Portrait de Nikolay Malkin

Nikolay Malkin

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Yoshua Bengio
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Modèles probabilistes
Raisonnement
Traitement du langage naturel
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu
Mizu Nishikawa-Toomey
Jithendaraa Subramanian
Nikolay Malkin
Laurent Charlin
Yoshua Bengio
Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied … (voir plus)to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.
openreview.net
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
openreview.net
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
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
GFlowNet-EM for Learning Compositional Latent Variable Models
Edward J Hu
Nikolay Malkin
Moksh J. Jain
Katie E Everett
Alexandros Graikos
Yoshua Bengio
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large nu… (voir plus)mber of possible configurations of the latents. A key tradeoff in modeling the posteriors over latents is between expressivity and tractable optimization. For algorithms based on expectation-maximization (EM), the E-step is often intractable without restrictive approximations to the posterior. We propose the use of GFlowNets, algorithms for sampling from an unnormalized density by learning a stochastic policy for sequential construction of samples, for this intractable E-step. By training GFlowNets to sample from the posterior over latents, we take advantage of their strengths as amortized variational inference algorithms for complex distributions over discrete structures. Our approach, GFlowNet-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-context-free grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A Malik
Salem Lahlou
Andrew Jesson
Moksh J. Jain
Nikolay Malkin
Tristan Deleu
Yoshua Bengio
Yarin Gal
We introduce BatchGFN—a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points pro… (voir plus)portional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able to construct highly informative batches for active learning in a principled way. We show our approach enables sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems. This alleviates the computational complexity of batch-aware algorithms and removes the need for greedy approximations to find maximizers for the batch reward. We also present early results for amortizing training across acquisition steps, which will enable scaling to real-world tasks.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net
Simulation-Free Schrödinger Bridges via Score and Flow Matching
Alexander Tong
Nikolay Malkin
Kilian FATRAS
Lazar Atanackovic
Yanlei Zhang
Guillaume Huguet
Guy Wolf
Yoshua Bengio
We present simulation-free score and flow matching ([SF]…
2023-06-19
ICML.cc/2023/Workshop/Frontiers4LCD (publié)
doi.org
openreview.net
Thompson Sampling for Improved Exploration in GFlowNets
Jarrid Rector-Brooks
Kanika Madan
Moksh J. Jain
Maksym Korablyov
Cheng-Hao Liu
Sarath Chandar
Nikolay Malkin
Yoshua Bengio
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over composition… (voir plus)al objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (voir plus)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.
2023-05-26
ArXiv (prépublication)
doi.org
arxiv.org
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Yoshua Bengio
2023-04-24
ICML.cc/2023/Conference (poster)
doi.org
openreview.net
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
GFlowNets and variational inference
Nikolay Malkin
Salem Lahlou
Tristan Deleu
Xu Ji
Edward J Hu
Katie E Everett
Dinghuai Zhang
Yoshua Bengio
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically us… (voir plus)ed to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Guy Wolf
Yoshua Bengio
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (voir plus)mulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, we show that when the true OT plan is available, our OT-CFM method approximates dynamic OT. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schr\"odinger bridge inference.
2023-02-01
ArXiv (prépublication)
arxiv.org
arxiv.org