paper-conference

We present GL-LowPopArt, a novel Catoni-style estimator for generalized low-rank trace regression. Building on LowPopArt (Jang et al., …

We study the problem of clustering T trajectories of length H, each generated by one of K unknown ergodic Markov chains over a finite …

Active learning improves training efficiency by selectively querying the most informative samples for labeling. While it naturally fits …

Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline …

Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation …

Multi-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various …

We present a unified likelihood ratio-based confidence sequence (CS) for any (self-concordant) generalized linear model (GLM) that is …

Although gradient descent with Polyak’s momentum is widely used in modern machine and deep learning, a concrete understanding of …

Proposes a new active learning approach by proposing a new uncertainty measure called the least disagree metric, as well as its efficient estimator, which is proven to be asymptotically consistent. This is then combined with seeding to become a new active learning algorith, LDM-S, which is shown to outperform existing approaches across various architectures and datasets.

Logistic bandit is a ubiquitous framework of modeling users’ choices, e.g., click vs. no click for advertisement recommender …