arXiv:2604.03478v1 Announce Type: new Abstract: In high-stakes settings where machine learning models are used to automate decision-making about individuals, the presence of algorithmic bias can exacerbate systemic harm to certain subgroups of people. These biases often stem from the underlying…
arXiv:2511.00064v5 Announce Type: replace Abstract: Clustering is a foundational task in data analysis, yet most algorithms impose rigid assumptions on cluster geometry: centroid-based methods favor convex structures, while density-based approaches break down under variable local density or moderate dimensionality. This…
arXiv:2604.03489v1 Announce Type: new Abstract: Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle to efficiently enforce general classes of constraints. To address this, we propose a novel data-driven…
arXiv:2601.06597v2 Announce Type: replace Abstract: A key challenge in machine learning is to explain how learning dynamics select among the many solutions that achieve identical loss values in overparameterized models – a phenomenon known as implicit bias. Controlling this bias…
arXiv:2604.03525v1 Announce Type: new Abstract: We study adversarial online learning of real-valued functions on $mathbb{R}$. In each round the learner is queried at $x_tinmathbb{R}$, predicts $hat y_t$, and then observes the true value $f(x_t)$; performance is measured by cumulative $p$-loss…
arXiv:2603.11321v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such as Group Relative Policy Optimization (GRPO) face a critical dilemma in sparse-reward settings: pure Reinforcement…
arXiv:2604.03541v1 Announce Type: new Abstract: This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among…
arXiv:2603.24324v3 Announce Type: replace Abstract: Designing effective auxiliary rewards for cooperative multi-agent systems remains challenging, as misaligned incentives can induce suboptimal coordination, particularly when sparse task rewards provide insufficient grounding for coordinated behavior. This study introduces an automated reward design…
arXiv:2604.03582v1 Announce Type: new Abstract: Neural operators have emerged as data-driven surrogates for solving partial differential equations (PDEs), and their success hinges on efficiently modeling the long-range, global coupling among spatial points induced by the underlying physics. In many PDE…
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
