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Found 27 papers, 6 papers with code
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Such features naturally arise in merchandised recommendation systems; for instance, "user clicked this item" as a feature is predictive of "user purchased this item" in the offline data, but is clearly not available during online serving.
DS-FACTO: Doubly Separable Factorization Machines
Traditional algorithms for FM which work on a single-machine are not equipped to handle this scale and therefore, using a distributed algorithm to parallelize the computation across a cluster is inevitable.
A Zero Attention Model for Personalized Product Search
In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine.
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
We further apply our algorithm to optimize a message that promotes adoption of an Amazon service.
Batch-Expansion Training: An Efficient Optimization Framework
We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset.
Online Learning of Combinatorial Objects via Extended Formulation
We present applications of our framework to online learning of Huffman trees and permutations.
Extreme Stochastic Variational Inference: Distributed and Asynchronous
Moreover, it requires the parameters to fit in the memory of a single processor; this is problematic when the number of parameters is in billions.
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
A Structural Smoothing Framework For Robust Graph Comparison
In this paper, we propose a general smoothing framework for graph kernels by taking \textit{structural similarity} into account, and apply it to derive smoothed variants of popular graph kernels.
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme.
Deep Graph Kernels
In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning.
Ranked #2 on
Malware Clustering
on Android Malware Dataset
WordRank: Learning Word Embeddings via Robust Ranking
Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
Totally Corrective Boosting with Cardinality Penalization
If actual quantum optimization were to be used with this algorithm in the future, we would expect equivalent or superior results at much smaller time and energy costs during training.
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands).
Distributed Stochastic Optimization of the Regularized Risk
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task.
DFacTo: Distributed Factorization of Tensors
We present a technique for significantly speeding up Alternating Least Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor factorization.
The Structurally Smoothed Graphlet Kernel
This vector representation can be used in a variety of applications, such as, for computing similarity between graphs.
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification.
Modeling Attractiveness and Multiple Clicks in Sponsored Search Results
Our model seamlessly incorporates the effect of externalities (quality of other search results displayed in response to a user query), user fatigue, as well as pre and post-click relevance of a sponsored search result.
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion.
Distributed, Parallel, and Cluster Computing
t-divergence Based Approximate Inference
Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions.
Multitask Learning without Label Correspondences
We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available.
Multiple Kernel Learning and the SMO Algorithm
Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm.
t-logistic regression
We extend logistic regression by using t-exponential families which were introduced recently in statistical physics.
Lower Bounds on Rate of Convergence of Cutting Plane Methods
By exploiting the structure of the objective function we can devise an algorithm that converges in $O(1/\sqrt{\epsilon})$ iterations.
Efficient graphlet kernels for large graph comparison
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges.
Ranked #84 on
Graph Classification
on PROTEINS
