data science

CPATTA: Conformal Supervision Allocation For Active Test-Time Adaptation

Conformal Prediction Active TTA is proposed, which first brings principled, conformal uncertainty with coverage-aware online calibration into ATTA, and consistently outperforms the state-of-the-art ATTA methods by around 5% in accuracy.

Causal pieces: analysing and improving spiking neural networks piece by piece

We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike coding into distinct regi...

RLTL;DR: Self-improvement by Internalizing Self-generated Feedback

RLTL;DR is introduced, a promising compacted training paradigm of the form"on this sort of task, keep this sort of thing in mind", which it is hoped will inspire future research on.

A Sharp Transition in Data Reconstruction under Differential Privacy

Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivating defenses with guarantees that remain valid against future threats. While differential privacy (DP) provides formal protection, choosing the privacy budget remains a ch...

Identifying ODEs from Unstructured Data with Causal Representation Learning

We study the problem of recovering the governing ODE of a dynamical system from unstructured, high-dimensional observations such as images. Existing methods for ODE discovery typically assume direct measurements of the variables, or do not provide theoretical guarantees on the learned variables and equations. While Cau...

Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models

Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a system by comparing anomalous observations with corresponding reference (i.e., regular) observations. However, existing approaches rely either on heuristic methods or on cond...

Second-Moment Stochastic Approximation Methods

This work derives second-moment stochastic approximation methods through the lens of optimal preconditioning for solving matrix equations, and develops a two-stage framework for their convergence analysis.

MoRE: Scaling mixture of experts with hardware-aware low-rank routing

Mixture-of-Experts (MoE) layers are central to frontier language models, and recent architectures push toward more and smaller experts. In this regime, the standard linear router becomes a bottleneck: with $M$ experts and hidden dimension $h$, its per-token cost $\Theta(Mh)$ dominates the MoE layer once $M$ is large. W...

Intrinsic Associative Memory on Riemannian Manifolds: Curvature, Capacity, and Emergent Modes

Geometry does more than constrain an associative memory: curvature determines what it remembers and which states it creates. We develop intrinsic dense associative memories on Riemannian manifolds by casting memory as Epanechnikov kernel-density mode seeking. We compare geodesic and volume-corrected energies and show t...

XU-RS: Explaining Credal Width in Random-Set Language Models

Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot tell whether that uncertainty score depends on input features that are relevant for the task. We study this problem in randomset classifiers built using pretrained langua...

OTROPE: Optimal Transport-based Robust Off-policy Evaluation for Large Language Models

The Optimal Transport-based Robust Off-Policy Evaluation (OTROPE) is proposed, a likelihood-free evaluation that performs distributional correction in a semantic space via optimal transport to align labeled behavior-policy samples with unlabeled target-policy samples, yielding a doubly robust-style evaluation.

Examining Issues in Translating Qualitative Data in Cross-Cultural Health Science Research Conducted in Malaysia: A Systematic Review