Cost-aware multi-objective Bayesian optimization for non-uniformly expensive functions.
problem Non-uniform cost of function evaluations in Bayesian optimization.
method Introduces cost-aware constraints and a new acquisition function to optimize multi-objective functions with varying costs.
result Demonstrates improved optimization in hyperparameter tuning of neural networks and random forests.
Improved LMP algorithm for better sensor network estimation in non-uniform noise.
problem Improving distributed estimation in sensor networks with non-uniform noise.
method Weighted sum of mean square error cost function and steepest-descent recursion for weight updates.
result Advantages over diffusion LMP in non-uniform noise conditions.
DA-LSTM adapts LSTM depth to non-uniform data, improving efficiency.
problem Non-uniform information distribution in sequential data cannot be accurately modeled by traditional LSTM.
method Developed DA-LSTM architecture that dynamically adjusts LSTM depth based on information distribution.
result DA-LSTM reduces computation resource usage and convergence time by 41.78% and 46.01% respectively.
STR reparameterizes DNN weights with soft thresholds for better sparsity and accuracy.
problem Improving sparsity in DNNs for better accuracy and lower inference cost.
method Soft Threshold Reparameterization (STR) using the soft-threshold operator on DNN weights.
result STR achieves state-of-the-art accuracy and reduces FLOPs by up to 50%.
Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems. Up to a primal-dual transformation, it is also the same as accelerated stochastic gradient descent that is one of the central methods used in machine learning. In this paper, we imp…
UBL R package handles utility-based learning problems in various domains.
problem Handling utility-based learning problems with non-uniform costs and benefits.
method Provides a set of methods for classification and regression tasks in R, allowing domain preferences specification and automatic inference.
result Facilitates utility-based predictive analytics in R, enhancing model performance.
This work certifies non-uniform bounds against adversarial attacks for neural networks.
problem Certifying robust regions around data points against non-uniform adversarial attacks.
method Formulated as an optimization problem with nonlinear constraints, using the augmented Lagrangian method for general feedforward neural networks.
result Non-uniform bounds have larger volumes and better interpretability compared to uniform bounds.
Compact scheme solves fractional Black-Scholes on non-uniform grids.
problem Solving time-fractional Black-Scholes equation on non-uniform grids.
method Three-point compact finite difference scheme on graded meshes.
result Fourth-order accuracy in space for special meshes.
The study finds non-uniform lattices with thin Hitchin representations in specific Lie groups.
problem Finding thin Hitchin representations in non-uniform lattices of Lie groups.
method Arithmetic methods to construct thin Hitchin representations.
result Infinitely many orbits of thin Hitchin representations in non-uniform lattices.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
New material groupoid theory subdivides non-uniform bodies into smoothly uniform parts and isolated points.
problem Lack of differentiability in material bodies leads to non-uniformity.
method Introducing material groupoid and material distribution to study non-uniform bodies rigorously.
result Material bodies can be subdivided into smoothly uniform parts and isolated points.
New approach to adversarial robustness with non-uniform perturbations.
problem Real-world adversaries craft adversarial examples with non-uniform perturbations.
method Proposes non-uniform perturbations based on feature dependencies and data distribution.
result Shows improved robustness to real-world attacks compared to uniform perturbations.
New method upsamples sparse, non-uniform point clouds more accurately.
problem Suboptimal results from existing point cloud upsampling methods.
method Imposes manifold distribution constraints using Gaussian functions.
result Generates higher-quality, more uniformly distributed dense point clouds.
New sampling method reduces variance in stochastic optimization.
problem Large variance in gradient estimation slows down optimization algorithms.
method Proposes a novel non-uniform sampling approach using multi-armed bandit framework.
result Asymptotically reduces variance within a factor of 3, significantly improving convergence time.
New method speeds up optimization with non-uniform sampling.
problem Optimizing complex stochastic problems efficiently.
method Stochastic Primal Dual Coordinate Method with Optimality Violation-based Sampling.
result The proposed method and variants outperform other methods in speed.
New approach finds minima of geodesic lengths for non-uniform fillings.
problem Finding minima of geodesic length functions for non-uniform fillings.
method Elementary optimization for 4-regular topological fillings, analysis of fat graphs and optimization techniques.
result Minima of geodesic length functions are found to be at triangle surfaces in both analyzed classes of non-uniform fillings.
New method uses graphene transistors for efficient non-uniform random number generation.
problem Generating non-uniform random variates efficiently.
method GFET-based hardware non-uniform random number generator.
result Demonstrated speedup of Monte Carlo integration by up to 2x.
