Author

Esha Singh

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#machine learning Preprint Sep 2026

Learning Where to Steer: Noise-Space Geometry for Efficient Offline Multi-Objective Optimization with Generative Models

Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives. Diffusion models trained on such data have emerged as a promising approach, but their samples are not inherently better than the data and must be steered toward the Pa...

Yuan-Tong Lu, Esha Singh, Yi-An Ma et al. · 0 citations

#machine learning Preprint Sep 2026

Residual-Stream Burden Shapes Representation Learning in Diffusion Transformers

In diffusion-based generation, a neural network can be trained to predict the clean data, the noise, or the velocity from a noisy input, yet plain Diffusion Transformers operating on large pixel patches succeed with clean prediction and fail with noise or velocity prediction.

Tong-Tong Liang, Si-Qi Kou, Zi-Qiao Xi et al. · 0 citations

Open access Aug 2026

On the Robustness of Biomolecular Systems to Perturbations in Translational Resources

It is shown that the constitutive circuit exhibits sensitivity to resource perturbations, and that redesigning it with negative autoregulatory feedback enhances the contraction rate and reduces the steady-state deviation bound, though at lower expression levels.

Akshay Kumar Jaiswal, Esha Singh, Abhilash Patel et al. · bioRxiv · 0 citations