~ similar to 2605.30113· 17 results
The paper presents two new attacks on decisional $k$-sparse LWE and LPN problems for higher moduli $q$ by generalizing the Kikuchi method using graph theory.
This paper develops a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms.
This paper develops a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms.
The paper refutes Steurer's conjecture regarding the existence of large constant-separated sets within families of unit-norm vectors with low average correlation, using high-dimensional expanders to s…
The paper establishes new hardness amplification results for Learning Parity with Noise (LPN) and its sparse variants, showing that solving the problem on a small fraction of instances implies solving…
Divesh Aggarwal, Rishav Gupta, Hai Hoang Nguyen, Kel Zin Tan +1 more
The paper presents a new worst-case to average-case reduction for the Learning Parity with Noise (LPN) problem, achieving hardness for inverse-polynomial noise rates previously unattainable.
The paper uses majorization theory to analyze lattice reduction, showing that local swaps smooth the Gram-Schmidt profile and deriving variational and telescoping identities for the worst-case profile…
The paper proposes a semi-relaxed Gromov-Wasserstein objective to estimate the latent connectivity structure of large-scale networks, achieving statistically consistent and efficient recovery of the u…
This paper settles the complexity of three sketching problems in graphs and distributions.
The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…
The paper formalizes the problem of representation identifiability in supervised learning, showing that a representation property is identifiable if and only if it is constant across all possible fact…
The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…
The paper proposes UR-JEPA, a novel regularizer for Joint-Embedding Predictive Architectures (JEPAs) that enforces uniform rectifiability, achieving superior performance and more structured representa…
The paper introduces TN-SHAP-G, a novel framework that uses graph-structured tensor networks to efficiently approximate and compute Shapley values and interaction indices for black-box models, overcom…
The paper introduces novel, efficient differentially private algorithms for estimating monotone statistics, significantly improving sample complexity compared to existing methods.
The paper investigates applying Riemannian optimization techniques to low-rank matrix parameters for deep learning, but finds that the proposed methods do not conclusively outperform the AdamW baselin…
The paper introduces Sparse Backdoor, a novel supply-chain attack that embeds a provably undetectable backdoor into pre-trained image classifiers by injecting structured sparse perturbations.