Generative models use kernel smoothing for conditioning on small example sets.
problem Improving generative models' performance with limited conditioning examples.
method Showed that cross-attention conditioning is equivalent to kernel smoothing, specifically a Nadaraya--Watson kernel smoother.
result The approach predicts and confirms three failure regimes for kernel-based conditioning.
Study on convex surfaces in Minkowski space, proving completeness and incompleteness conditions.
problem Characterizing isometric embeddings of hyperbolic plane in Minkowski 3-space.
method Analysis of null support function and curvature conditions.
result Conditions for completeness and incompleteness of convex surfaces.
We consider the homogeneous and the non-homogeneous convex relaxations for combinatorial penalty functions defined on support sets. Our study identifies key differences in the tightness of the resulting relaxations through the notion of the lower combinatorial envelope of a set-function along with new necessary conditi…
The study examines representations of compactly supported diffeomorphisms with a positive energy condition.
problem Analyzing projective unitary representations of compactly supported diffeomorphisms with a generalized positive energy condition.
method Investigates continuous second Lie algebra cohomology and uses it as an intermediate step to show that such representations are trivial on the identity component.
result Any such representation is trivial on the identity component of the group of compactly supported diffeomorphisms if the manifold is connected and has dimension greater than 1.
Neural interaction discoveries can be real or artifacts of model flexibility.
problem Identifying real neural interactions from data.
method Using a multiplicative-gating extension of neural additive vector autoregression.
result Effective rank of the joint lag-block covariance predicts interaction recoverability.
In this correspondence, we obtain exact recovery conditions for regularized modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions are weaker than those for modified-CS or for basis pursuit (BP). The discussion is also supported by simulation comparisons. Reg-mod-BP provides a solution to the spar…
We consider n-dimensional hypersurfaces flowing by mean curvature flow with Neumann free boundary conditions supported on a smooth support surface. We show that the Hausdorff n-measure of the singular set is zero. In fact, we consider two types of interaction between the support and flowing surfaces. In the case of…
IRKSN algorithm achieves sparse recovery with wider applicability conditions.
problem Sparse recovery challenges due to NP-hard nature and restrictive conditions.
method IRKSN algorithm based on k-support norm regularizer. result Achieves sparse recovery with explicit constants and standard linear rate.
SVM generalizes well even with many support vectors in high dimensions.
problem Generalization of SVM in high-dimensional spaces with many support vectors.
method Identified new deterministic equivalences and proved conditions for support vector proliferation.
result Broadened conditions for SVM generalization in high-dimensional settings and proved converse result.
We investigate the supports of extremal martingale measures with pre-specified marginals in a two-period setting. First, we establish in full generality the equivalence between the extremality of a given measure Q and the denseness in L1(Q) of a suitable linear subspace, which can be seen in a financial context as…
Study evaluates the impact of academic support center's face-to-face assistance on student performance.
problem Underestimation of Academic Support Center's true impact due to group bias.
method Applied causal inference theory and T-learner to evaluate conditional average treatment effect (CATE) of F2F personal assistance.
result Developed a new CATE function that depends on the number of F2F sessions, predicting improved CATE performance.
We identify a condition for regularity of optimal transport maps that requires only three derivatives of the cost function, for measures given by densities that are only bounded above and below. This new condition is equivalent to the weak Ma-Trudinger-Wang condition when the cost is C4. Moreover, we only require (n…
A main goal of regression is to derive statistical conclusions on the conditional distribution of the output variable Y given the input values x. Two of the most important characteristics of a single distribution are location and scale. Support vector machines (SVMs) are well established to estimate location functions …
The Lasso performs well in ultra-sparse linear models with finite support size.
problem Performance analysis of Lasso in ultra-sparse linear models.
method Novel application of replica method from statistical physics, rigorous analysis of average case performance.
result Average performance of Lasso assessed without scaling assumptions, offering sample complexity bounds.
We obtain sufficient conditions exlcuding the existence of non-trivial distribution sections of bundles over the boundary of symmetric spaces of negative curvature which are invariant with respect to a geometrically finite group of isometries and are supported on the limit set in a strong sense.
