Author name cluster

Fan Ding

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7 papers

2 author rows

ICML Conference 2022 Conference Paper

Proximal Exploration for Model-guided Protein Sequence Design

Zhizhou Ren
Jiahan Li
Fan Ding
Yuan Zhou 0007
Jianzhu Ma
Jian Peng 0001

Designing protein sequences with a particular biological function is a long-lasting challenge for protein engineering. Recent advances in machine-learning-guided approaches focus on building a surrogate sequence-function model to reduce the burden of expensive in-lab experiments. In this paper, we study the exploration mechanism of model-guided sequence design. We leverage a natural property of protein fitness landscape that a concise set of mutations upon the wild-type sequence are usually sufficient to enhance the desired function. By utilizing this property, we propose Proximal Exploration (PEX) algorithm that prioritizes the evolutionary search for high-fitness mutants with low mutation counts. In addition, we develop a specialized model architecture, called Mutation Factorization Network (MuFacNet), to predict low-order mutational effects, which further improves the sample efficiency of model-guided evolution. In experiments, we extensively evaluate our method on a suite of in-silico protein sequence design tasks and demonstrate substantial improvement over baseline algorithms.

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UAI Conference 2022 Conference Paper

X-MEN: guaranteed XOR-maximum entropy constrained inverse reinforcement learning

Fan Ding
Yexiang Xue

Inverse Reinforcement Learning (IRL) is a powerful way of learning from demonstrations. In this paper, we address IRL problems with the availability of prior knowledge that optimal policies will never violate certain constraints. Conventional approaches ignoring these constraints need many demonstrations to converge. We propose XOR-Maximum Entropy Constrained Inverse Reinforcement Learning (X-MEN), which is guaranteed to converge to the global optimal reward function in linear rate w. r. t. the number of learning iterations. X-MEN embeds XOR-sampling – a provable sampling approach which transforms the #-P complete sampling problem into queries to NP oracles – into the framework of maximum entropy IRL. X-MEN also guarantees the learned IRL agent will never generate trajectories that violate constraints. Empirical results in navigation demonstrate that X-MEN converges faster to the optimal rewards compared to baseline approaches and always generates trajectories that satisfy multi-state combinatorial constraints.

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UAI Conference 2021 Conference Paper

PALM: Probabilistic area loss Minimization for Protein Sequence Alignment

Fan Ding
Nan Jiang 0012
Jianzhu Ma
Jian Peng 0001
Jinbo Xu
Yexiang Xue

Protein sequence alignment is a fundamental problem in computational structure biology and popular for protein 3D structural prediction and protein homology detection. Most of the developed programs for detecting protein sequence alignments are based upon the likelihood information of amino acids and are sensitive to alignment noises. We present a novel method PALM for modeling pairwise protein structure alignments, using the area distance to reduce the biological measurement noise. PALM generatively learn the alignment of two protein sequences with probabilistic area distance objective, which can denoise the measurement errors contained in the ground-truth alignments. During learning, we show that the optimization is computationally efficient by estimating the gradients via dynamically sampling alignments. Empirically, we show that PALM can generate sequence alignments with higher precision and recall, as well as smaller area distance than the competing methods especially for long protein sequences and remote homologies. This study implies for learning over large-scale protein sequence alignment problems, one could potentially give PALM a try.

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ICML Conference 2021 Conference Paper

XOR-CD: Linearly Convergent Constrained Structure Generation

Fan Ding
Jianzhu Ma
Jinbo Xu
Yexiang Xue

We propose XOR-Contrastive Divergence learning (XOR-CD), a provable approach for constrained structure generation, which remains difficult for state-of-the-art neural network and constraint reasoning approaches. XOR-CD harnesses XOR-Sampling to generate samples from the model distribution in CD learning and is guaranteed to generate valid structures. In addition, XOR-CD has a linear convergence rate towards the global maximum of the likelihood function within a vanishing constant in learning exponential family models. Constraint satisfaction enabled by XOR-CD also boosts its learning performance. Our real-world experiments on data-driven experimental design, dispatching route generation, and sequence-based protein homology detection demonstrate the superior performance of XOR-CD compared to baseline approaches in generating valid structures as well as capturing the inductive bias in the training set.

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UAI Conference 2021 Conference Paper

XOR-SGD: provable convex stochastic optimization for decision-making under uncertainty

Fan Ding
Yexiang Xue

Many decision-making problems under uncertainty can be formulated as convex stochastic optimization, which minimizes a convex objective in expectation across exponentially many probabilistic scenarios. Despite its convexity, evaluating the objective function is #P-hard. Previous approaches use samples from MCMC and its variants to approximate the objective function but have a slow mixing rate. We present XOR-SGD, a stochastic gradient descent (SGD) approach guaranteed to converge to solutions that are at most a constant away from the true optimum in linear number of iterations. XOR-SGD harnesses XOR-sampling, which reduces the sample approximation of the expectation into queries of NP oracles via hashing and projection. We evaluate XOR-SGD on two real-world applications. The first stochastic inventory management problem searches for a robust inventory management plan in preparation for the virus pandemics, natural disasters, etc. The second network design problem decides an optimal land conservation plan which promotes the free movement of wild-life animals. We show that our approach finds better solutions with drastically fewer samples needed compared to a couple of state-of-the-art solvers.

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ECAI Conference 2020 Conference Paper

Towards Efficient Discrete Integration via Adaptive Quantile Queries

Fan Ding
Hanjing Wang
Ashish Sabharwal
Yexiang Xue

Discrete integration in a high dimensional space of n variables poses fundamental challenges. The WISH algorithm reduces the intractable discrete integration problem into n optimization queries subject to randomized constraints, obtaining a constant approximation guarantee. The optimization queries are expensive, which limits the applicability of WISH. We propose AdaWISH, which is able to obtain the same guarantee, but accesses only a small subset of queries of WISH. For example, when the number of function values is bounded by a constant, AdaWISH issues only O(log n) queries. The key idea is to query adaptively, taking advantage of the shape of the weight function being integrated. In general, we prove that AdaWISH has a regret of only O(log n) relative to an idealistic oracle that issues queries at data-dependent optimal points. Experimentally, AdaWISH gives precise estimates for discrete integration problems, of the same quality as that of WISH and better than several competing approaches, on a variety of probabilistic inference benchmarks. At the same time, it saves substantially on the number of optimization queries compared to WISH. On a suite of UAI inference challenge benchmarks, it saves 81. 5% of WISH queries while retaining the quality of results.

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IJCAI Conference 2018 Conference Paper

Progressive Generative Hashing for Image Retrieval

Yuqing Ma
Yue He
Fan Ding
Sheng Hu
Jun Li
Xianglong Liu

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.

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