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Ke Jiang

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6 papers

1 author row

TMLR Journal 2026 Journal Article

Dynamics‑Aligned Diffusion Planning for Offline RL: A Unified Framework with Forward and Inverse Guidance

Zihao Wang
Ke Jiang
Xiaoyang Tan

Diffusion-based planning has emerged as a powerful paradigm for offline reinforcement learning (RL). However, existing approaches often overlook the physical constraints imposed by real-world dynamics, resulting in dynamics inconsistency—a mismatch between diffusion-generated trajectories and those feasible under true environment transitions. To address this issue, we propose Dynamics-Aligned Diffusion Planning (DADP), a unified framework that explicitly enforces dynamics consistency during the diffusion denoising process. DADP offers two complementary variants: DADP-F (Forward), which employs a forward dynamics model to ensure state-level feasibility, and DADP-I (Inverse), which leverages an inverse dynamics model to enhance action-level executability. Both variants share a unified guidance formulation that integrates task return optimization and dynamics alignment through gradient-based updates. Experiments on state-based D4RL Maze2D and MuJoCo benchmarks demonstrate that DADP-F and DADP-I outperform state-of-the-art offline RL baselines, effectively reducing dynamics inconsistency and improving long-horizon robustness. This unifies diffusion-based planning with physically grounded dynamics modeling.

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AAAI Conference 2026 Conference Paper

Variational OOD State Correction for Offline Reinforcement Learning

Ke Jiang
Wen Jiang
Xiaoyang Tan

The performance of Offline reinforcement learning is significantly impacted by the issue of state distributional shift, and out-of-distribution (OOD) state correction is a popular approach to address this problem. However, previous methods correct the agent's transition distributions in a supervised way, which significantly degrades the flexibility and robustness. In this paper, we propose a novel method named Density-Aware Safety Perception (DASP) for OOD state correction. Specifically, our method encourages the agent to prioritize actions that lead to outcomes with higher data density, thereby promoting its operation within or the return to in-distribution (safe) regions. To achieve this, we optimize the objective within a variational framework that concurrently considers both the potential outcomes of decision-making and their density, thus providing crucial contextual information for safe decision-making. Finally, we validate the effectiveness and feasibility of our proposed method through extensive experimental evaluations on the offline MuJoCo and AntMaze suites.

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NeurIPS Conference 2023 Conference Paper

Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning

Ke Jiang
Jia-Yu Yao
Xiaoyang Tan

In this paper we deal with the state distributional shift problem commonly encountered in offline reinforcement learning during test, where the agent tends to take unreliable actions at out-of-sample (unseen) states. Our idea is to encourage the agent to follow the so called state recovery principle when taking actions, i. e. , besides long-term return, the immediate consequences of the current action should also be taken into account and those capable of recovering the state distribution of the behavior policy are preferred. For this purpose, an inverse dynamics model is learned and employed to guide the state recovery behavior of the new policy. Theoretically, we show that the proposed method helps aligning the transited state distribution of the new policy with the offline dataset at out-of-sample states, without the need of explicitly predicting the transited state distribution, which is usually difficult in high-dimensional and complicated environments. The effectiveness and feasibility of the proposed method is demonstrated with the state-of-the-art performance on the general offline RL benchmarks.

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JBHI Journal 2021 Journal Article

3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection

Jian Zheng
Haotian Sun
Shandong Wu
Ke Jiang
Yunsong Peng
Xiaodong Yang
Fan Zhang
Ming Li

False positives (FPs) reduction is indispensable for clustered microcalcifications (MCs) detection in digital breast tomosynthesis (DBT), since there might be excessive false candidates in the detection stage. Considering that DBT volume has an anisotropic resolution, we proposed a novel 3D context-aware convolutional neural network (CNN) to reduce FPs, which consists of a 2D intra-slices feature extraction branch and a 3D inter-slice features fusion branch. In particular, 3D anisotropic convolutions were designed to learn representations from DBT volumes and inter-slice information fusion is only performed on the feature map level, which could avoid the influence of anisotropic resolution of DBT volume. The proposed method was evaluated on a large-scale Chinese women population of 877 cases with 1754 DBT volumes and compared with 8 related methods. Experimental results show that the proposed network achieved the best performance with an accuracy of 92. 68% for FPs reduction with an AUC of 97. 65%, and the FPs are 0. 0512 per DBT volume at a sensitivity of 90%. This also proved that making full use of 3D contextual information of DBT volume can improve the performance of the classification algorithm.

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NeurIPS Conference 2013 Conference Paper

Small-Variance Asymptotics for Hidden Markov Models

Anirban Roychowdhury
Ke Jiang
Brian Kulis

Small-variance asymptotics provide an emerging technique for obtaining scalable combinatorial algorithms from rich probabilistic models. We present a small-variance asymptotic analysis of the Hidden Markov Model and its infinite-state Bayesian nonparametric extension. Starting with the standard HMM, we first derive a “hard” inference algorithm analogous to k-means that arises when particular variances in the model tend to zero. This analysis is then extended to the Bayesian nonparametric case, yielding a simple, scalable, and flexible algorithm for discrete-state sequence data with a non-fixed number of states. We also derive the corresponding combinatorial objective functions arising from our analysis, which involve a k-means-like term along with penalties based on state transitions and the number of states. A key property of such algorithms is that — particularly in the nonparametric setting — standard probabilistic inference algorithms lack scalability and are heavily dependent on good initialization. A number of results on synthetic and real data sets demonstrate the advantages of the proposed framework.

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NeurIPS Conference 2012 Conference Paper

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models

Ke Jiang
Brian Kulis
Michael Jordan

Links between probabilistic and non-probabilistic learning algorithms can arise by performing small-variance asymptotics, i. e. , letting the variance of particular distributions in a graphical model go to zero. For instance, in the context of clustering, such an approach yields precise connections between the k-means and EM algorithms. In this paper, we explore small-variance asymptotics for exponential family Dirichlet process (DP) and hierarchical Dirichlet process (HDP) mixture models. Utilizing connections between exponential family distributions and Bregman divergences, we derive novel clustering algorithms from the asymptotic limit of the DP and HDP mixtures that feature the scalability of existing hard clustering methods as well as the flexibility of Bayesian nonparametric models. We focus on special cases of our analysis for discrete-data problems, including topic modeling, and we demonstrate the utility of our results by applying variants of our algorithms to problems arising in vision and document analysis.

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