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Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
Daniel Severo, Ashish Khisti, Alireza Makhzani
We present an optimal method for encoding cluster assignments of arbitrary data sets. Our method, Random Cycle Coding (RCC), encodes data sequentially and sends assignment informat…
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
Daniel Severo, James Townsend, Ashish Khisti +1
We present a one-shot method for compressing large labeled graphs called Random Edge Coding. When paired with a parameter-free model based on Pólya's Urn, the worst-case computatio…
Sequential Gradient Coding For Straggler Mitigation
M. Nikhil Krishnan, MohammadReza Ebrahimi, Ashish Khisti
In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient technique that uses principles o…
Variational Model Inversion Attacks
Kuan-Chieh Wang, Yan Fu, Ke Li +3
Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion atta…
Regularized Classification-Aware Quantization
Daniel Severo, Elad Domanovitz, Ashish Khisti
Traditionally, quantization is designed to minimize the reconstruction error of a data source. When considering downstream classification tasks, other measures of distortion can be…
On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Ashish Khisti, Ben Liang
While momentum-based methods, in conjunction with stochastic gradient descent (SGD), are widely used when training machine learning models, there is little theoretical understandin…
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan, Karen Ullrich, Daniel Severo +5
Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal…
On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher +2
While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on t…