
Relaxed Softmax for learning from Positive and Unlabeled data
In recent years, the softmax model and its fast approximations have beco...
read it

Adversarial Robustness via Adversarial LabelSmoothing
We study LabelSmoothing as a means for improving adversarial robustness...
read it

Thompson Sampling in NonEpisodic Restless Bandits
Restless bandit problems assume timevarying reward distributions of the...
read it

Attribution Modeling Increases Efficiency of Bidding in Display Advertising
Predicting click and conversion probabilities when bidding on ad exchang...
read it

Deep CharacterLevel ClickThrough Rate Prediction for Sponsored Search
Predicting the clickthrough rate of an advertisement is a critical comp...
read it

MetaProd2Vec  Product Embeddings Using SideInformation for Recommendation
We propose MetaProd2vec, a novel method to compute item similarities fo...
read it

On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
read it

Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces
Learning to optimize  the idea that we can learn from data algorithms t...
read it

Learning Determinantal Point Processes by Sampling Inferred Negatives
Determinantal Point Processes (DPPs) have attracted significant interest...
read it

Convergent ActorCritic Algorithms Under OffPolicy Training and Function Approximation
We present the first class of policygradient algorithms that work with ...
read it

Some Theoretical Properties of GANs
Generative Adversarial Networks (GANs) are a class of generative algorit...
read it

Deeply Supervised Semantic Model for ClickThrough Rate Prediction in Sponsored Search
In sponsored search it is critical to match ads that are relevant to a q...
read it

Explicit shading strategies for repeated truthful auctions
With the increasing use of auctions in online advertising, there has bee...
read it

Neural Generative Models for Global Optimization with Gradients
The aim of global optimization is to find the global optimum of arbitrar...
read it

Adversarial Training of Word2Vec for Basket Completion
In recent years, the Word2Vec model trained with the Negative Sampling l...
read it

Basket Completion with Multitask Determinantal Point Processes
Determinantal point processes (DPPs) have received significant attention...
read it

Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
In online internet advertising, machine learning models are widely used ...
read it

Recurrent Neural Networks for Long and ShortTerm Sequential Recommendation
Recommender systems objectives can be broadly characterized as modeling ...
read it

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Recommender Systems are becoming ubiquitous in many settings and take ma...
read it

Actionconditional Sequence Modeling for Recommendation
In many online applications interactions between a user and a webservic...
read it

ROI constrained Auctions
A standard result from auction theory is that bidding truthfully in a se...
read it

Limitations of adversarial robustness: strong No Free Lunch Theorem
This manuscript presents some new results on adversarial robustness in m...
read it

Deep Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attentio...
read it

Causal Embeddings for Recommendation: An Extended Abstract
Recommendations are commonly used to modify user's natural behavior, for...
read it

Latent Variable SessionBased Recommendation
Session based recommendation provides an attractive alternative to the t...
read it

Three Methods for Training on Bandit Feedback
There are three quite distinct ways to train a machine learning model on...
read it

Unsupervised Community Detection with ModularityBased Attention Model
In this paper we take a problem of unsupervised nodes clustering on grap...
read it

Quantifying the alignment of graph and features in deep learning
We show that the classification performance of Graph Convolutional Netwo...
read it

Learning Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted substantial attentio...
read it

Regression with Conditional GAN
In recent years, impressive progress has been made in the design of impl...
read it

Robust Stackelberg buyers in repeated auctions
We consider the practical and classical setting where the seller is usin...
read it

EndtoEnd Learning of Geometric Deformations of Feature Maps for Virtual TryOn
The 2D virtual tryon task has recently attracted a lot of interest from...
read it

Distributionally Robust Counterfactual Risk Minimization
This manuscript introduces the idea of using Distributionally Robust Opt...
read it

A Bayesian Solution to the MBias Problem
It is common practice in using regression type models for inferring caus...
read it

Replacing the docalculus with Bayes rule
The concept of causality has a controversial history. The question of wh...
read it

Embedding models for recommendation under contextual constraints
Embedding models, which learn latent representations of users and items ...
read it

Recommendation Systembased Upper Confidence Bound for Online Advertising
In this paper, the method UCBRS, which resorts to recommendation system...
read it

Ranking metrics on nonshuffled traffic
Ranking metrics are a family of metrics largely used to evaluate recomme...
read it

Learning from Bandit Feedback: An Overview of the Stateoftheart
In machine learning we often try to optimise a decision rule that would ...
read it

On the Convergence of Approximate and Regularized Policy Iteration Schemes
Algorithms based on the entropy regularized framework, such as Soft Qle...
read it

How robust is MovieLens? A dataset analysis for recommender systems
Research publication requires public datasets. In recommender systems, s...
read it

Targeted display advertising: the case of preferential attachment
An average adult is exposed to hundreds of digital advertisements daily ...
read it

Quantum Bandits
We consider the quantum version of the bandit problem known as best arm...
read it
Criteo
Criteo is a personalized retargeting company that works with Internet retailers to serve personalized online display advertisements to consumers who have previously visited the advertiser's website.