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3,686 papers
This work derives explicit upper bounds on the approximate max-information of any algorithm that fulfills $(\epsilon, \delta)$-differential privacy or R\'enyi differential privacy, thereby going beyond the classical results for pure $\epsilon$-differential privacy.
This representation can be an efficient alternative to the original influence functions for estimating changes in parameters, model outputs and loss due to data point removal, when model size is large relative to dataset size, or when evaluating the original influence functions in parameter space is infeasible.
A convergence analysis is developed for the regularized Newton method for training neural networks (NNs) in the overparameterized limit and it is proved that the NN converges exponentially fast to the target data (i.e., a global minimizer with zero loss).
AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen tasks. A major concern in such settings is safety: often, an agent must not only execute unseen tasks, but do so while avoiding risks and handling ones that materializ...
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In deep networks with small initialization, training can exhibit long plateaus separated by sharp feature-acquisition transitions. Whereas shallow nonlinear networks and deep linear networks are well studied, extending these analyses to deep nonlinear networks remains challenging. We derive exact scalar and matrix iden...
A framework for inverse-designing such biases by constructing novel parametrizations and their associated symmetries is developed and it is shown how holomorphic functions make this construction and calculation simple and explicit.
It is shown that, when a model is aligned sequentially source by source, exact optimization makes each stage contribute additively to the log-probability of a full response, up to a prompt-dependent normalization constant, reducing the alignment cost of Shapley-based valuation from exponential to linear.
The proposed Neural Fixed-Parameter Tractable (N-FPT), a neural-model-agnostic framework that uses parameterized algorithms to improve neural solutions without retraining and guide learning, proves this guarantee and global optimality under perfect advice.
KME-CLIP is proposed, which leverages this structure through the inner product in a reproducing kernel Hilbert space (RKHS) and theoretically proves that the KME-CLIP similarity can bring the contrastive loss arbitrarily close to its optimal value, which is attained by PMI, as the size of the point set grows.
This work proposes a principled, attack-agnostic approach that uses mutual information to measure what the final model reveals about a datapoint's training membership and shows that this final-model disclosure is upper bounded by the sum of per-iteration gradient disclosures.
This paper theoretically analyzes the MaskGIT sampler for image modeling, revealing its implicit temperature sampling mechanism, and shows that MaskGIT is asymptotically equivalent to a choose-then-sample (CTS) formulation, instantiated as the "moment sampler," which explicitly separates index selection from token samp...
This paper proposes a novel three-step aggregation method that first aligns the permutations across all local estimators via a particular spectral clustering approach, then aligns the signs within each estimated cluster via another tailored spectral approach, and finally applies the geometric median for robust aggregat...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
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.
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