Multilayer aggregation with statistical validation: Application to investor networks, Scientific Reports, vol.8, issue.1, p.8198, 2018. ,
Unifying collaborative and content-based filtering, Proceedings of the twenty-first International Conference on Machine Learning, p.9, 2004. ,
Practical recommendations for gradient-based training of deep architectures, Neural Networks: Tricks of the Trade, pp.437-478, 2012. ,
Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society: Series B (Methodological), vol.57, issue.1, pp.289-300, 1995. ,
The Netflix Prize, Proceedings of KDD Cup and Workshop, p.35, 2007. ,
Mixing times of markov chains: Techniques and examples, Latin American Journal of Probability and Mathematical Statistics, 2016. ,
On the use of cross-validation for time series predictor evaluation, Information Sciences, vol.191, pp.192-213, 2012. ,
Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol.13, pp.281-305, 2012. ,
The continuous cold start problem in e-commerce recommender systems, 2015. ,
The pricing of options and corporate liabilities, Journal of Political Economy, vol.81, issue.3, pp.637-654, 1973. ,
Multiple significance tests: the Bonferroni method, British Medical Journal, vol.310, issue.6973, p.170, 1995. ,
, Bloomberg Professional Services
, Sure time to grasp the potential of structured products, Bloomberg Professional Services, 2019.
Recommender systems survey. Knowledge-based Systems, vol.46, pp.109-132, 2013. ,
Random forests, Machine Learning, vol.45, pp.5-32, 2001. ,
Statistically validated leadlag networks and inventory prediction in the foreign exchange market, Advances in Complex Systems, vol.21, issue.08, p.1850019, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01705087
Gradient descent optimization of smoothed information retrieval metrics, Information Retrieval, vol.13, issue.3, pp.216-235, 2010. ,
SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, vol.16, pp.321-357, 2002. ,
XGBoost: A scalable tree boosting system, Proceedings of the twenty-second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794, 2016. ,
Wide & deep learning for recommender systems, Proceedings of the first Workshop on Deep Learning for Recommender Systems, pp.7-10, 2016. ,
, Pre-training text encoders as discriminators rather than generators. International Conference on Learning Representations, 2020.
Deep neural networks for YouTube recommendations, Proceedings of the tenth ACM Conference on Recommender Systems, pp.191-198, 2016. ,
Emergence of statistically validated financial intraday lead-lag relationships, Quantitative Finance, vol.15, issue.8, pp.1375-1386, 2015. ,
Are we really making much progress? a worrying analysis of recent neural recommendation approaches, Proceedings of the thirteenth ACM Conference on Recommender Systems, pp.101-109, 2019. ,
Transformer-XL: Attentive language models beyond a fixed-length context, Proceedings of the fifty-seventh Annual Meeting of the Association for Computational Linguistics, pp.2978-2988, 2019. ,
The relationship between Precision-Recall and ROC curves, Proceedings of the twenty-third International Conference on Machine Learning, pp.233-240, 2006. ,
Advances in financial machine learning, 2018. ,
BERT: Pre-training of deep bidirectional transformers for language understanding, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.4171-4186, 2019. ,
Sampler design for bayesian personalized ranking by leveraging view data, IEEE Transactions on Knowledge and Data Engineering, 2019. ,
Time weight collaborative filtering, Proceedings of the fourteenth ACM International Conference on Information and Knowledge Management, pp.485-492, 2005. ,
A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, pp.78-87, 2012. ,
Incorporating Nesterov momentum into Adam, 2016. ,
Scalable learning of non-decomposable objectives, Artificial Intelligence and Statistics, pp.832-840, 2017. ,
Pixie: A system for recommending 3+ billion items to 200+ million users in real-time, Proceedings of the 2018 World Wide Web Conference, pp.1775-1784, 2018. ,
A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, vol.96, pp.226-231, 1996. ,
Bond Markets, Analysis and Strategies -8th Edition, 2012. ,
Deep learning for time series classification: a review, Data Mining and Knowledge Discovery, vol.33, issue.4, pp.917-963, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02365025
An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006. ,
The behavior of dealers and clients on the european corporate bond market: the case of multi-dealer-to-client platforms, Market Microstructure and Liquidity, vol.2, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01393134
, A tutorial on Bayesian optimization, 2018.
