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We consider the Random-Cluster model on $(\mathbb{Z}/n\mathbb{Z})^d$ with parameters $p \in (0,1)$ and $q\ge 1$. This is a generalization of the standard bond percolation (with open probability $p$) which is biased by a factor $q$ raised to…

Probability · Mathematics 2020-08-20 Shirshendu Ganguly , Insuk Seo

We prove that the wired uniform spanning forest exhibits mean-field behaviour on a very large class of graphs, including every transitive graph of at least quintic volume growth and every bounded degree nonamenable graph. Several of our…

Probability · Mathematics 2019-05-31 Tom Hutchcroft

In this paper we consider the cluster estimation problem under the Stochastic Block Model. We show that the semidefinite programming (SDP) formulation for this problem achieves an error rate that decays exponentially in the signal-to-noise…

Machine Learning · Statistics 2017-05-24 Yingjie Fei , Yudong Chen

We prove that there exists a constant $C$ so that, for all $s,k \in \mathbb{N}$, if $G$ has average degree at least $k^{Cs^3}$ and does not contain $K_{s,s}$ as a subgraph then it contains an induced subgraph which is $C_4$-free and has…

Combinatorics · Mathematics 2023-11-02 Xiying Du , António Girão , Zach Hunter , Rose McCarty , Alex Scott

Modern machine learning focuses on highly expressive models that are able to fit or interpolate the data completely, resulting in zero training loss. For such models, we show that the stochastic gradients of common loss functions satisfy a…

Machine Learning · Computer Science 2019-04-09 Sharan Vaswani , Francis Bach , Mark Schmidt

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the…

Statistics Theory · Mathematics 2022-02-17 Yihong Wu , Jiaming Xu , Sophie H. Yu

Algorithmic stability is among the most potent techniques in generalization analysis. However, its derivation usually requires a stepsize $\eta_t = \mathcal{O}(1/t)$ under non-convex training regimes, where $t$ denotes iterations. This…

Machine Learning · Computer Science 2026-02-27 Wenquan Ma , Yang Sui , Jiaye Teng , Bohan Wang , Jing Xu , Jingqin Yang

Neutrino energy reconstruction on nuclear targets underlies oscillation measurements and precision tests of weak interactions. Inclusive charged--current data have long exhibited degeneracies commonly attributed to axial-mass tuning,…

Nuclear Theory · Physics 2025-12-29 Sanjeev Kumar Verma

In this article, we consider a Branching Random Walk on the real line. The genealogical structure is assumed to be given through a supercritical branching process in the i.i.d. environment and satisfies the Kesten-Stigum condition. The…

Probability · Mathematics 2023-02-02 Ayan Bhattacharya , Zbigniew Palmowski

In this paper we consider the reconstruction problem on the tree for the hardcore model. We determine new bounds for the non-reconstruction regime on the k-regular tree showing non-reconstruction when lambda < (ln 2-o(1))ln^2(k)/(2 lnln(k))…

Probability · Mathematics 2013-06-24 Nayantara Bhatnagar , Allan Sly , Prasad Tetali

We prove an inequality on decision trees on monotonic measures which generalizes the OSSS inequality on product spaces. As an application, we use this inequality to prove a number of new results on lattice spin models and their…

Probability · Mathematics 2018-12-27 Hugo Duminil-Copin , Aran Raoufi , Vincent Tassion

An independent transversal of a graph $G$ with a vertex partition $\mathcal P$ is an independent set of $G$ intersecting each block of $\mathcal P$ in a single vertex. Wanless and Wood proved that if each block of $\mathcal P$ has size at…

Combinatorics · Mathematics 2022-05-11 Carla Groenland , Tomáš Kaiser , Oscar Treffers , Matthew Wales

The main theorem in Judge and Mittelhammer [Judge, G. G., and Mittelhammer, R. (2004), A Semiparametric Basis for Combining Estimation Problems under Quadratic Loss; JASA, 99, 466, 479--487] stipulates that, in the context of nonzero…

Methodology · Statistics 2015-09-07 Severien Nkurunziza

In network analysis, developing a unified theoretical framework that can compare methods under different models is an interesting problem. This paper proposes a partial solution to this problem. We summarize the idea of using separation…

Machine Learning · Computer Science 2022-08-24 Huan Qing

Through an uncertainty quantification (UQ) perspective, we show that score-based generative models (SGMs) are provably robust to the multiple sources of error in practical implementation. Our primary tool is the Wasserstein uncertainty…

Machine Learning · Statistics 2024-05-27 Nikiforos Mimikos-Stamatopoulos , Benjamin J. Zhang , Markos A. Katsoulakis

Constant-stepsize stochastic approximation (SA) is widely used in learning for computational efficiency. For a fixed stepsize, the iterates typically admit a stationary distribution that is rarely tractable. Prior work shows that as the…

Machine Learning · Computer Science 2026-02-17 Zedong Wang , Yuyang Wang , Ijay Narang , Felix Wang , Yuzhou Wang , Siva Theja Maguluri

In this work, lossy distributed compression of pairs of correlated sources is considered. Conventionally, Shannon's random coding arguments -- using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically…

Information Theory · Computer Science 2020-10-21 Farhad Shirani , S. Sandeep Pradhan

Using an exactly solvable model of the Wigner-Weisskopf atom it is shown that an unstable quantum state cannot be recovered completely by the procedure involving detection of the decay products followed by creation of the time reversed…

Quantum Physics · Physics 2008-07-17 Robert Alicki

In this paper we consider coalescing random walks on a general connected graph $G=(V,E)$. We set up a unified framework to study the leading order of the decay rate of $P_t$, the expectation of the fraction of occupied sites at time $t$,…

Probability · Mathematics 2022-09-13 Jonathan Hermon , Shuangping Li , Dong Yao , Lingfu Zhang

Statistical mechanics is used to study unrealizable generalization in two large feed-forward neural networks with binary weights and output, a perceptron and a tree committee machine. The student is trained by a teacher being larger, i.e.…

Condensed Matter · Physics 2007-05-23 Matts Sporre
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