Adaptive algorithm generates unrestricted adversarial inputs, defeating robust classifiers.
problem Vulnerability of neural networks to unrestricted adversarial inputs.
method Adaptive algorithm for generating unrestricted adversarial inputs.
result Adversarial inputs defeat robust classifiers.
Study on groups of unrestricted virtual braids and their properties.
problem Understanding the structure and properties of virtual braids and links.
method Exploration of unrestricted virtual braids, fused links, flat virtual braids, and virtual Gauss braids.
result Definition and study of groups of flat virtual braids and virtual Gauss braids, including their linearity.
New method generates unrestricted adversarial examples from generative models.
problem Traditional defense methods are ineffective against unrestricted adversarial examples.
method Train an AC-GAN to model class-conditional distribution, then search latent space for misclassified images.
result Unrestricted adversarial examples can bypass strong defenses and fool classifiers.
Proposes a new method to generate unrestricted adversarial examples.
problem Generating unrestricted adversarial examples without norm constraints.
method Leveraging state-of-the-art generative models to manipulate image fine-grained aspects.
result Our adversarial images look indistinguishable from natural images and can bypass certified defenses.
Optimal learning procedure for arbitrary function classes.
problem Learning arbitrary function classes without structural properties.
method Unrestricted learning procedure that selects functions outside given class.
result Optimal sample complexity for arbitrary function classes.
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
problem Fair selection in stochastic combinatorial semi-bandit with delayed feedback.
method Introduced merit-based fairness constraints and new bandit algorithms for reward and fairness.
result Achieved sublinear expected reward and fairness regrets with dependence on delay distribution quantiles.
Study of unrestricted virtual braid groups and their properties.
problem Characterize and describe homomorphisms of unrestricted virtual braid groups.
method Analyzing homomorphisms to symmetric groups and finite groups, characterizing images, proving characteristic subgroups, determining automorphism groups, and studying residual properties.
result Complete description of homomorphisms from UVBn to Sn for n≥5. Generative models create indistinguishable adversarial objects for object detection.
problem Creating unrestricted adversarial examples for object detection.
method Search over latent space of GAN for adversarial objects.
result Generated adversarial objects are indistinguishable from non-adversarial objects and transferable.
This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study in this general framework of both restricted and unrestricted payment schemes, wh…
New method generates unrestricted adversarial face images to bypass robust face recognition systems.
problem Vulnerability of face recognition systems to unrestricted adversarial examples.
method Image translation techniques to generate large perturbations in face images.
result Achieved high attack success rates (90% and 80%) under white- and black-box settings. New methods reduce extrapolation errors in feature importance.
problem Flawed feature importance methods using unrestricted permutations lead to extrapolation errors.
method Three new approaches: conditional model reliance, Knockoffs with Gaussian transformation, and restricted ALE plot designs.
result Theoretical and numerical results show our strategies reduce/eliminate extrapolation.
REBMIX package generates, estimates, clusters and classifies multivariate normal mixtures.
problem Generating, estimating, clustering and classifying multivariate normal mixtures with unrestricted variance-covariance matrices.
method Random generation, estimation of components, weights, and parameters, prediction of cluster and class membership.
result Demonstrates the REBMIX package's capabilities for multivariate normal mixtures.
MoMA improves model-based RL by using unrestricted policy classes.
problem Limited sample efficiency and generalizability in model-based offline RL.
method Model-based mirror ascent algorithm with general function approximations.
result Theoretical guarantees and practical implementation of MoMA.
Study compares adaptive vs fixed query learning methods.
problem Comparing adaptive and fixed query learning methods for task approximation.
method Examined in-context and agentic learning in two settings: unrestricted and realizable.
result Adaptivity does not hinder performance in unrestricted setting but can in realizable setting.
New findings on GRW space-times with constant scalar curvature.
problem Understanding GRW space-times in different subspaces.
method Analyzing orthogonal subspaces of Gray's decomposition.
result Generalized quasi-Einstein GRW space-times reduce to known types of space-times.
New method optimizes policies without assuming known link functions between preferences and rewards.
problem Policy alignment with unknown and unrestricted link functions.
method Formulates an f-divergence-constrained reward maximization problem, learning policies directly. result Induces a semiparametric single-index binary choice model for policy alignment.
The paper develops a new theory for quantum link invariants using quandles and biquandles.
problem Quantum link invariants for links with SL2(C) flat connections. method Using quandles and biquandles, the paper extends Reshetikhin-Turaev functor to tangles.
result A new invariant of links with a gauge class of quandle representations.
