arXiv Machine Learning
Jun 2
Towards Optimal Robustness in Learning-Augmented Paging
arXiv:2606. 01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years.
LARP: Learner-Agnostic Robust Data Prefiltering
arXiv:2506. 20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance.
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arXiv Machine Learning
Aug 4
T-TAMER: Provably Taming Trade-offs in ML Serving
arXiv:2509. 22992v2 Announce Type: replace Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives.
arXiv AI
Jun 29
Halt Fast! Early Stopping for Certified Robustness
arXiv:2606. 27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs.
arXiv AI
Aug 5
One Knob to Rule Them All: A Unified Optimal Transport View of Cold-Start Active Learning
arXiv:2608. 03249v1 Announce Type: new Abstract: Cold-Start Active Learning (CSAL) aims to select a valuable subset from an unlabeled pool without any prior knowledge or human assistance.
arXiv Machine Learning
Aug 11
The Cost of Adaptivity: Matching Lower Bounds Across Learning Problems
arXiv:2608. 08826v1 Announce Type: new Abstract: Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may have to answer queries whose coordinate and inspection time are chosen only after the data are seen.
arXiv Machine Learning
Jun 17
Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.
arXiv AI
Jun 4
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.
arXiv Machine Learning
Jun 2
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
arXiv:2605. 26919v2 Announce Type: replace Abstract: Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts.
arXiv Machine Learning
Jun 8
Certified Robustness to Data Poisoning in Gradient-Based Training
arXiv:2406. 05670v3 Announce Type: replace Abstract: Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks.
arXiv Machine Learning
Jun 10
Robust Regression of General ReLUs with Queries
arXiv:2606. 11130v1 Announce Type: new Abstract: We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss.
arXiv Machine Learning
Jun 26
Learning from Equivalence Queries, Revisited
arXiv:2604. 04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates.
arXiv Machine Learning
Jun 17
Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
arXiv:2602. 17894v2 Announce Type: replace-cross Abstract: Data collection is a critical component of modern statistical and machine learning pipelines, particularly when data must be gathered from multiple heterogeneous sources to study a target population of interest.