A theoretical framework for deep transfer learning. Information and Inference: A, Journal of the IMA, vol.5, issue.2, pp.159-209, 2016. ,
Using collaborative filtering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, pp.61-70, 1992. ,
Deep Learning, 2016. ,
Dealing with the inventory risk: a solution to the market making problem, Mathematics and Financial Economics, vol.7, issue.4, pp.477-507, 2013. ,
SpotTune: transfer learning through adaptive fine-tuning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4805-4814, 2019. ,
Mapping individual behavior in financial markets: synchronization and anticipation, EPJ Data Science, vol.8, issue.1, p.10, 2019. ,
Applying deep learning to AirBnB search, Proceedings of the twenty-fifth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.1927-1935, 2019. ,
Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, pp.1024-1034, 2017. ,
Critical reflexivity in financial markets: a Hawkes process analysis, The European Physical Journal B, vol.86, issue.10, p.442, 2013. ,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009. ,
Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. ,
Neural collaborative filtering, Proceedings of the 26th International Conference on World Wide Web, pp.173-182, 2017. ,
Adversarial personalized ranking for recommendation, The forty-first International ACM SIGIR Conference on Research & Development in Information Retrieval, pp.355-364, 2018. ,
End-to-end training of object class detectors for mean average precision, Asian Conference on Computer Vision, pp.198-213, 2016. ,
Long short-term memory, Neural Computation, vol.9, issue.8, pp.1735-1780, 1997. ,
Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, issue.5, pp.359-366, 1989. ,
Non-negative matrix factorization with sparseness constraints, Journal of Machine Learning Research, vol.5, pp.1457-1469, 2004. ,
Collaborative filtering for implicit feedback datasets, Eighth IEEE International Conference on Data Mining, pp.263-272, 2008. ,
Learning deep ResNet blocks sequentially using boosting theory, Proceedings of the thirty-fifth International Conference on Machine Learning, 2018. ,
Options, futures and other derivatives -9th Edition, 2014. ,
Batch Normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, pp.448-456, 2015. ,
Adaptive mixtures of local experts, Neural Computation, vol.3, issue.1, pp.79-87, 1991. ,
, Categorical reparameterization with Gumbel-Softmax. International Conference on Learning Representations, 2017.
,
, Stanford CS231n Convolutional Neural Networks for Visual Recognition, pp.2020-2025, 2016.
LightGBM: A highly e cient gradient boosting decision tree, Advances in Neural Information Processing Systems, pp.3146-3154, 2017. ,
, Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV), 2017.
Adam: A method for stochastic optimization, International Conference on Learning Representations, 2015. ,
Factorization meets the neighborhood: a multifaceted collaborative filtering model, Proceedings of the fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.426-434, 2008. ,
The BellKor solution to the Netflix grand prize. Netflix Prize Documentation, vol.81, pp.1-10, 2009. ,
Collaborative filtering with temporal dynamics, Proceedings of the fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.447-456, 2009. ,
Matrix factorization techniques for recommender systems, Computer, vol.42, issue.8, pp.30-37, 2009. ,
Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015. ,
A contextual-bandit approach to personalized news article recommendation, Proceedings of the nineteenth International Conference on World Wide Web, pp.661-670, 2010. ,
Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, pp.2980-2988, 2017. ,
Simultaneous training of negatively correlated neural networks in an ensemble, IEEE Transactions on Systems, Man, and Cybernetics, vol.29, issue.6, pp.716-725, 1999. ,
, A robustly optimized BERT pretraining approach, 2019.