New contest evaluates machine learning robustness against unrestricted adversarial examples.
problem Evaluating machine learning robustness against arbitrary adversarial inputs.
method Two-player contest with a prize pool, focusing on unconstrained adversarial examples.
result Demonstrates the need for comprehensive evaluation of machine learning models' worst-case adversarial risk.
A stochastic model helps maintain insufficiently funded pension funds.
problem Maintaining pension funds that are underfunded and require external financing.
method A time-homogeneous diffusion process with a barrier is used to model the unrestricted reserves value, and a renewal-reward process models the financing effort.
result Expected values and cost evaluations of maintenance are derived, and the approach is applied to a generalized Brownian motion process.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
problem Predicting price movements of cryptocurrencies using deep learning.
method Used deep learning algorithms (MLP, CNN, LSTM, ALSTM) on hourly and daily data of Bitcoin and Ethereum.
result Unrestricted model with trading and social media indicators outperforms restricted model.
ScoreAG generates unrestricted adversarial images maintaining semantic integrity.
problem Limited robustness evaluations due to ℓp-norm constraints. method Score-Based Adversarial Generation (ScoreAG) using score-based generative models.
result ScoreAG improves robustness assessments across multiple benchmarks.
The study extends classical results on harmonic functions to Riemannian manifolds with non-tangential boundary limits.
problem Extending classical results on harmonic functions to Riemannian manifolds with non-tangential boundary limits.
method Investigated the restricted mean-value property on Riemannian manifolds, focusing on non-tangential boundary behavior.
result Extended a classical result of Fenton to non-positively curved Harmonic manifolds of purely exponential volume growth.
The article has been withdrawn by the author. Wolfgang Lueck and Peter Linnell pointed out that the proof of Lemma 3.8 does not apply to the unrestricted case of wreath product. It is not clear at this stage how to complete the proof of Theorem 3.1 using the present version of Lemma 3.8. The valid results originating f…
New method estimates sparse covariance matrices in logit mixtures.
problem Estimating correlations among random coefficients in logit models.
method Mixed-integer optimization (MIO) with Markov Chain Monte Carlo (MCMC) for posterior draws.
result Correctly recovers true covariance structure from synthetic data.
Defines a new 2+1-G-HQFT using graded skein modules.
problem Developing a new quantum field theory for groups.
method G-graded chromatic maps and skein modules.
result Recover modified Turaev-Viro invariants.
Polynomial-time DP algorithm for learning Gaussians with matching sample complexity.
problem Learning Gaussian distributions while maintaining privacy.
method General framework for reducing DP estimation to non-private, polynomial-time algorithm for Gaussian learning.
result Matching sample complexity to information-theoretic upper bound for Gaussian learning.
This paper proposes BAT to balance accuracy and robustness in adversarial training.
problem Balancing accuracy and robustness in adversarial training models.
method Blind adversarial training (BAT) uses a cutoff-scale strategy to adaptively estimate a nonuniform budget for AEs.
result BAT improves the overall robustness of adversarial training models.
Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.
problem Optimizing portfolios with higher moments (variance, skewness, kurtosis) for large asset universes is computationally infeasible.
method Developed a structure-exploiting algorithm based on Yau's affine-normal descent, working directly with return matrix.
result Algorithm avoids explicit higher-order tensors and exploits quartic structure for efficient computation.
A new way to describe correlation matrices makes modeling easier.
problem Describing correlation matrices in a flexible and positive-definite way.
method Introduces a novel parametrization that allows unrestricted vectors for correlation matrices.
result The new parametrization ensures positive definiteness without additional constraints.
Optimizing dividend payouts in a foreign currency for insurance companies.
problem Maximizing expected discounted dividends in a foreign currency context.
method Modelled as a Brownian motion with drift and Lévy process for currency fluctuation, explicit calculations for value function and strategy.
result Explicit calculation of value function and optimal strategy for dividend payouts.
Study on framed surfaces with bounds on Morse index.
problem Bounding Morse index for framed surfaces.
method Proved a linear bound on Morse index for framed surfaces.
result Proved a linear bound on Morse index by a linear function of genus, number of ends, and branch points.
Study on lower bounds for deep neural networks using ReLU gates.
problem Understanding the role of depth in neural networks computing Boolean functions.
method Use of random restrictions and sign-rank methods to show lower bounds.
result Exponential lower bounds for ReLU circuits ending in a LTF gate.
This paper tackles the computational complexity of finding approximate stationary points in non-convex optimization.
problem Finding approximate stationary points in non-convex optimization problems.
method PLS-completeness, zero-order algorithms, and gradient queries.
result The query complexity of finding approximate stationary points is Θ(1/ε) for d=2.
