Publications

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
Lazar Atanackovic
Alexander Tong
Jason Hartford
Leo J Lee
Bo Wang
Yoshua Bengio
One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and th… (voir plus)eir products that control gene expression and cellular function. We can treat this as a causal discovery problem but with two non-standard challenges: (1) regulatory networks are inherently cyclic so we should not model a GRN as a directed acyclic graph (DAG), and (2) observations have significant measurement noise, so for typical sample sizes there will always be a large equivalence class of graphs that are likely given the data, and we want methods that capture this uncertainty. Existing methods either focus on challenge (1), identifying cyclic structure from dynamics, or on challenge (2) learning complex Bayesian posteriors over DAGs, but not both. In this paper we leverage the fact that it is possible to estimate the"velocity"of gene expression with RNA velocity techniques to develop an approach that addresses both challenges. Because we have access to velocity information, we can treat the Bayesian structure learning problem as a problem of sparse identification of a dynamical system, capturing cyclic feedback loops through time. Since our objective is to model uncertainty over discrete structures, we leverage Generative Flow Networks (GFlowNets) to estimate the posterior distribution over the combinatorial space of possible sparse dependencies. Our results indicate that our method learns posteriors that better encapsulate the distributions of cyclic structures compared to counterpart state-of-the-art Bayesian structure learning approaches.
2023-02-08
ArXiv (prépublication)
arxiv.org
arxiv.org
Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42
Ramesh Mainali
Daniel P Stark
Tucker Jones
Richard S Ellis
Yashar Hezaveh
Jane R Rigby
We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (voir plus)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.
2023-02-08
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
DeltaShield: Information Theory for Human- Trafficking Detection
Catalina Vajiac
Meng-Chieh Lee
Aayushi Kulshrestha
Sacha Lévy
Namyong Park
Andreas Olligschlaeger
Cara Jones
Reihaneh Rabbany
Christos Faloutsos
2023-02-07
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org
Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amin Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar
2023-02-06
ArXiv (prépublication)
doi.org
arxiv.org
A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions (preprint)
Dirk Douwes-Schultz
Alexandra M. Schmidt
Yannan Shen
David Buckeridge
2023-02-05
ArXiv (prépublication)
arxiv.org
arxiv.org
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese
Marco Romanelli
Siddharth Garg
Pablo Piantanida
2023-02-04
ArXiv (prépublication)
doi.org
arxiv.org
AmbieGen tool at the SBST 2022 Tool Competition
Dmytro Humeniuk
Giuliano Antoniol
Foutse Khomh
AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (voir plus) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.
2023-02-03
Proceedings of the 15th Workshop on Search-Based Software Testing (publié)
doi.org
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Yoshua Bengio
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an en… (voir plus)ergy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object
2023-02-03
ArXiv (prépublication)
doi.org
arxiv.org
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry
David Meger
2023-02-02
ArXiv (prépublication)
doi.org
arxiv.org
A Survey on Compositional Generalization in Applications
Baihan Lin
Djallel Bouneffouf
Irina Rish
2023-02-02
ArXiv (prépublication)
doi.org
arxiv.org
Broken Neural Scaling Laws
Ethan Caballero
Kshitij Gupta
Irina Rish
David Scott Krueger
We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models&extra… (voir plus)polates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as amount of compute used for training (or inference), number of model parameters, training dataset size, model input size, number of training steps, or upstream performance varies) for various architectures&for each of various tasks within a large&diverse set of upstream&downstream tasks, in zero-shot, prompted,&finetuned settings. This set includes large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, AI capabilities, robotics, out-of-distribution (OOD) generalization, continual learning, transfer learning, uncertainty estimation / calibration, OOD detection, adversarial robustness, distillation, sparsity, retrieval, quantization, pruning, fairness, molecules, computer programming/coding, math word problems,"emergent phase transitions", arithmetic, supervised learning, unsupervised/self-supervised learning,&reinforcement learning (single agent&multi-agent). When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models&extrapolates scaling behavior that other functional forms are incapable of expressing such as the nonmonotonic transitions present in the scaling behavior of phenomena such as double descent&the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. Code is available at https://github.com/ethancaballero/broken_neural_scaling_laws
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Critic Sequential Monte Carlo
Vasileios Lioutas
Jonathan Wilder Lavington
Justice Sefas
Matthew Niedoba
Yunpeng Liu
Berend Zwartsenberg
Setareh Dabiri
Frank N. Wood
Adam Ścibior
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q fun… (voir plus)ction heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpful for planning in environments with hard constraints placed sparsely in time. Compared with previous work, we modify the placement of such heuristic factors, which allows us to cheaply propose and evaluate large numbers of putative action particles, greatly increasing inference and planning efficiency. CriticSMC is compatible with informative priors, whose density function need not be known, and can be used as a model-free control algorithm. Our experiments on collision avoidance in a high-dimensional simulated driving task show that CriticSMC significantly reduces collision rates at a low computational cost while maintaining realism and diversity of driving behaviors across vehicles and environment scenarios.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net