R-GPM enables efficient graph pattern mining through user-defined relations.
problem Efficient graph pattern mining through user-defined relations.
method Parallel computing framework with MCMC sampling algorithm and optimizations.
result Efficient estimators for graph pattern statistics with up to 3-orders-of-magnitude computational cost reduction.
Paper trains models to resist string transformations.
problem Vulnerability of NLP models to adversarial string transformations.
method Combines search and abstraction techniques for robust training.
result Trained models resist combinations of user-defined transformations.
New method steers protein design towards desired properties.
problem Challenges in designing proteins with specific structures and properties.
method Feynman-Kac framework applied to RFdiffusion models with guiding potentials.
result Significant improvement in predicted interface energetics and binder designability.
IKA approximates kernels with linear combinations of chosen functions, outperforming Nyström method.
problem Efficient kernel approximation for large datasets.
method IKA method approximates kernels as a linear combination of user-defined functions.
result IKA consistently outperformed Nyström method on the STL-10 dataset.
This paper optimizes clustering interpretability by balancing value and user-defined features.
problem Generating interpretable clusters in graph data.
method Proposes a β-interpretable clustering algorithm that ensures at least β fraction of nodes share the same feature value.
result Empirical demonstration of the benefits of the proposed approaches in generating interpretable clusters.
ARMS automates fraud detection rules to improve efficiency and accuracy.
problem Maintaining effective fraud detection rules over time and reducing manual review.
method ARMS uses heuristic search and a user-defined loss-function to optimize and manage rules.
result ARMS can maintain original systems' performance with only a fraction of the rules.
Framework for controlling multiple risks in AI models.
problem Enforcing multiple risk constraints in generative AI models.
method Formalizes problem, introduces two dynamic programming algorithms.
result Achieves nearly tight control of all constraint risks under mild assumptions.
Improves music composition with user-defined constraints using continuous time models.
problem Combining sequence models with user-defined constraints in continuous time.
method Introduces a novel particle filter scheme for continuous time point processes.
result The particle filter scheme yields superior results in a human listening test.
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization practice requires a user-defined bounding box which is assumed to contain the optimizer. However, when little is known about the probed objective…
A new probabilistic polygonal curve representation using Gaussian Mixture Models.
problem Capturing curves with uncertainty in both tangent and normal directions.
method Probabilistic polygonal approximation with Gaussian Mixture Model (GMM).
result The GMM accurately captures the local geometry and uncertainty of curves.
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which app…
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense. Constrained clustering, as a subclass of SSL, makes use of user input in the form of relationships between data …
Interactive topic models with user-defined anchor words.
problem Building interpretable topic models.
method Protocol allowing users to interactively define anchor words for topic modeling.
result Validation of the usefulness of interactive topic models.
Improves interpretability of neural network intermediate layers by making concept activations more robust and effective.
problem Challenges in interpreting neural network decisions and learning in intermediate layers due to opacity and shared interactions.
method Proposes A-CAV to increase effectiveness and employs Gram-Schmidt process to improve robustness.
result Significant improvement in recall rate of concept images from 18.35% to 76.83% for VGG16, with reduced variance in recall across different random seeds.
FISHDBC clusters arbitrary data with flexible, scalable, and hierarchical features.
problem Clustering arbitrary data with arbitrary distance functions efficiently.
method Flexible, incremental, scalable, hierarchical density-based clustering algorithm.
result Flexible clustering of arbitrary data without feature extraction.
Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most po…
SlicStan improves Stan's usability and efficiency.
problem Stan's block syntax sacrifices usability for scalability.
method Formalized Stan, introduced SlicStan with compositional syntax and flexible functions.
result SlicStan facilitates better code reuse and abstraction.
Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Factor Analysis (SPARFA) framework provides a new statistical model and algorithms for machine learning-based learning analy…
A new Branch-and-Bound solver tackles L0-penalized problems with flexible loss functions.
problem Solving L0-penalized optimization problems with a broader class of loss functions.
method Generic Branch-and-Bound procedure with closed-form expressions for key quantities.
result El0ps solver achieves state-of-the-art performance and extends computational feasibility.
In this work, we are concerned with the spherical quasiconformal parameterization of genus-0 closed surfaces. Given a genus-0 closed triangulated surface and an arbitrary user-defined quasiconformal distortion, we propose a fast algorithm for computing a spherical parameterization of the surface that satisfies the pres…
The paper tackles automatic interpretation of manifold coordinates.
problem Finding physical meaning of abstract manifold coordinates.
method Proposes a method to explain embedding coordinates as compositions of functions from a dictionary.
result Demonstrates the effectiveness of the method on data.
One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utili…
The paper identifies and analyzes subjective class issues in user-generated data.
problem Subjective labels in user-generated data can lead to biased and manipulated results.
method Defined subjective and objective classes, proposed a framework for detecting subjective labels.
result Data mining practitioners can detect and avoid subjective class issues early in their projects.
APINNs use neural networks to solve MCMC problems efficiently.
problem Accurate Bayesian parameter estimation for systems governed by PDEs.
method Construct an offline PINN-UQ model and refine it on the fly using MCMC samples.
result Guaranteed approximation error less than a residual error threshold.