Advances geometric structure flows, proving short-time existence and uniqueness for various flows.
problem Analyzing flows of geometric structures, focusing on non-isometric flows and specific subgroups.
method Developed algebra and compared two flows: negative gradient and Ricci-harmonic. Proved existence and uniqueness for Ricci-harmonic flow.
result Proved short-time existence and uniqueness for Ricci-harmonic flow for arbitrary lower-order torsion-quadratic terms.
New method learns latent structures for deep NLP models without tradeoffs.
problem Joint learning of latent structures and downstream predictors with end-to-end differentiability.
method SparseMAP inference for joint learning of latent structures and downstream predictors.
result First method to enable unrestricted dynamic computation graph construction from global latent structure while maintaining differentiability.
New method for private density estimation of high-dimensional Gaussian mixtures.
problem Private density estimation for mixtures of unrestricted high-dimensional Gaussians.
method Exploits list global stability to prove upper bound on sample complexity.
result First upper bound on sample complexity for agnostic private density estimation.
The paper studies properties of stated SL(n)-skein algebras and their centers.
problem Properties of stated SL(n)-skein algebras and their centers.
method Quantum trace maps and embeddings into quantum tori.
result Finitely generation and PI-degrees of centers of stated SL(n)-skein algebras.
Estimates growth loss in fund models and proposes a shrinkage method.
problem Estimating growth loss in fund models under frequentist and Bayesian estimation.
method Proposes a shrinkage method to target maximal growth with minimal deviation.
result Empirical evidence shows shrinkage gives a stable estimate closer to growth potential.
FFJORD models generate complex distributions efficiently with unbiased density estimation.
problem Efficiently generating complex distributions with unbiased density estimation.
method FFJORD uses continuous-time invertible neural networks with Hutchinson's trace estimator for unbiased log-density estimation.
result FFJORD achieves state-of-the-art performance in high-dimensional density estimation, image generation, and variational inference.
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style regularization where the regularization penalty articulates the way in which the marginal density should constrain otherwise unrestricted co…
Optimal trading strategy with unobservable pricing errors for co-integrated assets.
problem Dynamic portfolio optimization of convergence trading with unobservable pricing errors.
method Modeling of convergence trading strategy with unobservable Markov-modulated pricing errors, extending Liu and Timmermann (2013) model.
result Characterization of optimal portfolio strategies in full and partial information settings.
The paper characterizes crystallographic groups derived from virtual braid and twin groups.
problem Characterizing crystallographic groups from virtual braid and twin groups.
method Analyzing quotients of virtual braid and twin groups by their commutator subgroups.
result The quotients of virtual braid and twin groups by their commutator subgroups are crystallographic groups.
Tensor networks preserve data interpretability for supervised learning.
problem Efficiently classifying data using tensor networks.
method Number-state preserving tensor networks for supervised learning.
result Number-state preserving tensor networks can be trained to maximize their scalar product against data sets.
Study on virtual singular braid groups with algebraic properties and homomorphisms.
problem Algebraic properties and homomorphisms of virtual singular braid groups.
method Numerical invariants, homomorphisms, semi-direct product decompositions, presentations, and quotients.
result Determined all group homomorphisms from VSGn to Sn and obtained corresponding semi-direct product decompositions. Study on the Euler-Plateau energy with elastic modulus, focusing on minimizers and critical surfaces.
problem Minimizing the Euler-Plateau energy with elastic modulus.
method Analyzing the energy functional and its minimizers, considering different boundary conditions and topological constraints.
result Potential minimizers are highly dependent on physical rigidity parameters, and the area of critical surfaces can be computed from boundary data.
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables. LDAGs generalize earlier proposals for allowing local structures in the conditional probability distribution of a node, such that unrestricted label sets determine which edges can be deleted from the underl…
Paper introduces methods for more reliable probabilistic predictions with confidence intervals.
problem Inaccurate labeling of datasets due to unreliable probabilistic predictions from weak labeling functions.
method Proposes a methodology to provide confidence intervals for label probabilities using uncertainty sets of distributions.
result Improves reliability of probabilistic predictions and provides confidence intervals for label probabilities.
Paper presents a new framework for covariance matrix estimation with geometric insights.
problem Challenges in covariance matrix estimation, especially in finding suitable models and efficient estimation methods.
method General framework for linear restrictions on different transformations of the covariance matrix, including matrix logarithm and its inverse.
result Yields an M-estimator with M-estimation allowing for straightforward asymptotic and finite sample analysis.