A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems, pp.4765-4774, 2017. ,
Visualizing data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008. ,
The concrete distribution: A continuous relaxation of discrete random variables, International Conference on Learning Representations, 2017. ,
Introduction to information retrieval, vol.16, pp.100-103, 2010. ,
UMAP: Uniform manifold approximation and projection for dimension reduction, 2018. ,
Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems, pp.3111-3119, 2013. ,
Probabilistic matrix factorization, Advances in Neural Information Processing Systems, pp.1257-1264, 2008. ,
Playing Atari with deep reinforcement learning, Advances in Neural Information Processing Systems, 2013. ,
Asynchronous methods for deep reinforcement learning, International Conference on Machine Learning, pp.1928-1937, 2016. ,
Machine learning: a probabilistic perspective, 2012. ,
Long-term ecology of investors in a financial market, vol.4, p.92, 2018. ,
Rectified linear units improve restricted Boltzmann machines, Proceedings of the twenty-seventh International Conference on Machine Learning (ICML-10), pp.807-814, 2010. ,
Information and Recommender Systems, 2015. ,
A method for solving the convex programming problem with convergence rate o(1/k 2 ), Dokl. Akad. Nauk SSSR, vol.269, pp.543-547, 1983. ,
, Neural Networks and Deep Learning, 2015.
, WaveNet: A generative model for raw audio, 2016.
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.5, issue.2, pp.111-126, 1994. ,
Technical Analysis Library using Pandas, 2018. ,
The long tail of recommender systems and how to leverage it, Proceedings of the 2008 ACM Conference on Recommender Systems, pp.11-18, 2008. ,
Fast ALS-based matrix factorization for explicit and implicit feedback datasets, Proceedings of the fourth ACM Conference on Recommender Systems, pp.71-78, 2010. ,
Consistent cross-validatory model-selection for dependent data: hv-block crossvalidation, Journal of Econometrics, vol.99, issue.1, pp.39-61, 2000. ,
Language models are unsupervised multitask learners, OpenAI Blog, vol.1, issue.8, p.9, 2019. ,
BPR: Bayesian personalized ranking from implicit feedback, Proceedings of the twenty-fifth Conference on Uncertainty in Artificial Intelligence, pp.452-461, 2009. ,
Explaining the predictions of any classifier, Proceedings of the twenty-second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144, 2016. ,
Maps of random walks on complex networks reveal community structure, Proceedings of the National Academy of Sciences, vol.105, issue.4, pp.1118-1123, 2008. ,
Learning representations by backpropagating errors, Nature, vol.323, issue.6088, pp.533-536, 1986. ,
ImageNet large scale visual recognition challenge, International Journal of Computer Vision, vol.115, issue.3, pp.211-252, 2015. ,
A latent space approach to dynamic embedding of co-occurrence data, In Artificial Intelligence and Statistics, pp.420-427, 2007. ,
A value for n-person games, Contributions to the Theory of Games, vol.2, pp.307-317, 1953. ,
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, International Conference on Learning Representations, 2017. ,
TFMAP: optimizing MAP for top-n context-aware recommendation, Proceedings of the thiry-fifth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.155-164, 2012. ,
Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges, ACM Computing Surveys (CSUR), vol.47, issue.1, pp.1-45, 2014. ,
Learning important features through propagating activation di erences, Proceedings of the thirty-fourth International Conference on Machine Learning, pp.3145-3153, 2017. ,
Universal features of price formation in financial markets: Perspectives from deep learning, Quantitative Finance, vol.19, issue.9, pp.1449-1459, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01754054
Amazon search: The joy of ranking products, Proceedings of the thiry-ninth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.459-460, 2016. ,
Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,
A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009. ,
On the importance of initialization and momentum in deep learning, International Conference on Machine Learning, pp.1139-1147, 2013. ,
Alternating least squares for personalized ranking, Proceedings of the sixth ACM Conference on Recommender Systems, pp.83-90, 2012. ,
, The European Commission. Markets in Financial Instruments Directive, II -Scopes and Definitions, 2014.
