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08/14/2023
Locally Adaptive and Differentiable Regression
Over-parameterized models like deep nets and random forests have become ...
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06/28/2023
For Kernel Range Spaces a Constant Number of Queries Are Sufficient
We introduce the notion of an ε-cover for a kernel range space. A kernel...
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06/05/2023
Linear Distance Metric Learning
In linear distance metric learning, we are given data in one Euclidean m...
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05/26/2023
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
Ranking is at the core of many artificial intelligence (AI) applications...
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09/03/2022
Classifying Spatial Trajectories
We provide the first comprehensive study on how to classify trajectories...
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12/21/2021
Hiding Signal Strength Interference from Outside Adversaries
The presence of people can be detected by passively observing the signal...
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08/27/2021
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Gender is widely discussed in the context of language tasks and when exa...
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07/10/2021
Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning
Network traffic classification that is widely applicable and highly accu...
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06/25/2021
Approximate Maximum Halfspace Discrepancy
Consider the geometric range space (X, ℋ_d) where X ⊂ℝ^d and ℋ_d is the ...
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04/06/2021
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
Word vector embeddings have been shown to contain and amplify biases in ...
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07/31/2020
Orientation-Preserving Vectorized Distance Between Curves
We introduce an orientation-preserving landmark-based distance for conti...
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06/30/2020
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Language representations are known to carry stereotypical biases and, as...
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02/05/2020
A Deterministic Streaming Sketch for Ridge Regression
We provide a deterministic space-efficient algorithm for estimating ridg...
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12/16/2019
Finding the Mode of a Kernel Density Estimate
Given points p_1, ..., p_n in R^d, how do we find a point x which maximi...
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07/04/2019
Sketched MinDist
We consider sketch vectors of geometric objects J through the function ...
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06/04/2019
Scalable Spatial Scan Statistics for Trajectories
We define several new models for how to define anomalous regions among e...
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03/19/2019
Independent Range Sampling, Revisited Again
We revisit the range sampling problem: the input is a set of points wher...
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03/07/2019
The VC Dimension of Metric Balls under Fréchet and Hausdorff Distances
The Vapnik-Chervonenkis dimension provides a notion of complexity for sy...
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11/09/2018
Relative Error RKHS Embeddings for Gaussian Kernels
We show how to obliviously embed into the reproducing kernel Hilbert spa...
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09/19/2018
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
With the popularity of portable wireless devices it is important to mode...
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06/04/2018
Absolute Orientation for Word Embedding Alignment
We propose a new technique to align word embeddings which are derived fr...
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04/30/2018
Practical Low-Dimensional Halfspace Range Space Sampling
We develop, analyze, implement, and compare new algorithms for creating ...
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04/30/2018
Computing Approximate Statistical Discrepancy
Consider a geometric range space (X,A̧) where each data point x ∈ X has ...
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04/30/2018
A Data-Dependent Distance for Regression
We develop a new data-dependent distance for regression problems to comp...
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02/06/2018
Near-Optimal Coresets of Kernel Density Estimates
We construct near-optimal coresets for kernel density estimate for point...
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10/18/2017
Visualizing Sensor Network Coverage with Location Uncertainty
We present an interactive visualization system for exploring the coverag...
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10/11/2017
Improved Coresets for Kernel Density Estimates
We study the construction of coresets for kernel density estimates. That...
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09/13/2017
Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
The size of large, geo-located datasets has reached scales where visuali...
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09/05/2016
The Robustness of Estimator Composition
We formalize notions of robustness for composite estimators via the noti...
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12/16/2015
Streaming Kernel Principal Component Analysis
Kernel principal component analysis (KPCA) provides a concise set of bas...
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06/14/2013
Rethinking Abstractions for Big Data: Why, Where, How, and What
Big data refers to large and complex data sets that, under existing appr...
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04/16/2012
Efficient Protocols for Distributed Classification and Optimization
In distributed learning, the goal is to perform a learning task over dat...
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02/27/2012
Protocols for Learning Classifiers on Distributed Data
We consider the problem of learning classifiers for labeled data that ha...
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03/02/2010
A Unified Algorithmic Framework for Multi-Dimensional Scaling
In this paper, we propose a unified algorithmic framework for solving ma...
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01/04/2010
Comparing Distributions and Shapes using the Kernel Distance
Starting with a similarity function between objects, it is possible to d...
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06/22/2006