
Scaling Hierarchical Agglomerative Clustering to Billionsized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but sti...
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Balancing Robustness and Sensitivity using Feature Contrastive Learning
It is generally believed that robust training of extremely large network...
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Disentangling Sampling and Labeling Bias for Learning in LargeOutput Spaces
Negative sampling schemes enable efficient training given a large number...
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Balancing Constraints and Submodularity in Data Subset Selection
Deep learning has yielded extraordinary results in vision and natural la...
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On the Reproducibility of Neural Network Predictions
Standard training techniques for neural networks involve multiple source...
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Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
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Coping with Label Shift via Distributionally Robust Optimisation
The label shift problem refers to the supervised learning setting where ...
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Learning discrete distributions: user vs itemlevel privacy
Much of the literature on differential privacy focuses on itemlevel pri...
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O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Transformer networks use pairwise attention to compute contextual embedd...
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Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
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Why distillation helps: a statistical perspective
Knowledge distillation is a technique for improving the performance of a...
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Doublystochastic mining for heterogeneous retrieval
Modern retrieval problems are characterised by training sets with potent...
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Federated Learning with Only Positive Labels
We consider learning a multiclass classification model in the federated...
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Robust LargeMargin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning ...
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Does label smoothing mitigate label noise?
Label smoothing is commonly used in training deep learning models, where...
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Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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LowRank Bottleneck in Multihead Attention Models
Attention based Transformer architecture has enabled significant advance...
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Pretraining Tasks for Embeddingbased Largescale Retrieval
We consider the largescale querydocument retrieval problem: given a qu...
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Are Transformers universal approximators of sequencetosequence functions?
Despite the widespread adoption of Transformer models for NLP tasks, the...
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Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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Learning to Learn by ZerothOrder Oracle
In the learning to learn (L2L) framework, we cast the design of optimiza...
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Online Hierarchical Clustering Approximations
Hierarchical clustering is a widely used approach for clustering dataset...
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New Loss Functions for Fast Maximum Inner Product Search
Quantization based methods are popular for solving large scale maximum i...
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AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
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Sampled Softmax with Random Fourier Features
The computational cost of training with softmax cross entropy loss grows...
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Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Neural Ordinary Differential Equation (Neural ODE) has been proposed as ...
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On the Convergence of Adam and Beyond
Several recently proposed stochastic optimization methods that have been...
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Local Orthogonal Decomposition for Maximum Inner Product Search
Inverted file and asymmetric distance computation (IVFADC) have been suc...
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Efficient Inner Product Approximation in Hybrid Spaces
Many emerging use cases of data mining and machine learning operate on l...
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Escaping Saddle Points with Adaptive Gradient Methods
Adaptive methods such as Adam and RMSProp are widely used in deep learni...
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Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Neural language models have been widely used in various NLP tasks, inclu...
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Stochastic Negative Mining for Learning with Large Output Spaces
We consider the problem of retrieving the most relevant labels for a giv...
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Truncated Laplacian Mechanism for Approximate Differential Privacy
We derive a class of noise probability distributions to preserve (ϵ, δ)...
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Optimal NoiseAdding Mechanism in Additive Differential Privacy
We derive the optimal (0, δ)differentially private queryoutput indepen...
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The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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cpSGD: Communicationefficient and differentiallyprivate distributed SGD
Distributed stochastic gradient descent is an important subroutine in di...
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Nonlinear Online Learning with Adaptive Nyström Approximation
Use of nonlinear feature maps via kernel approximation has led to succes...
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Now Playing: Continuous lowpower music recognition
Existing music recognition applications require a connection to a server...
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Efficient Natural Language Response Suggestion for Smart Reply
This paper presents a computationally efficient machinelearned method f...
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Stochastic Generative Hashing
Learningbased binary hashing has become a powerful paradigm for fast se...
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Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: i...
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Binary embeddings with structured hashed projections
We consider the hashing mechanism for constructing binary embeddings, th...
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Structured Transforms for SmallFootprint Deep Learning
We consider the task of building compact deep learning pipelines suitabl...
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Learning to Hash for Indexing Big Data  A Survey
The explosive growth in big data has attracted much attention in designi...
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Quantization based Fast Inner Product Search
We propose a quantization based approach for fast approximate Maximum In...
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Fast Online Clustering with Randomized Skeleton Sets
We present a new fast online clustering algorithm that reliably recovers...
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Compact Nonlinear Maps and Circulant Extensions
Kernel approximation via nonlinear random feature maps is widely used in...
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An exploration of parameter redundancy in deep networks with circulant projections
We explore the redundancy of parameters in deep neural networks by repla...
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Circulant Binary Embedding
Binary embedding of highdimensional data requires long codes to preserv...
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On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
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Sanjiv Kumar
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Research Scientist at Google Research, NY, Principal Scientist at at Google Research, NY, PhD (2005; Robotics, SCS, CMU)