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Machine Learning and Algorithms
426 papers indexed
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- The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Nets
Yujun Kim, Chaewon Moon, Chulhee Yun · 29 October 2025
We study the parameter complexity of robust memorization for $\mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $\epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $\mu$-ball around each traini…
- Revisiting Agnostic Boosting
Arthur da Cunha, Mikael M{\o}ller H{\o}gsgaard, Andrea Paudice, Yuxin Sun · 28 October 2025
Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are no assumptions on the distributio…
- Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization
Milad Sefidgaran, Kimia Nadjahi, Abdellatif Zaidi · 28 October 2025
In this paper, we leverage stochastic projection and lossy compression to establish new conditional mutual information (CMI) bounds on the generalization error of statistical learning algorithms. It is shown that these bounds are generally tighter than the existing ones. In particular, we prove that…
- On the Hardness of Approximating Distributions with Tractable Probabilistic Models
John Leland, YooJung Choi · 28 October 2025
A fundamental challenge in probabilistic modeling is to balance expressivity and inference efficiency. Tractable probabilistic models (TPMs) aim to directly address this tradeoff by imposing constraints that guarantee efficient inference of certain queries while maintaining expressivity. In particul…
- Learning to Better Search with Language Models via Guided Reinforced Self-Training
Seungyong Moon, Bumsoo Park, Hyun Oh Song · 28 October 2025
While language models have shown remarkable performance across diverse tasks, they still encounter challenges in complex reasoning scenarios. Recent research suggests that language models trained on linearized search traces toward solutions, rather than solely on the final solutions, exhibit improve…
- Online POMDP Planning with Anytime Deterministic Optimality Guarantees
Moran Barenboim, Vadim Indelman · 28 October 2025
Decision-making under uncertainty is a critical aspect of many practical autonomous systems due to incomplete information. Partially Observable Markov Decision Processes (POMDPs) offer a mathematically principled framework for formulating decision-making problems under such conditions. However, find…
- How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension
Cynthia Dwork, Lunjia Hu, Han Shao · 27 October 2025
- Uniform Convergence Beyond Glivenko-Cantelli
Tanmay Devale, Pramith Devulapalli, Steve Hanneke · 27 October 2025
- Learning from Interval Targets
Rattana Pukdee, Ziqi Ke, Chirag Gupta · 27 October 2025
- SAMOSA: Sharpness Aware Minimization for Open Set Active learning
Young In Kim, Andrea Agiollo, Rajiv Khanna · 27 October 2025