The paper analyzes sparse PCA for incomplete data and proves support recovery conditions.
problem Support recovery in sparse PCA with non-random missing data.
method Semidefinite relaxation of the ℓ1-regularized PCA problem. result Support of the sparse leading eigenvector can be recovered with high probability.
DAT-CGAN improves time series generation for better decision support.
problem Generating accurate time series data for decision support.
method DAT-CGAN uses multi-Wasserstein loss and overlapped block-sampling for improved sample efficiency.
result DAT-CGAN outperforms GAN-based baselines in generating data relevant to decision processes.
Paper characterizes umbilical hypersurfaces using a generalized overdetermined problem.
problem Characterizing umbilical hypersurfaces in space forms.
method Using a Serrin-type partially overdetermined problem with inhomogeneous Robin boundary condition.
result Any contact angle θ ∈ (0, π) can be achieved, generalizing previous results.
We construct the coarse index class with support condition (as an element of coarse K-homology) of an equivariant Dirac operator on a complete Riemannian manifold endowed with a proper, isometric action of a group. We further show a coarse relative index theorem and discuss the compatibility of the index with the sus…
Curvature conditions distinguish Euclidean space and disks in contractible manifolds.
problem Distinguish Euclidean space and disks among contractible manifolds.
method Investigate curvature conditions on open and compact contractible manifolds with boundary.
result Stronger curvature conditions can distinguish disks from Euclidean spaces.
In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient messa…
Study stability and bifurcation of liquid interfaces in cylindrical supports.
problem Stability and bifurcation of liquid interfaces in cylindrical support surfaces.
method Analysis of eigenvalues of the Jacobi operator, Plateau-Rayleigh instability, bifurcation theory.
result Conditions for the emergence of new morphologies and bifurcations from circular cylinders.
Study predicts academic achievement using students' support networks.
problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.
We provide necessary conditions for the Alexander polynomials of algebraically split component-preservingly amphicheiral links. We raise a conjecture that the Alexander polynomial of an algebraically split component-preservingly amphicheiral link with even components is zero. Our necessary conditions and some examples …
IVMs help identify dangerous traffic conditions in real-time.
problem Real-time crash risk analysis in urban traffic systems.
method Import Vector Machines (IVMs) applied to historical crash and traffic data.
result IVMs successfully identify dangerous traffic conditions with computational advantage.
We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature. We investigate the simplest type of LSVMs called Local Linear Support Vector Machines (LLSVMs). For…
In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using l1/l2-reg…
We systematically develop a transform of the Fourier-Mukai type for sheaves on symplectic manifolds X of any dimension fibred in Lagrangian tori. One obtains a bijective correspondence between unitary local systems supported on Lagrangian submanifolds of X and holomorphic vector bundles with compatible unitary conn…
Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.
problem Support recovery for high-dimensional diffusion processes under sparsity constraints.
method Adaptive Lasso estimator for d-dimensional ergodic diffusion process, focusing on linear models.
result Adaptive Lasso achieves support recovery and asymptotic normality for drift parameter under certain conditions.
Support selection and eventwise decoupling for simultaneous bets proven.
problem Optimizing expected utility for simultaneous independent events with multiple outcomes.
method Proved a support theorem for a broad class of strictly increasing strictly concave utilities, identifying the exact active support and proving independence from utility function.
result The exact active support is the eventwise union of single-event supports, independent of the utility function.
We formulate a sufficient condition for the existence of a consistent price system (CPS), which is weaker than the conditional full support condition (CFS) introduced by Guasoni, Rasonyi, and Schachermayer [Ann. Appl. Probab., 18(2008), pp. 491-520] . We use the new condition to show the existence of CPSs for certain p…
Bayesian model averaging under predictor redundancy
problem Reporting Bayesian model averaging posterior without changing the Bayesian target
method Using hard or soft regions of support space
result Region reports often give shorter and clearer summaries while preserving the main posterior information
The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …
A new meta-learning method using shared variational inference.
problem Meta-learning with uncertainty over model parameters.
method Shared amortized variational inference network for conditional prior and posterior.
result Prevents collapse of conditional prior to Dirac delta function.
