The paper introduces Entropic Projection Alignment (EPA), a unified framework that estimates, explains, and improves model performance under distribution shift by aligning source and target distributi…

This paper studies the behavior of mixture-of-experts (MoE) models under distribution shift and proposes an adversarial reweighting method to improve their calibration.

This paper proposes CRISP, a model-agnostic framework for source-only medical image segmentation under distribution shift, which uses rank stability of positive regions to derive robust spatial priors…

This survey analyzes the field of On-device Learning (ODL) for TinyML by categorizing existing works based on how they address various types of post-deployment distribution changes.

This paper proposes SHIFT, a topology-agnostic approach for communication-aware workload placement and routing optimization in large-scale heterogeneous systems, achieving up to 12.5x throughput impro…

A simple online algorithm is presented for the stacking problem to avoid shifts with a sufficient condition involving stacking area dimension, load/unload points, and maximum items.

The paper introduces SHIFT, a training-free attack that exploits the vulnerability of diffusion-based watermarking by stochastically deflecting the generative trajectory, achieving high removal rates…

The paper proposes Fair Fine-tuning (FFt), a method that fine-tunes a model using an Equalized Odds constraint on a complementary distribution, and theoretically proves that this approach significantl…

The paper proposes Fair Fine-tuning (FFt), a method that fine-tunes a model using an Equalized Odds constraint on a complementary distribution, and provides a formal theoretical bound linking this fai…

The paper proposes DARTS, a distribution-aware active rollout trajectory shaping method that fundamentally accelerates LLM reinforcement learning by actively shaping the long-tail response distributio…

This paper introduces Distributional Random Forests, which replace mean-based CART splitting with criteria that compare full conditional response distributions in candidate children. The authors syste…

The paper introduces a parameterized implementation to analyze the effects of bias, shift, and rotation on the Marine Predators Algorithm using the CEC 2017 benchmark.

This paper studies distribution testing with respect to bounded classes of distinguishers, revealing connections between testable learning, verification of learning algorithms, and testing of structur…

The paper introduces Temporary Power Adjusting Withholding (T-PAW), a generalized and more potent block withholding attack than the existing PAW attack, demonstrating that this attack can yield signif…

The paper uses majorization theory to analyze lattice reduction, showing that local swaps smooth the Gram-Schmidt profile and deriving variational and telescoping identities for the worst-case profile…

This paper proposes a method for handling overparameterized linear regression using early-stopped negative-shifted gradient descent, which allows for smooth filters and mixed-sign capabilities.

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.