Study shows how non-uniform scaling affects persistence diagrams.
problem Stability of persistence diagrams under non-uniform scaling.
method Explicit bounds on bottleneck distance derived for Euclidean scaling.
result Explicit bounds on the stability of persistence diagrams under non-uniform scaling.
Unified framework for non-uniform materials evolving over time.
problem Dealing with non-uniform materials evolving over time.
method Constructing a material groupoid and material distribution.
result Unified framework for general non-uniform evolution materials.
UNIQ method quantizes neural networks adaptively, reducing BOPS.
problem Efficiently quantizing neural networks to reduce computational cost.
method Adapts to parameter distribution, using uniform noise injection.
result Shows advantages in low computational budget scenarios.
APoT quantization improves neural network efficiency and accuracy.
problem Efficiently quantizing weights and activations in neural networks.
method Constraining quantization levels as sums of Powers-of-Two terms, applying reparameterization, and weight normalization.
result 4-bit quantized ResNet-50 achieves 76.6% top-1 accuracy, 22% computational cost reduction.
New rigidity theorem for product of lattices.
problem Understanding quasi-isometry of product lattices.
method Demonstrated rigidity for product of non-uniform rank one lattice and nilpotent lattice.
result Any quasi-isometric group is an extension of a non-uniform rank one lattice by a nilpotent lattice.
Non-uniform lattices in PU(n,1) cannot geometrically act on CAT(0) cube complexes.
problem Non-uniform lattices in PU(n,1) cannot geometrically act on CAT(0) cube complexes.
method Proving non-uniform lattices in PU(n,1) cannot geometrically act on CAT(0) cube complexes.
result Non-uniform lattices in PU(n,1) cannot geometrically act on CAT(0) cube complexes.
New model learns SDEs without gradient matching for non-uniform time increments.
problem Learning non-parametric drift and diffusion functions for SDEs.
method Formulates sensitivity equations for learning and optimizes path distributions.
result Robust and efficient learning of SDE systems with non-uniform time increments.
We apply stochastic average gradient (SAG) algorithms for training conditional random fields (CRFs). We describe a practical implementation that uses structure in the CRF gradient to reduce the memory requirement of this linearly-convergent stochastic gradient method, propose a non-uniform sampling scheme that substant…
New method detects communities in complex hypergraphs, matching theoretical limits.
problem Detecting communities in non-uniform hypergraphs with varying hyperedge sizes.
method Developed a spectral theory for weighted non-backtracking operators on non-uniform hypergraphs.
result Achieved the Kesten-Stigum bound for weak recovery in a general class of non-uniform HSBMs.
Sharp threshold for exact recovery in non-uniform hypergraph stochastic block model.
problem Community detection in random hypergraphs with non-uniform hyperedge probabilities.
method Sharp threshold established; two efficient algorithms for exact recovery.
result Sharp threshold for exact recovery; information-theoretic lower bound on misclassification.
Improved sampling accuracy in SG-MCMC methods via non-uniform gradient subsampling.
problem Computational inefficiency and sampling error in stochastic gradient MCMC methods.
method Proposes a non-uniform subsampling scheme to reduce sampling error in EWSG, a variant of SG-MCMC.
result EWSG reduces sampling error compared to uniform subsampling, improving accuracy without sacrificing convergence speed.
Efficient non-uniform quantizer improves CNN performance on FPGA.
problem Improving CNN performance on low-power systems like mobile devices.
method Custom hardware-friendly non-uniform quantizer for parameters and activations using a single scale integer representation.
result Little degradation in accuracy on CIFAR-10 and CIFAR-100 datasets.
We prove that if G is a non-uniform lattice in a rank-one semi-simple Lie group $\ne Isom(\H^2_\R)$ then G is quasi-isometrically co-Hopf. This means that every quasi-isometric embedding G→G is coarsely onto and thus is a quasi-isometry.
In this note, we study deformations of a non-uniform real hyperbolic lattice in quaternionic hyperbolic spaces. Specially we show that the representations of the fundamental group of the figure eight knot complement into PU(2,1) cannot be deformed in PSp(2,1) out of PU(2,1) up to conjugacy.
We improve GANs by enforcing reproducibility and using non-uniform sampling.
problem Overrepresentation of certain samples in GANs' marginal log-likelihood.
method Enforce reproducibility through matching empirical distribution to prior, use non-uniform sampling for mini-batch selection.
result Improved quality and variety in generated samples, validated on CIFAR10, Fashion MNIST, and CelebA.
