10 accepted papers
Modern foundation models are evaluated through broad capabilities such as arithmetic, reasoning, safety, and robustness, yet it remains unclear in a principled sense when *finite tests* can meaningfully certify such claims. We develop a rigorous theory of capability evaluation by formalizing evaluat…
We study agnostic online learning from continuous-time data streams, a setting that naturally arises in applications such as environmental monitoring, personalized recommendation, and high-frequency trading. Unlike classical discrete-time models, learners in this setting must interact with a continu…
Generative models have shown impressive capabilities in synthesizing high-quality outputs across various domains. However, a persistent challenge is the occurrence of "hallucinations," where the model produces outputs that are not grounded in the underlying facts. While empirical strategies have bee…
Developing machine learning models that account for potential faults encountered in real-world environments presents a fundamental challenge for mission-critical applications. In this paper, we introduce a novel theoretical framework grounded in learning theory for dealing with faults. In particular…
We study online classification with general hypothesis classes where the true labels are determined by some function within the class, but are corrupted by *unknown* stochastic noise, and the features are generated adversarially. Predictions are made using observed *noisy* labels and noiseless featu…
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy. The problem may be succinctly stated as follows. Let $\mathcal{F}$ be a distribution-valued function class with an unbounded label set. Our aim is to estimate an \emp…
We study the problem of learning functional distributions in the presence of noise. A functional is a map from the space of features to *distributions* over a set of labels, and is often assumed to belong to a known class of hypotheses $\mathcal{F}$. Features are generated by a general random proces…
We study sequential general online regression, known also as sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We obtain tight, often matching, lower and upper bounds for sequential minimax regret, which is defined as the excess loss incurred…
Using only samples from a probabilistic model, we predict properties of the model and of future observations. The prediction game continues in an online fashion as the sample size grows with new observations. After each prediction, the predictor incurs a binary (0-1) loss. The probability model unde…
We study the optimal sampling set selection problem in sampling a noisy k -bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a k -bandlimited graph signal from m samples, the optimal sampling set selection problem has been shown to be equivalent to finding a m×k su…