arXiv Statistics ML
Sep 25
Boolean threshold functions, neuron capacity, and memory retrieval
arXiv:2609. 29756v1 Announce Type: cross Abstract: How much information can a single neuron remember?
Bias-Induced Crossover in Absolute Capacity of Dense Associative Memory
arXiv:2609. 17477v1 Announce Type: cross Abstract: The absolute capacity of dense associative memory has mainly been analyzed for unbiased patterns.
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arXiv Machine Learning
Jul 10
A law of robustness for two-layer neural networks with arbitrary weights
arXiv:2607. 07778v1 Announce Type: new Abstract: Bubeck, Li and Nagaraj conjectured that, for generic data, any two-layer neural network with $m$ neurons that fits $n$ noisy labels must have Lipschitz constant at least of order $\sqrt{n/m}$, with no restriction on the size of the weights.
arXiv Machine Learning
Sep 25
Bandit Multiclass PAC Learning: Corrected Lower Bounds, Exact Families, and a Confidence Direct-Sum Phenomenon
The paper revisits realizable multiclass PAC learning with bandit feedback, correcting a previously claimed lower bound on sample complexity. It introduces a new anchored dimension, “aBDS,” and establishes a constant‑free three‑part lower bound, while also providing tighter upper bounds that eliminate dependence on the total label count. The authors demonstrate that the optimal sample complexity can vary dramatically even among classes with identical dimensional profiles, revealing a confidence direct‑sum phenomenon and a rank‑saturation phase transition.
arXiv Machine Learning
Sep 10
Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff
arXiv:2609. 08564v1 Announce Type: cross Abstract: We study distributed one-dimensional mean estimation under a 1-bit communication constraint.
arXiv Machine Learning
1d ago
Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift
The paper studies how an eavesdropper can adaptively attack quantum key distribution (QKD) systems when channel noise and device drift vary over time. By modeling the attack as a constrained Markov decision process and using reinforcement learning to jointly search gate structures and rotation angles, the authors construct compact attack circuits that perform near the theoretical upper bound for both device‑independent E91 and BB84 protocols under realistic noise models. The results show that adaptive attacks can significantly increase the eavesdropper’s information compared to fixed‑circuit strategies, and that the learned attacks recover known optimal cloners and key‑rate bounds.
arXiv Machine Learning
Sep 15
Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking
arXiv:2609.13197v1 Announce Type: new Abstract: Algorithmic Information Dynamics (AID) studies systems by perturbing them and measuring changes in algorithmic complexity, but its usual estimator, the...
arXiv Machine Learning
1d ago
Geometry-Dependent Bounds for Online Non-Monotone DR-Submodular Maximization
arXiv:2610. 00545v1 Announce Type: new Abstract: We study adversarial online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed sets.
arXiv Machine Learning
Aug 14
Gradient-Free Warm-Start Library Recovery: an Amortized-Regret Separation
arXiv:2606. 21253v2 Announce Type: replace Abstract: Continual learning that is gradient-free, local, online, and append-only is attractive for edge and streaming deployment, but its value is usually argued informally.
arXiv Machine Learning
Sep 22
Poisson Exchange Beyond Submodularity: Effective Approximation Algorithms for Offline and Online Subset Selection over Matroids
arXiv:2609.24569v1 Announce Type: cross Abstract: Over the past decade, a growing body of research has shown that $\gamma$-weak submodularity broadly arises in numerous subset selection tasks, includ...
arXiv Machine Learning
Sep 7
Optimal Rates for Agentic Networked Information Aggregation
arXiv:2609. 05318v1 Announce Type: new Abstract: Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a networked learning model.
arXiv Machine Learning
Sep 22
Optimal Sample Complexity of Stable Discounted Markov Decision Processes
arXiv:2302.07477v4 Announce Type: replace Abstract: We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricte...
arXiv AI
Jul 24
Improved lower bounds for the Shannon capacity of odd cycles
arXiv:2607. 21517v1 Announce Type: cross Abstract: The Shannon capacity $\Theta(G)$ of a graph $G$ quantifies the maximum rate at which information can be transmitted with zero error over a noisy channel.