We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: (i) \emph{leverage scores-based} and (ii) \emph{norm-based} feature selection. Experimenta…
Improved matrix completion for non-uniformly sampled data.
problem Estimating unobserved entries in a matrix with varying sampling probabilities.
method Developed entry-specific bounds for low-rank matrix completion under structured non-uniform sampling.
result Error bounds for each entry match minimax lower bounds under certain conditions.
New loss function equivalence reveals PER's uniform sampling can be improved.
problem Improving Prioritized Experience Replay (PER) for better learning efficiency.
method Transforming non-uniformly sampled data loss functions into uniformly sampled ones.
result Some environments can replace PER with a new loss function without performance loss.
High-order compact schemes improve option pricing accuracy for stochastic volatility models.
problem Improving option pricing accuracy for stochastic volatility models with non-uniform grids.
method Fourth-order accurate compact schemes applied to option pricing PDEs for stochastic volatility models on non-uniform grids.
result Fourth-order accuracy achieved for non-zero correlation, outperforming standard schemes.
Two algorithms converge to dictionary learning with geometric rate for non-uniform data.
problem Dictionary learning for non-uniform data models.
method Derivation of convergence conditions for MOD and ODL.
result Both algorithms converge to the generating dictionary with geometric rate under certain conditions.
Proof shows volumes of certain geometric representations are always integers.
problem Integrality of volumes of specific geometric representations.
method Elementary, combinatorial-geometrical proof.
result Volumes of representations are integers when n≥2. Spectral algorithm recovers community structure in sparse hypergraphs.
problem Community detection in sparse random hypergraphs with community structure and higher-order interactions.
method Spectral algorithm with three steps: hyperedge selection, spectral partition, and correction/merging.
result Weak consistency achieved for weak signal-to-noise ratio.
The paper proves Zimmer's conjecture for non-uniform lattices by controlling mass escape and Lyapunov exponents.
problem Proving Zimmer's conjecture for non-uniform lattices in higher-rank semisimple Lie groups.
method Establishes finiteness of low-dimensional actions, introduces novel techniques to control mass escape and Lyapunov exponents.
result Proves Zimmer's conjecture for many non-uniform lattices, improving previous results.
The study finds that certain hyperbolic manifolds contain subgroups isomorphic to surface groups.
problem The existence of thin surface subgroups in non-uniform arithmetic lattices.
method Analyzes arithmetic hyperbolic manifolds and their fundamental groups.
result Fundamental groups of non-compact arithmetic hyperbolic manifolds contain thin surface subgroups.
New algorithm improves online clustering of bandits with minimal frequency constraints.
problem Online clustering of bandits with non-uniform user frequencies.
method Proposes an efficient algorithm with simple set structures to represent clusters, proving a regret bound free of minimal frequency constraints.
result The new algorithm consistently outperforms existing methods in experiments on synthetic and real datasets.
We investigate the rank gradient and growth of torsion in homology in residually finite groups. As a tool, we introduce a new complexity notion for generating sets, using measured groupoids and combinatorial cost. As an application we prove the vanishing of the above invariants for Farber sequences of subgroups of righ…
Paper uses IGA for efficient pricing of financial derivatives, comparing it to FDM and FEM.
problem Efficiently pricing complex financial derivatives with high accuracy.
method Isogeometric Analysis (IGA) for solving nonlinear Black-Scholes PDEs.
result IGA provides very accurate solutions with fewer knots, significantly reducing computational time.
Rough Transformers improve time series modeling with lower costs and better performance.
problem Inefficient modeling of irregularly sampled time series data.
method Signature patching for continuous-time representations, reducing computational costs.
result Rough Transformers outperform vanilla Transformers and Neural ODE models.
Validates conformal prediction for network data under non-uniform sampling.
problem Validity of conformal prediction for network data under non-representative sampling.
method Interprets sampling mechanisms as selection rules, studies validity conditional on selection events, uses permutation invariance and joint exchangeability.
result Finite-sample validity of conformal prediction for certain selection events and asymptotic validity for random walk sampling.
A new method for matrix completion with model-free weights.
problem Matrix completion under non-uniform missing structures.
method Constructs weights via convex optimization to adjust for non-uniformity without modeling observation probabilities.
result Recover matrix with stronger theoretical guarantees, especially in heterogeneous missing settings.
We derive high-order compact finite difference schemes for option pricing in stochastic volatility models on non-uniform grids. The schemes are fourth-order accurate in space and second-order accurate in time for vanishing correlation. In our numerical study we obtain high-order numerical convergence also for non-zero …