UAPCA projects uncertain data to low dimensions using GMMs.
problem Uncertain multidimensional data not well described by normal distributions.
method Model data with Gaussian mixture models, derive UAPCA projection from general formulation.
result Low-dimensional projections better represent multidimensional distributions.
Framework estimates PDEs from noisy data using neural networks.
problem Estimating unknown PDEs from noisy data.
method Interpolates noisy samples using a neural network, extracts PDE by matching derivatives.
result Method outperforms other methods in low signal-to-noise regimes.
We propose some axioms for hierarchical clustering of probability measures and investigate their ramifications. The basic idea is to let the user stipulate the clusters for some elementary measures. This is done without the need of any notion of metric, similarity or dissimilarity. Our main results then show that for e…
In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framewo…
BOP-Elites uses Bayesian Optimisation for QD search, improving efficiency and insight.
problem Finding diverse high-performing points from an objective function.
method Bayesian Optimisation and Gaussian Processes to model quality and diversity.
result Significantly more sample efficient and better at identifying niche solutions.
Proposes a parametric t-SNE without perplexity tuning.
problem Non-parametric t-SNE's perplexity parameter limits DR quality.
method Multi-scale parametric t-SNE with deep neural network.
result Produces reliable embeddings with competitive neighborhood preservation.
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run,…
Stochastic kernel based dimensionality reduction approaches have become popular in the last decade. The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data. Typically, the Markov chain is fully specified…
ConvNets improve nonstationary covariance estimation for large-scale spatial data.
problem Estimating nonstationary spatial covariance functions on large scales.
method Convolutional Neural Networks (ConvNets) for subregion identification and selection.
result Enhanced accuracy in parameter estimation using ConvNet-based partitioning.
Proposes efficient, modular method for implicit differentiation.
problem Implicit differentiation of optimization problems.
method Automatic implicit differentiation using autodiff and implicit function theorem.
result Automatic differentiation of optimization problems is made easier and more modular.
Improved text generation with constraints using discrete auto-regressive biasing.
problem Balancing fluency and constraint satisfaction in LLM outputs.
method Discrete Auto-regressive Biasing, leveraging gradients in discrete text space.
result Significantly improved constraint satisfaction with comparable fluency.
Robust algorithm detects season length without parameters.
problem Automatic detection of season length in time series.
method Interpolating, filtering, detrending followed by autocorrelation analysis.
result Outperforms existing algorithms in practical applications.
Deep learning models detect nanopore translocation events with high accuracy.
problem Manual parameter selection for nanopore signal analysis is prone to error.
method Developed a synthetic signal generator for training ML models.
result Deep learning models achieve over 99% true event detection.
Enhanced kernel framework for advanced data forecasting.
problem Complex mechanical behaviors and timefrequency dynamics in aircraft systems.
method Frequency-aware surrogate modeling with SMT kernels, extending kernel types and incorporating derivatives.
result Improved accuracy in forecasting CO2 concentrations and airline passenger traffic.
Study explores efficient data division for ICPs.
problem Efficiently dividing limited development data for ICPs.
method Experiments with training, calibration, and test data divisions.
result Allows overlap between training and calibration sets improves efficiency.
S2cGAN uses fewer labels to train cGANs effectively.
problem Training conditional GANs requires expensive labelled data.
method Semi-supervised training with sparse labels and unsupervised data.
result S2cGAN learns conditional mapping with sparse labels and unconditional distribution with unsupervised data.
In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which control…
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression. PA algorithms are formulated in terms of deterministic point-estimation problems governed by a set of user-defined hyperparameters: the a…
Paper tackles goal-directed generation of discrete structures using conditional generative models.
problem Challenges in generating structured discrete data, especially for problems like program synthesis and materials design.
method Investigates conditional generative models to directly model the distribution of discrete structures given properties of interest. Introduces a novel approach to optimize a reinforcement learning objective.
result Improvements over maximum likelihood estimation and other baselines in generating molecules and identifying short python expressions.
A faster graph kernel using optical random features.
problem High computation cost of graphlet kernel due to isomorphism test.
method Kernel random features, optical random features, mean kernel metric.
result The proposed method is orders of magnitude faster with similar or better accuracy.
Extracts controllable models from videos of real-world activities.
problem Creating realistic and controllable character models from video data.
method Two networks: one for pose and control signal to next pose, and another for pose, new pose, and background to output frame.
result High-quality, controllable character models can be generated from arbitrary videos.
A new method for sparse regression models using graph structure.
problem Sparse regression models for high-dimensional data.
method Decomposes coefficient vector into latent variables, performs regularization on latent variables, uses proximal projection.
result Stable performance compared to other models, especially for high-dimensional data.
APES simplifies reinforcement learning environment design in Python.
problem Creating and simulating reinforcement learning environments.
method Introduces APES, a Python toolbox for 2D grid-world environments.
result Equips reinforcement learning agents with customizable field of vision and item/reward interactions.
Proposes a new MOBO framework for better estimation of uncertain parameters in expensive black-box functions.
problem Estimating the unknown utopia in multi-objective optimization problems with expensive black-box functions.
method Nested weighted Tchebycheff Multi-objective Bayesian Optimization (WTB-MOBO) with ensemble model selection.
result Improves MOBO performance by selecting the best model from an ensemble of predictive models.