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CollaFuse: Collaborative Diffusion Models

- In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses...

Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs

- Bias and fairness risks in Large Language Models (LLMs) vary substantially across deployment contexts, yet existing approaches lack systematic guidance for selecting appropriate evaluation metrics....

Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms

- We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of their Frobenius norms and positive lineal entrywise...

Value Explicit Pretraining for Learning Transferable Representations

- Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose \textit{Value Explicit Pretraining} (VEP), a method...

Koopman-Assisted Reinforcement Learning

- The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theory. However, these equations become intractable for...

Mutatis Mutandis: Revisiting the Comparator in Discrimination Testing

- Testing for individual discrimination involves deriving a profile, the comparator, similar to the one making the discrimination claim, the complainant, based on a protected attribute, such as race or...

Non-norm criteria and optimal $2times 2$ space-time block codes over rings of integers of imaginary quadratic fields

- Codes arising from algebraic structures over number fields lead naturally to determinant optimization problems governed by arithmetic invariants. In this paper, we investigate $2\times 2$ space-time...

Decentralized Proximal Stochastic Gradient Langevin Dynamics

- We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Markov chain Monte Carlo (MCMC) algorithm for sampling from a log-concave probability distribution...

Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks

- Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) is a popular...

Optimal network structure for collective performance with strategic information sharing

- Information sharing between individuals is crucial to improve performance in collective tasks. However, in a competitive world, individuals may be reluctant to share information with the others, and...

Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks

- With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using...

Multi-frame Restoration for High-rate Lissajous Confocal Laser Endomicroscopy

- Lissajous confocal laser endomicroscopy (CLE) is a promising solution for high speed in vivo optical biopsy for handheld scenarios. However, Lissajous scanning traces a resonant trajectory and...

Linking PageRank, Time Reversal, and Policy Evaluation

- We establish a connection between policy evaluation in Markov decision processes and PageRank in network analysis. For a fixed policy, we show that the value function of a discounted Markov decision...

Gradient Regularized Newton Boosting Trees with Global Convergence

- Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being based on Newton boosting: a second-order descent...

Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion

- Deep Unfolding Network-based methods have emerged as effective solutions for multi-source image fusion by combining model-driven iterative optimization with data-driven deep learning. However, most...

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

- Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley...

Local Geometry of Least Squares for Unmixing Signals with Parameter-Dependent Dictionaries

- Modeling signals as linear combinations of atoms from a dictionary is ubiquitous in modern signal processing. In the finite-dimensional setting, whenever atoms depend nonlinearly upon unknown...

How to Do Statistical Evaluations in ECE/CS Papers: A Practical Playbook for Defensible Results

- Strong experimental papers in electrical and computer engineering and computer science (ECE/CS), especially in systems, networking, and applied machine learning, rest on more than a single impressive...

Adaptive Norm-Based Regularization for Neural Networks

- In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and...

FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting

- Reliable parameter extraction from experimental data is central to quantitative analysis in spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved measurements....

SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

- Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel...