196 citations · 394 across the 27 of their papers we have counts for
15 papers · 1 filter
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
David Nigenda, Zohar Karnin, Muhammad Bilal Zafar +4
With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment ha…
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Piali Das, Valerio Perrone, Nikita Ivkin +22
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of…
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic +1
We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transf…
GANMEX: One-vs-One Attributions Guided by GAN-based Counterfactual Explanation Baselines
Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin
Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a ba…
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
Julian Katz-Samuels, Lalit Jain, Zohar Karnin +1
This paper proposes near-optimal algorithms for the pure-exploration linear bandit problem in the fixed confidence and fixed budget settings. Leveraging ideas from the theory of su…
PruneNet: Channel Pruning via Global Importance
Ashish Khetan, Zohar Karnin
Channel pruning is one of the predominant approaches for accelerating deep neural networks. Most existing pruning methods either train from scratch with a sparsity inducing term su…
Asymmetric Random Projections
Nick Ryder, Zohar Karnin, Edo Liberty
Random projections (RP) are a popular tool for reducing dimensionality while preserving local geometry. In many applications the data set to be projected is given to us in advance,…
Discrepancy, Coresets, and Sketches in Machine Learning
Zohar Karnin, Edo Liberty
This paper defines the notion of class discrepancy for families of functions. It shows that low discrepancy classes admit small offline and streaming coresets. We provide general t…
DARC: Differentiable ARchitecture Compression
Shashank Singh, Ashish Khetan, Zohar Karnin
In many learning situations, resources at inference time are significantly more constrained than resources at training time. This paper studies a general paradigm, called Different…
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Satyen Kale, Zohar Karnin, Tengyuan Liang +1
Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a rea…
Copeland Dueling Bandits
Masrour Zoghi, Zohar Karnin, Shimon Whiteson +1
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the…
Reducing Dueling Bandits to Cardinal Bandits
Nir Ailon, Thorsten Joachims, Zohar Karnin
We present algorithms for reducing the Dueling Bandits problem to the conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits problem is an online model of learn…
Volumetric Spanners: an Efficient Exploration Basis for Learning
Elad Hazan, Zohar Karnin, Raghu Mehka
Numerous machine learning problems require an exploration basis - a mechanism to explore the action space. We define a novel geometric notion of exploration basis with low variance…
Near-Optimal Entrywise Sampling for Data Matrices
Dimitris Achlioptas, Zohar Karnin, Edo Liberty
We consider the problem of selecting non-zero entries of a matrix in order to produce a sparse sketch of it, , that minimizes . For large matrices, s…
Distributed Exploration in Multi-Armed Bandits
Eshcar Hillel, Zohar Karnin, Tomer Koren +2
We study exploration in Multi-Armed Bandits in a setting where players collaborate in order to identify an -optimal arm. Our motivation comes from recent employment of bandi…