Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test. We maximize a budget-free utility, the best observed score minus a cost per increment, with a one-step lookahead: advance the candidate with the largest expected improvement (EI) under...
Long Vu, Jongseong Brad Choi, Binoy Ravindran et al.· 0 citations
We study the general problem of estimating $D(\dde(S))$, the probability that an unknown distribution $D$ assigns to an event determined by a sample $S$ of $n$ i.i.d.\ draws from $D$ through a known mapping $\dde$. The estimation algorithm knows $\dde$ and $S$ but does not have access to any additional samples from $D$...
Vitaly Feldman, Satyen Kale, Vinod Raman et al.· 0 citations
Omniprediction concerns the task of making predictions that are simultaneously accurate with respect to different losses. Earlier results by [CV09] show that in an online learning setting, $O(\log(|\mathcal{F}||\mathcal{L}|))$ regret is achievable simultaneously against every predictor in a finite predictor class $\mat...
Junyan Liu, Haipeng Luo, Lillian J. Ratliff· 0 citations
This work introduces density-guided bootstrap boundaries, a trajectory-dependent mechanism that adapts the depth of imagined returns using a score-informed relative density proxy and clarifies the theoretical assumptions distinguishing the authors' expansion proxy from exact path likelihoods.
Shashank Gupta, Pilhwa Lee· 0 citations
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An algorithm for the special case of self-aware graphs, in which every vertex has a self-loop and a map whose fixed points yield sampling distributions that control the one-round regret remainder in terms of the learner's loss and the graph's independence number is constructed.
Meng-Xiao Zhang, Yu-Heng Zhang, Hai-Peng Luo et al.· 0 citations
Continual adaptation requires deciding not only how to update a model, but whether to commit an update without exceeding a declared budget for changes to protected behavior. We introduce Certified Anchor-Based Adaptation (CABA), a candidate-agnostic post-update gate that reserves historical observations from candidate...
Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn disentangled and interpretable representations is critical for uncovering latent genera...
Ioannis N. Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker et al.· 0 citations
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components. Very recently, Tsedenov \& Kovalev (2026) showed that the minimax optimal complexity under averaged smoothness and convexity is $\tilde{\Theta}(n + n^{6/7} \epsilon^{-2/7})$ by establishing nearl...
Xinliang Zhang, Lesi Chen, Zonghan Li et al.· 0 citations
Chronos-2 is introduced, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications and shows that a single pretrained model can capture structure shared across temporal systems.
Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken et al.· 0 citations
Self-distillation methods for large language models with an exponential moving average (EMA) teacher already retain a history of student updates. Muon nevertheless stores a separate momentum buffer, increasing persistent training memory. We propose Muon with Reused Teacher (Muon-RT), which reuses the teacher-student di...
This work theoretically study how stochastic rounding (SR) affects prediction risk as model size, training time, and precision change, and derives how the smallest prediction error decreases with compute, and identifies model sizes and training times that achieve optimal-compute scaling.
Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results. This paper proposes CausalGrade, a causal graph scoring method based on the probabilistic judgment model Jev. The method jointly provides Jev with evidence...
Jiangming Zhong, Jiawei Zhu, Guanting Wu et al.· 0 citations
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
