where ML approaches excel.
This paper presents the first systematic review of integrating foundation models into mobile service robotics, analyzing how these technologies address core challenges in perception and control, enabling applications in domestic and healthcare settings while discussing ethical implications and outlining future directions for safe, scalable, and trustworthy deployment.
This paper proposes SemiCP, a semi-supervised conformal prediction framework that utilizes an unlabeled nonconformity score based on Nearest Neighbor Matching to leverage unlabeled data for calibration, thereby significantly reducing coverage gaps and improving stability in scenarios with limited labeled data.