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Found 28 papers, 4 papers with code
No Dimensional Sampling Coresets for Classification
We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework.
On Mergable Coresets for Polytope Distance
We show that a constant-size constant-error coreset for polytope distance is simple to maintain under merges of coresets.
Sketching Multidimensional Time Series for Fast Discord Mining
In this work, we propose a sketch for discord mining among multi-dimensional time series.
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.
For Kernel Range Spaces a Constant Number of Queries Are Sufficient
For a point set $X$ of size $n$, a query returns a vector of values $R_p \in \mathbb{R}^n$, where the $i$th coordinate $(R_p)_i = K(p, x_i)$ for $x_i \in X$.
Linear Distance Metric Learning with Noisy Labels
In linear distance metric learning, we are given data in one Euclidean metric space and the goal is to find an appropriate linear map to another Euclidean metric space which respects certain distance conditions as much as possible.
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
While previous studies have demonstrated the effectiveness of using user behavior signals (e. g., clicks) as both features and labels of LTR algorithms, we argue that existing LTR algorithms that indiscriminately treat behavior and non-behavior signals in input features could lead to suboptimal performance in practice.
Batch Multi-Fidelity Active Learning with Budget Constraints
However, this method only queries at one pair of fidelity and input at a time, and hence has a risk to bring in strongly correlated examples to reduce the learning efficiency.
Classifying Spatial Trajectories
We provide the first comprehensive study on how to classify trajectories using only their spatial representations, measured on 5 real-world data sets.
Self-Adaptable Point Processes with Nonparametric Time Decays
Specifically, we use an embedding to represent each event type and model the event influence as an unknown function of the embeddings and time span.
Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning
Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks.
Approximate Maximum Halfspace Discrepancy
For different classes of $\Phi$ we can either provide a $\Omega(|X|^{3/2 - o(1)})$ time lower bound for the exact solution with a reduction to APSP, or an $\Omega(|X| + 1/\varepsilon^{2-o(1)})$ lower bound for the approximate solution with a reduction to 3SUM.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks.
A Deterministic Streaming Sketch for Ridge Regression
We provide a deterministic space-efficient algorithm for estimating ridge regression.
Constrained Non-Affine Alignment of Embeddings
Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.
The Kernel Spatial Scan Statistic
Kulldorff's (1997) seminal paper on spatial scan statistics (SSS) has led to many methods considering different regions of interest, different statistical models, and different approximations while also having numerous applications in epidemiology, environmental monitoring, and homeland security.
The GaussianSketch for Almost Relative Error Kernel Distance
We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space.
Closed Form Word Embedding Alignment
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e. g., GloVe or word2vec).
Simple Distances for Trajectories via Landmarks
We develop a new class of distances for objects including lines, hyperplanes, and trajectories, based on the distance to a set of landmarks.
Near-Optimal Coresets of Kernel Density Estimates
When $d\geq 1/\varepsilon^2$, it is known that the size of coreset can be $O(1/\varepsilon^2)$.
Improved Coresets for Kernel Density Estimates
When the dimension $d$ is constant, we demonstrate much tighter bounds on the size of the coreset specifically for Gaussian kernels, showing that it is bounded by the size of the coreset for axis-aligned rectangles.
Coresets for Kernel Regression
Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data.
The Robustness of Estimator Composition
And so on, if the composition is of more than two estimators.
Relative Error Embeddings for the Gaussian Kernel Distance
A reproducing kernel can define an embedding of a data point into an infinite dimensional reproducing kernel Hilbert space (RKHS).
Streaming Kernel Principal Component Analysis
Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning.
Subsampling in Smoothed Range Spaces
We consider smoothed versions of geometric range spaces, so an element of the ground set (e. g. a point) can be contained in a range with a non-binary value in $[0, 1]$.
Frequent Directions : Simple and Deterministic Matrix Sketching
It performed $O(d \times \ell)$ operations per row and maintains a sketch matrix $B \in R^{\ell \times d}$ such that for any $k < \ell$ $\|A^TA - B^TB \|_2 \leq \|A - A_k\|_F^2 / (\ell-k)$ and $\|A - \pi_{B_k}(A)\|_F^2 \leq \big(1 + \frac{k}{\ell-k}\big) \|A-A_k\|_F^2 $ .
Data Structures and Algorithms 68W40 (Primary)
