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Ara Vartanian

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5 papers

1 author row

AAAI Conference 2023 Conference Paper

Training-Time Attacks against K-nearest Neighbors

Ara Vartanian
Will Rosenbaum
Scott Alfeld

Nearest neighbor-based methods are commonly used for classification tasks and as subroutines of other data-analysis methods. An attacker with the capability of inserting their own data points into the training set can manipulate the inferred nearest neighbor structure. We distill this goal to the task of performing a training-set data insertion attack against k-Nearest Neighbor classification (kNN). We prove that computing an optimal training-time (a.k.a. poisoning) attack against kNN classification is NP-Hard, even when k = 1 and the attacker can insert only a single data point. We provide an anytime algorithm to perform such an attack, and a greedy algorithm for general k and attacker budget. We provide theoretical bounds and empirically demonstrate the effectiveness and practicality of our methods on synthetic and real-world datasets. Empirically, we find that kNN is vulnerable in practice and that dimensionality reduction is an effective defense. We conclude with a discussion of open problems illuminated by our analysis.

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AAAI Conference 2019 Conference Paper

Attacking Data Transforming Learners at Training Time

Scott Alfeld
Ara Vartanian
Lucas Newman-Johnson
Benjamin I.P. Rubinstein

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, little is known about how to adapt attacks when the defender transforms data prior to model estimation. We consider the setting where the defender Bob first transforms the data then learns a model from the result; Alice, the attacker, perturbs Bob’s input data prior to him transforming it. We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression. This allows learning algorithms and data transformations to be paired and composed arbitrarily: attacks can be adapted through the use of the chain rule—analogous to backpropagation on neural network parameters—to compositional learning maps. Bestresponse attacks can be computed through matrix multiplications from a library of attack matrices for transformations and learners. Our treatment of linear regression extends state-ofthe-art attacks at training time, by permitting the attacker to affect both features and targets optimally and simultaneously. We explore several transformations broadly used across machine learning with a driving motivation for our work being autogressive modeling. There, Bob transforms a univariate time series into a matrix of observations and vector of target values which can then be fed into standard learners. Under this learning reduction, a perturbation from Alice to a single value of the time series affects features of several data points along with target values.

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NeurIPS Conference 2019 Conference Paper

Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models

Farnam Mansouri
Yuxin Chen
Ara Vartanian
Jerry Zhu
Adish Singla

Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest to lower teaching complexity and to achieve more natural teacher-learner interactions, several teaching models and complexity measures have been proposed for both the batch settings (e. g. , worst-case, recursive, preference-based, and non-clashing models) as well as the sequential settings (e. g. , local preference-based model). To better understand the connections between these different batch and sequential models, we develop a novel framework which captures the teaching process via preference functions $\Sigma$. In our framework, each function $\sigma \in \Sigma$ induces a teacher-learner pair with teaching complexity as $\TD(\sigma)$. We show that the above-mentioned teaching models are equivalent to specific types/families of preference functions in our framework. This equivalence, in turn, allows us to study the differences between two important teaching models, namely $\sigma$ functions inducing the strongest batch (i. e. , non-clashing) model and $\sigma$ functions inducing a weak sequential (i. e. , local preference-based) model. Finally, we identify preference functions inducing a novel family of sequential models with teaching complexity linear in the VC dimension of the hypothesis class: this is in contrast to the best known complexity result for the batch models which is quadratic in the VC dimension.

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NeurIPS Conference 2016 Conference Paper

Active Learning with Oracle Epiphany

Tzu-Kuo Huang
Lihong Li
Ara Vartanian
Saleema Amershi
Jerry Zhu

We present a theoretical analysis of active learning with more realistic interactions with human oracles. Previous empirical studies have shown oracles abstaining on difficult queries until accumulating enough information to make label decisions. We formalize this phenomenon with an “oracle epiphany model” and analyze active learning query complexity under such oracles for both the realizable and the agnos- tic cases. Our analysis shows that active learning is possible with oracle epiphany, but incurs an additional cost depending on when the epiphany happens. Our results suggest new, principled active learning approaches with realistic oracles.

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IJCAI Conference 2016 Conference Paper

Stochastic Multiresolution Persistent Homology Kernel

Xiaojin Zhu
Ara Vartanian
Manish Bansal
Duy Nguyen
Luke Brandl

We introduce a new topological feature representation for point cloud objects. Specifically, we construct a Stochastic Multiresolution Persistent Homology (SMURPH) kernel which represents an object's persistent homology at different resolutions. Under the SMURPH kernel two objects are similar if they have similar number and sizes of "holes" at these resolutions. Our multiresolution kernel can capture both global topology and fine-grained topological texture in the data. Importantly, on large point clouds the SMURPH kernel is more computationally tractable compared to existing topological data analysis methods. We demonstrate SMURPH's potential for clustering and classification on several applications, including eye disease classification and human activity recognition.

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