The problem of concept drift: definitions and related work, Computer Science Department, vol.106, issue.2, p.58, 2004. ,
Statistically validated networks in bipartite complex systems, PloS one, vol.6, issue.3, 2011. ,
Identification of clusters of investors from their real trading activity in a financial market, New Journal of Physics, vol.14, issue.1, p.13041, 2012. ,
Attention is all you need, Advances in Neural Information Processing Systems, pp.5998-6008, 2017. ,
Residual networks behave like ensembles of relatively shallow networks, Advances in Neural Information Processing Systems, pp.550-558, 2016. ,
Learning hidden features for contextual bandits, Proceedings of the twenty-fifth ACM International Conference on Information and Knowledge Management, pp.1633-1642, 2016. ,
Neural graph collaborative filtering, Proceedings of the fourty-second International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.165-174, 2019. ,
Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992. ,
Machine learning and corporate bond trading, vol.7, pp.105-110, 2018. ,
Recurrent recommender networks, Proceedings of the tenth ACM International Conference on Web Search and Data Mining, pp.495-503, 2017. ,
, Neural tensor factorization, 2018.
Temporal recommendation on graphs via long-and short-term preference fusion, Proceedings of the sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.723-732, 2010. ,
Temporal collaborative filtering with bayesian probabilistic tensor factorization, Proceedings of the 2010 SIAM International Conference on Data Mining, pp.211-222, 2010. ,
XLNet: Generalized autoregressive pretraining for language understanding, Advances in Neural Information Processing Systems, pp.5754-5764, 2019. ,
Twenty years of mixture of experts, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.8, pp.1177-1193, 2012. ,
Information filtering by similarity-preferential di usion processes, Europhysics Letters), vol.105, issue.5, p.58002, 2014. ,
, Lookahead Optimizer: k steps forward, 1 step back, 2019.
Optimizing top-n collaborative filtering via dynamic negative item sampling, Proceedings of the thirty-sixth International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.785-788, 2013. ,
Drn: A deep reinforcement learning framework for news recommendation, Proceedings of the 2018 World Wide Web Conference, pp.167-176, 2018. ,
Bipartite network projection and personal recommendation, Physical Review E, vol.76, issue.4, p.46115, 2007. ,
Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, vol.107, issue.10, pp.4511-4515, 2010. ,
,
20 1.8 Candidate generation network of the YouTube recommender system, p.22 ,
27 2.1 Evolution of validation symmetrized mAP score with ? ,
54 3.5 Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet-100, Universality matrix of investors' strategies ,
, Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet on the TEF dataset
, Distribution over experts of all investors and UMAP visualization of investors embeddings for ExNet on the BNPP CIB Bonds' RFQ dataset, p.58
, Global architecture of the Average Network
, Architecture of the HCF network and details on used convolutions, p.69
, Evolution of validation symmetrized mAP with training window size, p.72
, Evolution of validation symmetrized mAP with training window size with benchmark optimized for various windows
Daily evolution of symmetrized mAP during the test period, p.74 ,
, Evolution of HCF test performance with history size
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, Evolution of scores with time for a low-activity user on two di erent items, p.79
, Evolution of American Treasury bonds scores during the test period for the most active user with two di erent features
, Statistically Validated Network obtained on corporate bonds RFQ data, p.88
, A split of purged K-fold cross-validation
, Overall average precision test performance as a function of overall average precision validation performance
, Weight repartition of the entropic stacking strategy
, Heatmap visualization of the Pearson correlations between the stacked models, p.94
, Example of data volume reduction for a particular investor using investor and lag attentions
, Heatmap visualization of investor-investor and investor-lag attention weights for the -= 0 experiment
, Heatmap visualization of investor-investor and investor-lag attention weights for the -= 0
, A proposal neural network architecture merging the ideas underpinning the ExNet and HCF algorithms
Global outline of the perceptron model ,
, A simple perceptron and its R 2 representation
, An example of multi-layer perceptron
An example of convolutional layer ,
80 4.4 Qualitative comparison of classic and featurized versions of HCF, A.6 An example of convolutional neural network ,
, , vol.89, p.118