Author name cluster

Cheng-Hao Liu

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8 papers

2 author rows

AAAI Conference 2025 Conference Paper

Flow Factorization for Efficient Generative Flow Networks

Jiashun Liu
Chunhui Li
Cheng-Hao Liu
Dianbo Liu
Qingpeng Cai
Ling Pan

Generative Flow Networks (GFlowNets) is a new family of probabilistic samplers for generating objects under an unnormalized reward distribution. It has emerged as a promising framework for learning stochastic policies that generate high-quality and diverse discrete objects proportional to their rewards, surpassing traditional reward-maximizing reinforcement learning methods. However, existing GFlowNets often suffer with data efficiency due to the direct parameterization of edge flows or dependence on backward policies that are challenging to specify or optimize, especially in high-dimensional action spaces. While the recent development of GFlowNets has primarily focused on developing alternative loss functions, we introduce a novel approach by exploring enhanced flow representations from an architectural perspective. In this paper, we propose to factorize the conventional edge flows into separate state flow and edge-based allocation streams. By introducing an effective method to synergistically combine these two streams to estimate the flows, we develop Bifurcated Generative Flow Networks (BN), a practical implementation to improve learning efficiency. We conduct extensive experiments on various standard benchmarks, and results show that BN significantly improves learning efficiency and effectiveness compared to state-of-the-art baselines.

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ICLR Conference 2025 Conference Paper

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Jarrid Rector-Brooks
Mohsin Hasan
Zhangzhi Peng
Cheng-Hao Liu
Sarthak Mittal
Nouha Dziri
Michael M. Bronstein
Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process—typically via RLHF—to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pretrained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

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ICLR Conference 2024 Conference Paper

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

Dinghuai Zhang
Ricky T. Q. Chen
Cheng-Hao Liu
Aaron C. Courville
Yoshua Bengio

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional ``flow function''. Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.

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ICML Conference 2024 Conference Paper

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh
Jarrid Rector-Brooks
Avishek Joey Bose
Sarthak Mittal
Pablo Lemos
Cheng-Hao Liu
Marcin Sendera
Siamak Ravanbakhsh

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient—and no data samples—to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant $n$-body particle systems. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5\times$ faster, which allows it to be the first method to train using energy on the challenging $55$-particle Lennard-Jones system.

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TMLR Journal 2024 Journal Article

Multi-Fidelity Active Learning with GFlowNets

Alex Hernández-García
Nikita Saxena
Moksh Jain
Cheng-Hao Liu
Yoshua Bengio

In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, structured and high-dimensional spaces. Moreover, the high fidelity, black-box objective function is often very expensive to evaluate. Progress in machine learning methods that can efficiently tackle such challenges would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates where multiple approximations of the black-box function are available at lower fidelity and cost. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart while maintaining diversity, unlike RL-based alternatives. These results open new avenues for multi-fidelity active learning to accelerate scientific discovery and engineering design.

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NeurIPS Conference 2024 Conference Paper

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer van der Sloot
Piotr Gaiński
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.

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ICLR Conference 2024 Conference Paper

SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Avishek Joey Bose
Tara Akhound-Sadegh
Guillaume Huguet
Kilian Fatras
Jarrid Rector-Brooks
Cheng-Hao Liu
Andrei Cristian Nica
Maksym Korablyov

The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce \foldflow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions---i.e. the group $\mathrm{SE(3)}$---enabling accurate modeling of protein backbones. We first introduce $\text{FoldFlow-Base}$, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\mathrm{SE(3)}$. We next accelerate training by incorporating Riemannian optimal transport to create $\text{FoldFlow-OT}$, leading to the construction of both more simple and stable flows. Finally, we design \foldflowsfm, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\mathrm{SE(3)}$. Our family of $\text{FoldFlow}$, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\mathrm{SE(3)}$. Empirically, we validate $\text{FoldFlow}$, on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples.

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NeurIPS Conference 2024 Conference Paper

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation

Guillaume Huguet
James Vuckovic
Kilian Fatras
Eric Thibodeau-Laufer
Pablo Lemos
Riashat Islam
Cheng-Hao Liu
Jarrid Rector-Brooks

Proteins are essential for almost all biological processes and derive their diverse functions from complex $3 \rm D$ structures, which are in turn determined by their amino acid sequences. In this paper, we exploit the rich biological inductive bias of amino acid sequences and introduce FoldFlow++, a novel sequence-conditioned $\text{SE}(3)$-equivariant flow matching model for protein structure generation. FoldFlow++ presents substantial new architectural features over the previous FoldFlow family of models including a protein large language model to encode sequence, a new multi-modal fusion trunk that combines structure and sequence representations, and a geometric transformer based decoder. To increase diversity and novelty of generated samples -- crucial for de-novo drug design -- wetrain FoldFlow++ at scale on a new dataset that is an order of magnitude larger than PDB datasets of prior works, containing both known proteins in PDB and high-quality synthetic structures achieved through filtering. We further demonstrate the ability to align FoldFlow++ to arbitrary rewards, e. g. increasing secondary structures diversity, by introducing a Reinforced Finetuning (ReFT) objective. We empirically observe that FoldFlow++ outperforms previous state-of-the-art protein structure-based generative models, improving over RFDiffusion in terms of unconditional generation across all metrics including designability, diversity, and novelty across all protein lengths, as well as exhibiting generalization on the task of equilibrium conformation sampling. Finally, we demonstrate that a fine-tuned FoldFlow++ makes progress on challenging conditional design tasks such as designing scaffolds for the VHH nanobody.

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