Publications

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

doi.org

arxiv.org

Estimating Social Influence from Observational Data

Dhanya Sridhar

Caterina De Bacco

David Blei

We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The k… (voir plus)ey challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.

2022-06-28

Proceedings of the First Conference on Causal Learning and Reasoning (publié)

doi.org

openreview.net

Fair Representation Learning through Implicit Path Alignment

Changjian Shui

Qi CHEN

Jiaqi Li

Boyu Wang

Christian Gagné

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

doi.org

openreview.net

A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions

Anthony GX-Chen

Veronica Chelu

Blake Richards

Joelle Pineau

2022-06-28

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Generative Flow Networks for Discrete Probabilistic Modeling

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Buil… (voir plus)ding upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic data construction policy and thus amortize expensive MCMC exploration into a fixed number of actions sampled from a GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs sampling to mix between modes. We propose a framework to jointly train a GFlowNet with an energy function, so that the GFlowNet learns to sample from the energy distribution, while the energy learns with an approximate MLE objective with negative samples from the GFlowNet. We demonstrate EB-GFN's effectiveness on various probabilistic modeling tasks. Code is publicly available at https://github.com/zdhNarsil/EB_GFN.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

proceedings.mlr.press

arxiv.org

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Utku Evci

Vincent Dumoulin

Hugo Larochelle

Michael Curtis Mozer

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

proceedings.mlr.press

arxiv.org

3D Infomax improves GNNs for Molecular Property Prediction

Hannes Stärk

Dominique Beaini

Gabriele Corso

Prudencio Tossou

Christian Dallago

Stephan Günnemann

Pietro Lio

Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D mol… (voir plus)ecular structure as input to learned models improves their predictions for many molecular properties. However, this information is infeasible to compute at the scale required by most real-world applications. We propose pre-training a model to understand the geometry of molecules given only their 2D molecular graph. Using methods from self-supervised learning, we maximize the mutual information between a 3D summary vector and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to inform downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of molecular properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Crucially, the learned representations can be effectively transferred between datasets with vastly different molecules.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

proceedings.mlr.press

openreview.net

Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning

Max B. Paulus

Giulia Zarpellon

Andreas Krause

Laurent Charlin

Chris J. Maddison

Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal so… (voir plus)lution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

doi.org

arxiv.org

Multi-scale Feature Learning Dynamics: Insights for Double Descent

Mohammad Pezeshki

Amartya Mitra

Yoshua Bengio

Guillaume Lajoie

A key challenge in building theoretical foundations for deep learning is the complex optimization dynamics of neural networks, resulting fro… (voir plus)m the high-dimensional interactions between the large number of network parameters. Such non-trivial interactions lead to intriguing model behaviors such as the phenomenon of "double descent" of the generalization error. The more commonly studied aspect of this phenomenon corresponds to model-wise double descent where the test error exhibits a second descent with increasing model complexity, beyond the classical U-shaped error curve. In this work, we investigate the origins of the less studied epoch-wise double descent in which the test error undergoes two non-monotonous transitions, or descents as the training time increases. We study a linear teacher-student setup exhibiting epoch-wise double descent similar to that in deep neural networks. In this setting, we derive closed-form analytical expressions for the evolution of generalization error over training. We find that double descent can be attributed to distinct features being learned at different scales: as fast-learning features overfit, slower-learning features start to fit, resulting in a second descent in test error. We validate our findings through numerical experiments where our theory accurately predicts empirical findings and remains consistent with observations in deep neural networks.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

proceedings.mlr.press

openreview.net

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Zhaocheng Zhu

Mikhail Galkin

Zuobai Zhang

Jian Tang

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic me… (voir plus)thods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

doi.org

arxiv.org

Only tails matter: Average-Case Universality and Robustness in the Convex Regime

Leonardo Cunha

Gauthier Gidel

Fabian Pedregosa

Damien Scieur

Courtney Paquette

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)