Low-degree method fails to predict robust subspace recovery problem.
problem Predicting computational tractability of robust subspace recovery problem.
method Low-degree polynomial framework, anti-concentration properties.
result Low-degree method fails to predict computational tractability of robust subspace recovery problem even up to high degree.
Survey on using low-degree polynomials to assess statistical tasks complexity.
problem Understanding the complexity of statistical tasks using polynomial functions.
method Applying low-degree polynomials to measure the complexity of statistical tasks, including detection, recovery, and estimation.
result Low-degree polynomials provide a framework to predict and explain statistical-computational tradeoffs.
New findings on computational limits for estimating hidden structures.
problem Estimating hidden structures in noisy data.
method Use of low-degree polynomials as a restricted model of computation.
result Established low-degree hardness of recovery problems for easy detection problems.
Low-degree hyperbolic hypersurfaces in complex projective space constructed.
problem Constructing hyperbolic hypersurfaces of low degree in complex projective space.
method Families of hyperbolic hypersurfaces X d X_d X d constructed with degree d d d . result Families of hyperbolic hypersurfaces X d X_d X d of degree d ≥ ( n + 3 2 ) 2 d \geq (\frac{n+3}{2})^2 d ≥ ( 2 n + 3 ) 2 constructed in P n + 1 ( C ) \mathbb{P}^{n+1}(\mathbb{C}) P n + 1 ( C ) . Paper connects free-energy and low-degree hardness in high-dimensional statistics.
problem High-dimensional statistical inference problems are computationally hard.
method Defines a free-energy criterion and connects it to low-degree hardness.
result Establishes connection between free-energy and low-degree hardness for Gaussian models.
Study disproves conjecture about low-degree polynomials in hypothesis testing.
problem Conjecture about limitations of polynomial-time algorithms in hypothesis testing.
method Used counterexamples to refute the conjecture and modified the conjecture to rule out the counterexample.
result Disproved conjecture about limitations of low-degree polynomials in hypothesis testing.
Learn low-degree functions with few random queries.
problem Learning low-degree functions from limited random queries.
method Learn bounded functions f : { − 1 , 1 } n o [ − 1 , 1 ] f:\{-1,1\}^n o[-1,1] f : { − 1 , 1 } n o [ − 1 , 1 ] of degree at most d d d with L 2 L_2 L 2 -accuracy ε \varepsilon ε and confidence 1 − δ 1-δ 1 − δ from log ( f r a c n δ ) ε − d − 1 C d 3 / 2 log d \log( frac{n}δ)\,\varepsilon^{-d-1} C^{d^{3/2}\sqrt{\log d}} log ( f r a c n δ ) ε − d − 1 C d 3/2 l o g d random queries. result Learn low-degree functions efficiently with logarithmic number of random queries.
Sublinear algorithms detect cliques in graphs with high probability.
problem Detecting a planted clique in random graphs efficiently.
method Non-adaptive low-degree polynomial queries of adjacency matrix entries.
result Sublinear time detection is possible for a specific range of clique sizes.
Surveying a new method to predict computational hardness in hypothesis testing.
problem Understanding statistical-versus-computational tradeoffs in high-dimensional inference problems.
method The low-degree method, which predicts computational hardness using the second moment of the low-degree likelihood ratio.
result Sharp low-degree lower bounds against subexponential-time algorithms for tensor PCA.
New work shows FP potential monotonicity equals low-degree polynomial estimators limits.
problem Establishing a precise mathematical relationship between statistical physics and polynomial estimators limits.
method Analyzing Gaussian additive models (GAMs) to show FP potential monotonicity equals low-degree polynomial estimators limits.
result For a broad family of Gaussian additive models, the power of low-degree polynomials is equivalent to the monotonicity of the annealed FP potential.
New method explains computational barriers in high-dimensional statistical models.
problem Understanding detection-recovery gaps in high-dimensional inference.
method Combining algorithmic contiguity and cross-validation reduction to obtain conditional computational lower bounds.
result Mild control of low-degree advantage is sufficient to explain computational barriers for recovery.
Detects dense subhypergraphs in random hypergraphs using low-degree polynomials.
problem Detecting a planted dense subhypergraph in a random hypergraph model.
method Degree-n^o(1) polynomials of adjacency tensor entries.
result Thresholds for detection in different density regimes.
GCNs favor high-degree nodes, leading to biased performance; a new method mitigates this.
problem Degree-related biases in GCNs, especially for low-degree nodes.
method Developed a novel SL-DSGC that reduces model and data biases.
result SL-DSGC improves GCN accuracy significantly for low-degree nodes.
New evidence shows computational barriers in graphon estimation using low-degree polynomials.
problem Estimating graphons efficiently and accurately.
method Low-degree polynomials to analyze computational limits.
result Low-degree polynomial estimators cannot significantly outperform USVT in graphon estimation.
New method uses almost orthonormal bases to prove low-degree lower bounds in complex statistical models.
problem Proving statistical-computational gaps in high-dimensional models with planted structures.
method Constructing an almost orthonormal polynomial basis under the planted distribution.
result Established new low-degree lower bounds for various complex models.
