A better method for planning complex visual tasks
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
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Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
arXiv:2506.20533v4 Announce Type: replace-cross Abstract: Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective approach to this problem, yet its theoretical properties remain poorly understood.…
DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking
arXiv:2603.01367v2 Announce Type: replace Abstract: Masked diffusion models (MDMs) generate text by iteratively selecting positions to unmask and then predicting tokens at those positions. Yet MDMs lack proper likelihood evaluation: the evidence lower bound (ELBO) is not only a loose…
Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
arXiv:2410.07409v2 Announce Type: replace-cross Abstract: From autonomous driving to package delivery, ensuring safe yet efficient multi-agent interaction is challenging as the interaction dynamics are influenced by hard-to-model factors such as social norms and contextual cues. Understanding these influences can aid…
Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
arXiv:2506.01290v2 Announce Type: replace Abstract: High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promising rating accuracy within individual domains, primarily by extending data quality…
Structured Matrix Scaling for Multi-Class Calibration
arXiv:2511.03685v2 Announce Type: replace Abstract: Post-hoc recalibration methods are widely used to ensure that classifiers provide faithful probability estimates. We argue that parametric recalibration functions based on logistic regression can be motivated from a simple theoretical setting for both binary…
SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation
arXiv:2603.09574v1 Announce Type: cross Abstract: Distilling humanoid locomotion control from offline datasets into deployable policies remains a challenge, as existing methods rely on privileged full-body states that require complex and often unreliable state estimation. We present Sensor-Conditioned Diffusion Policies (SCDP)…
Unsupervised Representation Learning from Sparse Transformation Analysis
arXiv:2410.05564v3 Announce Type: replace Abstract: There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality, controllability, or symmetry. In this paper we propose to learn representations from sequence data by factorizing the…
Are Expressive Encoders Necessary for Discrete Graph Generation?
arXiv:2603.08825v1 Announce Type: new Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph data, often relying on highly expressive neural backbones such as transformers or higher-order architectures. We revisit this design choice by introducing GenGNN, a…
Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
arXiv:2603.08859v1 Announce Type: new Abstract: Hybrid sequence models–combining Transformer and state-space model layers–seek to gain the expressive versatility of attention as well as the computational efficiency of state-space model layers. Despite burgeoning interest in hybrid models, we lack a basic…