Publications

Biological Sequence Design with GFlowNets

Moksh Jain

Emmanuel Bengio

Alex-Hernandez Garcia

Jarrid Rector-Brooks

Bonaventure F. P. Dossou

Chanakya Ekbote

Michael Kilgour

Payel Das

Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several r… (see more)ounds of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple stages, with increasing levels of precision and cost of evaluation, where candidates are filtered. This makes the diversity of proposed candidates a key consideration in the ideation phase. In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round. We also propose a scheme to incorporate existing labeled datasets of candidates, in addition to a reward function, to speed up learning in GFlowNets. We present empirical results on several biological sequence design tasks, and we find that our method generates more diverse and novel batches with high scoring candidates compared to existing approaches.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Building Robust Ensembles via Margin Boosting

Dinghuai Zhang

Hongyang Zhang

Aaron Courville

Yoshua Bengio

Pradeep Ravikumar

Arun Sai Suggala

In the context of adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attac… (see more)ks, and as a result, has sub-optimal robustness. Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks. In this work, we take a principled approach towards building robust ensembles. We view this problem from the perspective of margin-boosting and develop an algorithm for learning an ensemble with maximum margin. Through extensive empirical evaluation on benchmark datasets, we show that our algorithm not only outperforms existing ensembling techniques, but also large models trained in an end-to-end fashion. An important byproduct of our work is a margin-maximizing cross-entropy (MCE) loss, which is a better alternative to the standard cross-entropy (CE) loss. Empirically, we show that replacing the CE loss in state-of-the-art adversarial training techniques with our MCE loss leads to significant performance improvement.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Characterizing User Behaviors in Open-Source Software User Forums: An Empirical Study

Jazlyn Hellman

Jiahao Chen

Md. Sami Uddin

Jinghui Cheng

Jin L.C. Guo

User forums of Open Source Software (OSS) enable end-users to collaboratively discuss problems concerning the OSS applications. Despite deca… (see more)des of research on OSS, we know very little about how end-users engage with OSS communities on these forums, in particular, the challenges that hinder their continuous and meaningful participation in the OSS community. Many previous works are developer-centric and overlook the importance of end-user forums. As a result, end-users' expectations are seldom reflected in OSS development. To better understand user behaviors in OSS user forums, we carried out an empirical study analyzing about 1.3 million posts from user forums of four popular OSS applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the contribution patterns of three common user types (end-users, developers, and organizers), we observed that end-users not only initiated most of the threads (above 96% of threads in three projects, 86% in the other), but also acted as the significant contributors for responding to other users' posts, even though they tended to lack confidence in their activities as indicated by psycho-linguistic analyses. Moreover, we found end-users more open, reflecting a more positive emotion in communication than organizers and developers in the forums. Our work contributes new knowledge about end-users' activities and behaviors in OSS user forums that the vital OSS stakeholders can leverage to improve end-user engagement in the OSS development process.

2022-07-18

Proceedings of the 15th International Conference on Cooperative and Human Aspects of Software Engineering (published)

doi.org

arxiv.org

Direct Behavior Specification via Constrained Reinforcement Learning

Christopher Pal

The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. Most oft… (see more)en, practitioners go about the task of behavior specification by manually engineering the reward function, a counter-intuitive process that requires several iterations and is prone to reward hacking by the agent. In this work, we argue that constrained RL, which has almost exclusively been used for safe RL, also has the potential to significantly reduce the amount of work spent for reward specification in applied RL projects. To this end, we propose to specify behavioral preferences in the CMDP framework and to use Lagrangian methods to automatically weigh each of these behavioral constraints. Specifically, we investigate how CMDPs can be adapted to solve goal-based tasks while adhering to several constraints simultaneously. We evaluate this framework on a set of continuous control tasks relevant to the application of Reinforcement Learning for NPC design in video games.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

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

Harley Wiltzer

David Meger

Bellemare Marc-Emmanuel

Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not nat… (see more)urally divided into discrete increments. Here we consider the problem of predicting the distribution of returns obtained by an agent interacting in a continuous-time, stochastic environment. Accurate return predictions have proven useful for determining optimal policies for risk-sensitive control, learning state representations, multiagent coordination, and more. We begin by establishing the distributional analogue of the Hamilton-Jacobi-Bellman (HJB) equation for Itô diffusions and the broader class of Feller-Dynkin processes. We then specialize this equation to the setting in which the return distribution is approximated by

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning

Arnab Kumar Mondal

Vineet Jain

Kaleem Siddiqi

Siamak Ravanbakhsh

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

proceedings.mlr.press

Generative Coarse-Graining of Molecular Conformations

Wujie Wang

Minkai Xu

Chen Cai

Benjamin K Miller

Tess Smidt

Yusu Wang

Jian Tang

Rafael Gomez-Bombarelli

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drasti… (see more)cally accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i.e., restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspired by the recent progress in generative models and equivariant networks, we propose a novel model that rigorously embeds the vital probabilistic nature and geometric consistency requirements of the backmapping transformation. Our model encodes the FG uncertainties into an invariant latent space and decodes them back to FG geometries via equivariant convolutions. To standardize the evaluation of this domain, we provide three comprehensive benchmarks based on molecular dynamics trajectories. Experiments show that our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.

2022-07-18

ICML (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Generative Flow Networks for Discrete Probabilistic Modeling

Nikolay Malkin

Alexandra Volokhova

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Buil… (see more)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.

2022-07-18

ICML (Accept for Short Presentation)

doi.org

proceedings.mlr.press

IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Emanuele Bugliarello

Fangyu Liu

Jonas Pfeiffer

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of… (see more) a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target-source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.

2022-07-18

ICML (Accept for Short Presentation)

doi.org

proceedings.mlr.press

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… (see more)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.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Multi-scale Feature Learning Dynamics: Insights for Double Descent

A key challenge in building theoretical foundations for deep learning is the complex optimization dynamics of neural networks, resulting fro… (see more)m the high-dimensional interactions between the large number of network parameters. Such non-trivial dynamics lead to intriguing 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. By leveraging tools from statistical physics, 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.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic me… (see more)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.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

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