The paper explores how low-degree polynomials can detect shuffled linear regression models.
problem Detecting multivariate shuffled linear regression models from independent Gaussian random matrices.
method Investigates the effectiveness of low-degree polynomial algorithms for distinguishing the model from independent Gaussian random matrices.
result Establishes a phase transition phenomenon in the performance of low-degree polynomial algorithms for distinguishing the model.
Deep learning explained through spectral filtering of hierarchical features.
problem Understanding how deep neural networks learn useful representations from data.
method Neural Low-Degree Filtering (Neural LoFi) as a stylized limit of gradient-based training.
result Predicts how representations are selected layer by layer and explains emergence of concepts.
Statistical query algorithms and low-degree tests are nearly equivalent in high-dimensional hypothesis testing.
problem High-dimensional hypothesis testing and information-computation gaps.
method Analysis of statistical query framework and low-degree polynomials.
result Statistical query algorithms and low-degree polynomials are almost equivalent in power under mild conditions.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
Gradient Descent with Projection learns low-degree polynomials efficiently.
problem Learning low-degree spherical polynomials with neural networks.
method Over-parameterized two-layer neural network with Gradient Descent with Projection.
result Achieves nearly minimax optimal sample complexity and risk bound.
Paper proves computational hardness for graph matching and detection problems.
problem Computational hardness for graph matching and detection problems in correlated random graphs.
method Algorithmic contiguity and low-degree advantage bounds.
result No efficient algorithms exist for certain graph matching and detection problems.
New algorithms find half-optimal independent sets in sparse graphs.
problem Finding large independent sets in sparse random graphs.
method Low-degree polynomial algorithms.
result Low-degree polynomial algorithms can find independent sets of half-optimal size.
New ε \varepsilon ε -harmonic maps of low degree are rigid under certain energy bounds.
problem Understanding the rigidity of ε \varepsilon ε -harmonic maps of low degree. method Analysis of ε \varepsilon ε -harmonic maps and their critical points. result Non-trivial ε \varepsilon ε -harmonic maps of degree zero exist with energy above 8 π 8\pi 8 π . New study shows limits of low-degree algorithms in finding large independent sets in sparse hypergraphs.
problem Finding large independent sets in sparse random hypergraphs.
method Low-degree polynomial algorithms are analyzed to determine their limits.
result Low-degree algorithms can find independent sets of density up to \(\left(\frac{\log d}{(r-1)d}
ight)^{1/(r-1)}\), but no larger.
Two-layer NN with channel attention learns low-degree spherical polynomials efficiently.
problem Learning low-degree spherical polynomials with over-parameterized neural networks.
method Two-layer neural network with channel attention, vanilla gradient descent, learnable channel selection.
result Minimally improved sample complexity of $n \asymp Θ(d^{\ell_0}/\eps)$ for learning low-degree polynomials.
Classifies real rational knots and curves in a specific quadric space.
problem Classifying real rational knots and curves in a quadric space of signature ( 3 , 2 ) (3,2) ( 3 , 2 ) . method Classification through a study of real rational curves of low degree in the quadric.
result Provides representatives of all real rational knots of degree ≤ 5 \leq 5 ≤ 5 in the quadric. Constructs simplified or complexified simplicial complexes.
problem Efficiently simplifying or complexifying complex spaces.
method Embeddings of simplicial complexes into a simplicial ball with bounded degrees and low volume.
result Realizes complicated spaces as parts of a ball/sphere or gives spheres specific metrics.
In this paper we study rational real algebraic knots in R P 3 \R P^3 R P 3 . We show that two real algebraic knots of degree ≤ 5 \leq5 ≤ 5 are rigidly isotopic if and only if their degrees and encomplexed writhes are equal. We also show that any irreducible smooth knot which admits a plane projection with less than or equal to four cro…
The study sets new bounds on positive scalar curvature using group homology.
problem Finding lower bounds on positive scalar curvature using group homology.
method Baum-Connes assembly map, rational injectivity, and homological degrees.
result Lower bounds on positive scalar curvature groups are related to homology of Γ.
Detection of dense cycles in graphs reveals a gap between easy detection and hard recovery.
problem Detecting and recovering dense cycles in Erdős-Rényi graphs.
method Characterization of computational thresholds for detection and recovery using low-degree polynomial algorithms.
result A gap exists between the detection and recovery thresholds for certain parameter regimes.