SVR analyzed within RQ framework for risk management.
problem Risk management in stochastic optimization.
method Risk Quadrangle (RQ) theory applied to SVR.
result SVR formulations as minimization of Vapnik error and CVaR norm.
The paper analyzes kNN density estimation's convergence rates under different conditions.
problem Analyzing convergence rates of kNN density estimation under bounded and unbounded support conditions.
method Examined two cases: bounded support with known and unknown support sets, and unbounded support with smooth density function.
result kNN density estimation is minimax optimal under certain conditions and better than kernel density estimation in some cases.
On a compact manifold Mn (n≥3) with boundary, we study the asymptotic behavior as ε tends to zero of solutions uε:M→C to the equation Δuε+ε−2(1−∣uε∣2)uε=0 with the boundary condition ∂νuε=0 on ∂M. Assuming an energy upper bound on the solutions and a…
Suppose that Ω is the open region in Rn above a Lipschitz graph and let d denote the exterior derivative on Rn. We construct a convolution operator T which preserves support in $\bar{Ω$}, is smoothing of order 1 on the homogeneous function spaces, and is a potential map in the sense that …
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
Proposes a new method for selecting regularization parameters in sparse precision matrix estimation.
problem Selecting an appropriate regularization parameter for sparse precision matrix estimation.
method Developed a closed-form matrix-valued regularization parameter based on the sampling distribution of optimality conditions.
result The proposed method achieves comparable estimation accuracy and superior support recovery to cross-validation, with significant runtime improvements.
Paper tackles efficient risk estimation under dataset shift conditions.
problem Limited data from target population; auxiliary data available.
method Semiparametric efficiency theory; efficient and multiply robust estimators.
result Developed estimators for various dataset shift conditions.
A new method reformulates Optimal Transport Conditional Flow Matching using proximal operators.
problem Optimal Transport Conditional Flow Matching (OT-CFM) for generating models.
method Reformulate OT-CFM using proximal operators and extended Brenier potential.
result OT-CFM dynamics are terminally normally hyperbolic for manifold-supported targets.
Research shows guidance in diffusion models does not sample from intended distribution, affecting boundary sampling.
problem Clarifying the misconception that guidance modifies the data distribution in diffusion models.
method Rigorous proof and fine-grained analysis of guidance dynamics in two cases: mixtures of compactly supported distributions and mixtures of Gaussians.
result Guidance leads to sampling more heavily from the boundary of the support of the conditional distribution as the parameter increases.
A rational projective plane (QP2) is a simply connected, smooth, closed manifold M such that H∗(M;Q)≅Q[α]/⟨α3⟩. An open problem is to classify the dimensions at which such a manifold exists. The Barge-Sullivan rational surgery realization theorem provides necessar…
We extend nonparametric models to handle extrapolation, providing bounds for inference.
problem Challenges in nonparametric statistical inference when evaluating outside the conditioning variable's support.
method Introduced a class of extrapolation assumptions and a consistent estimation procedure to handle extrapolation.
result Validated extrapolation-aware conclusions through various applications and real-world data.
ContextFlow++ improves generative models by conditioning on mixed-variable contexts.
problem Lack of effective methods for context conditioning in flow-based generative models.
method Proposes ContextFlow++ with additive conditioning and mixed-variable architecture.
result ContextFlow++ achieves higher performance metrics and faster training.
Algorithm recovers sparse PCA support from incomplete data.
problem Sparse PCA with incomplete and noisy data.
method Semidefinite program (SDP) relaxation of non-convex l1-regularized PCA. result SDP enables exact recovery of true support of sparse leading eigenvector.
New design method improves Lasso performance in sparse regression.
problem Sparse linear regression with correlated design columns.
method Introduces partially-rotated designs to improve Lasso's RE constant.
result Lasso achieves better prediction error with high probability.