We have established two hardness results for proper agnostic learning of low-degree PTFs. Our results show that even if there exist low-degree PTFs that are almost perfect hypotheses, it is computationally hard to find low-degree PTF hypotheses that…

Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In t…

Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic erro…

We now show how our techniques can be applied to solve an open problem on L1 tolerant testing of monotonicity, asked at the Sublinear Algorithms Workshop 2016 [Sub16].

A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$ alternates between the values $0$ and $1$ at most $k$ times on any asce…

Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational complexity of accurately answering a family $Q$ of statistical queries over a data universe $X$ under dif…

Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational compl…

This paper studies the problem of passive grasp stability under an external disturbance, that is, the ability of a grasp to resist a disturbance thro…

We propose a simple change to the current neural network structure for defending against gradient-based adversarial attacks. Instead of using popular…

This paper introduces a stochastic generation framework (SDVI) to infill long intervals in video sequences. Video interpolation aims to produce trans…

Variational Auto-Encoders (VAEs) have been widely applied for learning compact low-dimensional latent representations for high-dimensional data. When…

Many generative models have to combat $\textit{missing modes}$. The conventional wisdom to this end is by reducing through training a statistical dis…

Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sourc…

We study the problem of testing whether an unknown $n$-variable Boolean function is a $k$-junta in the distribution-free property testing model, wher…

Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existin…

Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform traditional machine learning models and achieve the state-of-art results on most tasks. However, many…

Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform t…

ENUM is a DNS-based protocol standard for mapping E.164 telephone numbers to Internet Uniform Resource Identifiers (URIs). It places unique requireme…

Although deep learning models perform remarkably across a range of tasks such as language translation, parsing, and object recognition, it remains un…

All generative models have to combat missing modes. The conventional wisdom is by reducing a statistical distance (such as f-divergence) between the …

Variational inference transforms posterior inference into parametric optimization thereby enabling the use of latent variable models where otherwise …

Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interacti…

In this section we establish that α-agnostic learning with α<2 is information theoretically impossible, thus establishing Theorem 1.

Let $p$ be an unknown and arbitrary probability distribution over $[0,1)$. We consider the problem of {\em density estimation}, in which a learning a…

Vision-based grasping systems typically adopt an open-loop execution of a planned grasp. This policy can fail due to many reasons, including ubiquito…

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