In this paper, using exclusively homotopy theoretical methods, we study degrees of maps between ( n − 2 ) (n-2) ( n − 2 ) -connected ( 2 n − 1 ) (2n-1) ( 2 n − 1 ) -dimensional Poincar\' e complexes which have torsion free integral homology. Necessary and sufficient algebraic conditions for the existence of map degrees between such Poincar\' e complexes are es…
In this paper, we give a complete set of finite type string link invariants of degree <5. In addition to Milnor invariants, these include several string link invariants constructed by evaluating knot invariants on certain closure of (cabled) string links. We show that finite type invariants classify string links up to …
Homotopy classes of nanowords and nanophrases are combinatorial generalizations of virtual knots and links. Goussarov, Polyak and Viro defined finite type invariants for virtual knots and links via semi-virtual crossings. We extend their definition to nanowords and nanophrases. We study finite type invariants of low de…
The study characterizes and constructs polynomial harmonic morphisms on spheres.
problem Characterizing and constructing polynomial harmonic morphisms on spheres.
method Characterization and construction of polynomial harmonic morphisms using eigenfamilies.
result Strong restrictions and classification of polynomial harmonic morphisms in low dimensions.
New study shows low-degree polynomial algorithms struggle at clause densities close to Fix's.
problem Finding satisfying assignments in random k-SAT formulas at high clause densities.
method Analysis of low-degree polynomial algorithms and a new many-way overlap gap property.
result No efficient algorithms can find satisfying assignments at clause densities close to Fix's.
Study shows a tradeoff between sample complexity and computational efficiency for learning halfspaces with random noise.
problem PAC learning γ-margin halfspaces with Random Classification Noise.
method Established an information-computation tradeoff and provided a simple efficient algorithm with sample complexity O(1/(γ^2 ε^2)). Also, proved lower bounds for SQ algorithms and low-degree polynomial tests.
result Inherent gap between sample complexity and computational efficiency for learning halfspaces with random noise.
In this paper we classify, up to rigid isotopy, non-singular real rational curves of degrees less than or equal to 6 in a quadric homeomorphic to the 3-sphere. We also study their connections with rigid isotopy classes of real rational knots in R P 3 \mathbb{RP}^3 RP 3 .
GANs learn distributions by matching low-degree moments.
problem Understanding when GANs learn the target distribution efficiently.
method Theoretical analysis and empirical observation of GAN training process.
result GANs can learn notable distributions by matching polynomially many low-degree moments.
The study constructs balanced and rigid curves on specific types of hypersurfaces and complete intersections.
problem Constructing balanced and rigid curves on Calabi-Yau and general-type complete intersections.
method Balanced and rigid curves are constructed using specific hypersurfaces and complete intersections.
result Rigid curves of various genera and balanced rational curves of high degrees are constructed.
FairACE improves fairness in GNNs by balancing node performance across degree groups.
problem Degree biases in GNNs lead to unequal prediction performance among nodes with varying degrees.
method Integrates asymmetric contrastive learning with adversarial training to balance performance between high-degree and low-degree nodes.
result Significantly improves degree fairness metrics while maintaining competitive accuracy.
Paper proves first non-trivial PTF testing lower bounds for NGCA.
problem Proving lower bounds against PTF tests is challenging.
method Developed tools to prove PTF testing lower bounds for NGCA.
result First non-trivial PTF testing lower bounds for NGCA.
Paper proves non-vanishing of index map for low-degree cohomology classes.
problem Non-vanishing of index map for low-degree cohomology classes.
method Analysis of G G G -equivariant K K K -homology and C ∗ C^{*} C ∗ -algebra of group G G G . result Non-vanishing of the image of low-degree cohomology classes under the index map.
New lower bounds show challenges in clustering in moderate dimensions.
problem Clustering points from mixtures of isotropic Gaussians in moderate dimensions.
method Established low-degree polynomial lower bounds and developed a novel non-spectral algorithm.
result New lower bounds reveal a 'non-parametric rate' in moderate dimensions.
Deep neural networks reduce loan portfolio risk.
problem Minimizing risk in peer-to-peer lending portfolios.
method Proposed DeNN and DSNN models to predict default probability and time.
result DeNN model significantly reduces portfolio VaRs at various confidence levels.
Study shows cliff-learning in transfer learning from foundation models.
problem Data-scaling of transfer learning from foundation models in low data regimes.
method Investigation of cliff-learning phenomenon through foundation-model analysis and toy models.
result Cliff-learning reflects compatibility between priors and tasks.
New algorithms for hypothesis testing in high-dimensional data are shown to be effective under various noisy conditions.
problem Testing high-dimensional probability measures under noisy conditions.
method Low coordinate degree functions (LCDF) using Efron-Stein decomposition.
result LCDF can effectively test high-dimensional probability measures under noisy channels, with efficacy depending on scalar Fisher information.
Vanishing of cohomology for SL_2 groups over special fields.
problem Vanishing of cohomology for SL_2 groups over non-Archimedean local fields and S-integers.
method Analyzing continuous bounded cohomology of SL_2(k) and generalizing to tree automorphisms.
result Continuous bounded cohomology of SL_2(k) vanishes in all positive degrees for non-Archimedean local fields k.
Classifies low energy maps from curved surfaces into spheres.
problem Classifying maps from surfaces of constant curvature into spheres.
method Analyzes maps with low energy and degree ±1, focusing on bubble configurations.
result Maps are quantitively close to a bubble configuration with specific radii.