Work done while at University of Cambridge. The author is at Waymo now.

Neural Information Processing Systems http://nips.cc/

We thank the reviewers for their close reading, detailed comments, and overall positive assessment. We will improve the flow and formatting of the paper, and fix the references in the final version. As we can see, ADE consistently achieves comparable or the best performance. We are exploring alternative sampling algorithm embeddings, e.g., ADE limitations and how to overcome. See Appendix C for details. ADE, then the parameter tuning requirements for ADE and GANs are comparable, i.e., we tune the inner optimization Re: "[the authors] further conduct T vanilla HMC steps to approximately solve it."

Recent advances have shown that sequential fine-tuning (SeqFT) of pre-trained vision transformers (ViTs), followed by classifier refinement using approximate distributions of class features, can be an effective strategy for class-incremental learning (CIL). However, this approach is susceptible to distribution drift, caused by the sequential optimization of shared backbone parameters. This results in a mismatch between the distributions of the previously learned classes and that of the updater model, ultimately degrading the effectiveness of classifier performance over time. To address this issue, we introduce a latent space transition operator and propose Sequential Learning with Drift Compensation (SLDC). SLDC aims to align feature distributions across tasks to mitigate the impact of drift. First, we present a linear variant of SLDC, which learns a linear operator by solving a regularized least-squares problem that maps features before and after fine-tuning. Next, we extend this with a weakly nonlinear SLDC variant, which assumes that the ideal transition operator lies between purely linear and fully nonlinear transformations. This is implemented using learnable, weakly nonlinear mappings that balance flexibility and generalization. To further reduce representation drift, we apply knowledge distillation (KD) in both algorithmic variants. Extensive experiments on standard CIL benchmarks demonstrate that SLDC significantly improves the performance of SeqFT. Notably, by combining KD to address representation drift with SLDC to compensate distribution drift, SeqFT achieves performance comparable to joint training across all evaluated datasets. Code: https://github.com/raoxuan98-hash/sldc.git.

Abstract-- We challenge the long-standing assumption that exhaustive scene modeling is required for high-performance end-to-end autonomous driving (E2EAD). Inspired by cognitive science, we propose that effective planning arises not from reconstructing the world, but from the co-evolution of belief and intent within a minimal set of semantically rich tokens. Experiments on the nuPlan benchmark (720 scenarios, 11k+ samples) reveal three principles: (1) sparse intent tokens alone achieve 0.487 m ADE, demonstrating strong performance without future prediction; (2) conditioning trajectory decoding on predicted future tokens reduces ADE to 0.382 m, a 21.6% improvement, showing that performance emerges from cognitive planning; and (3) explicit reconstruction loss degrades performance, confirming that task-driven belief-intent co-evolution suffices under reliable perception inputs. Crucially, we observe the emergence of cognitive consistency: through prolonged training, the model spontaneously develops stable token dynamics that balance current perception (belief) and future goals (intent). This process, accompanied by "temporal fuzziness," enables robustness under uncertainty and continuous self-optimization. Our work establishes a new paradigm: intelligence lies not in pixel fidelity, but in the tokenized duality of belief and intent. Note: Numerical comparisons with methods reporting results on nuScenes are indicative only, as nuPlan presents a more challenging planning-focused evaluation.

An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequential dependencies, resulting in enhanced defect prediction accuracy. ADE optimization enhances model convergence and predictive performance. ADE-QVAET integrates AI-ML techniques such as tuning hyperparameters for scalable and accurate software defect prediction, representing an AI-ML-driven technology for quality engineering. During training with a 90% training percentage, ADE-QVAET achieves high accuracy, precision, recall, and F1-score of 98.08%, 92.45%, 94.67%, and 98.12%, respectively, when compared to the Differential Evolution (DE) ML model.

Work done while at University of Cambridge. The author is at Waymo now.

Can a simple yet effective algorithm be developed for estimating general exponential family distributions? There has been extensive prior work addressing this question.

We thank the reviewers for their close reading, detailed comments, and overall positive assessment. We will improve the flow and formatting of the paper, and fix the references in the final version. As we can see, ADE consistently achieves comparable or the best performance. We are exploring alternative sampling algorithm embeddings, e.g., ADE limitations and how to overcome. See Appendix C for details. ADE, then the parameter tuning requirements for ADE and GANs are comparable, i.e., we tune the inner optimization Re: "[the authors] further conduct T vanilla HMC steps to approximately solve it."

Modern machine learning systems operating in dynamic environments often face \textit{sequential covariate shift} (SCS), where input distributions evolve over time while the conditional distribution remains stable. We introduce FADE (Fisher-based Adaptation to Dynamic Environments), a lightweight and theoretically grounded framework for robust learning under SCS. FADE employs a shift-aware regularization mechanism anchored in Fisher information geometry, guiding adaptation by modulating parameter updates based on sensitivity and stability. To detect significant distribution changes, we propose a Cramer-Rao-informed shift signal that integrates KL divergence with temporal Fisher dynamics. Unlike prior methods requiring task boundaries, target supervision, or experience replay, FADE operates online with fixed memory and no access to target labels. Evaluated on seven benchmarks spanning vision, language, and tabular data, FADE achieves up to 19\% higher accuracy under severe shifts, outperforming methods such as TENT and DIW. FADE also generalizes naturally to federated learning by treating heterogeneous clients as temporally fragmented environments, enabling scalable and stable adaptation in decentralized settings. Theoretical analysis guarantees bounded regret and parameter consistency, while empirical results demonstrate FADE's robustness across modalities and shift intensities.