arXiv Machine Learning
Implementing a White-Box Undetectable Backdoor for Random Fourier Features
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arXiv AI
Jul 7
Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions
arXiv:2605. 04209v2 Announce Type: replace-cross Abstract: We present Sparse Backdoor, a supply-chain attack that plants a provably undetectable backdoor in pre-trained image classifiers, including convolutional networks and Vision Transformers.
arXiv Machine Learning
Jul 20
Testing Distributions Against Bounded Distinguishers
arXiv:2607. 15645v1 Announce Type: cross Abstract: Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers.
arXiv AI
Jul 21
NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs
arXiv:2603. 18046v2 Announce Type: replace-cross Abstract: We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations.
arXiv Machine Learning
Jun 17
Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials
arXiv:2606. 17319v1 Announce Type: cross Abstract: Motivated by the optimization of bounded binary black-box functions, we study the problem of learning polynomial surrogates over the Boolean hypercube.
arXiv Machine Learning
Jun 15
Let's Ask Gauss: Improved One-Run Privacy Auditing
arXiv:2606. 12733v2 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice.
arXiv Machine Learning
Sep 10
High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
arXiv:2609.08873v1 Announce Type: cross Abstract: Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over th...