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Ben Liang

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

Online Capacitated General Matching with Knapsack

Ruoyu Wu
Wei Bao
Ben Liang
Hequn Wang

We study a new online matching problem termed Online Capacitated General Matching with Knapsack (OCGMK), which generalizes the Online General Matching (OGM) problem. In the original OGM, vertices arrive sequentially and need to be paired with other vertices to maximize the total reward of pairing. Our study is the first to consider capacitated vertices in OGM: we allow each vertex to be assigned to multiple vertices up to a capacity limit. We also consider a previously unexamined knapsack constraint in OGM: assigning a pair of vertices has a cost, but the total cost is budgeted. To solve the OCGMK problem, we propose the Online Capacity-Knapsack Assignment (OCKA) algorithm, which constructs capacity-friendly sets and knapsack-friendly sets to simultaneously and effectively address both constraints. OCKA achieves a competitive ratio of ⍺=?/2?, where?=1/(3+e^(-2)) and? is the ratio between the overall cost of all edges and the cost budget. When the knapsack constraint is not imposed but the capacitated vertices remain, the competitive ratio of OCKA is ⍺'=1/2, recovering the previous best result for single-capacity OGM. We implement trace-driven experiments to evaluate the practical performance of OCKA on a real-world dating dataset, demonstrating the superior performance of OCKA in online dating applications.

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NeurIPS Conference 2025 Conference Paper

Adaptive Sigmoid Clipping for Balancing the Direction–Magnitude Mismatch Trade-off in Differentially Private Learning

Faeze Moradi Kalarde
Ali Bereyhi
Ben Liang
Min Dong

Differential privacy (DP) limits the impact of individual training data samples by bounding their gradient norms through clipping. Conventional clipping operations assign unequal scaling factors to sample gradients with different norms, leading to a direction mismatch between the true batch gradient and the aggregation of the clipped gradients. Applying a smaller but identical scaling factor to all sample gradients alleviates this direction mismatch; however, it intensifies the magnitude mismatch by excessively reducing the aggregation norm. This work proposes a novel clipping method, termed adaptive sigmoid (AdaSig), which uses a sigmoid function with an adjustable saturation slope to clip the sample gradients. The slope is adaptively adjusted during the training process to balance the trade-off between direction mismatch and magnitude mismatch, as the statistics of sample gradients evolve over the training iterations. Despite AdaSig’s adaptive nature, our convergence analysis demonstrates that differentially private stochastic gradient descent (DP-SGD) with AdaSig clipping retains the best-known convergence rate under non-convex loss functions. Evaluating AdaSig on sentence and image classification tasks across different datasets shows that it consistently improves learning performance compared with established clipping methods.

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NeurIPS Conference 2025 Conference Paper

Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling

Shayan Mohajer Hamidi
Ben Liang
EN-HUI YANG

Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computational biology. Diffusion models have recently emerged as powerful priors for solving such problems. However, existing methods either rely on projection-based techniques that enforce measurement consistency through heuristic updates, or they approximate the likelihood $p(\boldsymbol{y} \mid \boldsymbol{x})$, often resulting in artifacts and instability under complex or high-noise conditions. To address these limitations, we propose a novel framework called coupled data and measurement space diffusion posterior sampling (C-DPS), which eliminates the need for constraint tuning or likelihood approximation. C-DPS introduces a forward stochastic process in the measurement space $\{\boldsymbol{y}_t\}$, evolving in parallel with the data-space diffusion $\{\boldsymbol{x}_t\}$, which enables the derivation of a closed-form posterior $p(\boldsymbol{x}_{t-1} \mid \boldsymbol{x}_t, \boldsymbol{y}_{t-1})$. This coupling allows for accurate and recursive sampling based on a well-defined posterior distribution. Empirical results demonstrate that C-DPS consistently outperforms existing baselines, both qualitatively and quantitatively, across multiple inverse problem benchmarks.

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JMLR Journal 2024 Journal Article

On the Generalization of Stochastic Gradient Descent with Momentum

Ali Ramezani-Kebrya
Kimon Antonakopoulos
Volkan Cevher
Ashish Khisti
Ben Liang

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. Then, for smooth Lipschitz loss functions, we analyze a modified momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes, and show that it can train machine learning models for multiple epochs with a guarantee for generalization. Finally, for the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes. Extending our results on generalization, we also develop an upper bound on the expected true risk, in terms of the number of training steps, sample size, and momentum. Our experimental evaluations verify the consistency between the numerical results and our theoretical bounds. SGDEM improves the generalization error of SGDM when training ResNet-18 on ImageNet in practical distributed settings. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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