NeurIPS 2026 Papers

Neural networks are expressive predictors capable of modeling rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions—a critical requirement in safety-critical domains. Randomized smoothing is a leading technique for improving robustness, particularly against adversarial perturbations. Yet, in stochastic multi-modal regression settings, randomized smoothing often fails due to mode collapse, yielding averaged predictions that do not reflect the underlying distribution. To address this limitation, we propose clustered $\alpha$-smoothing, a framework that (1) partitions noisy samples using an arbitrary clustering algorithm, (2) applies $\alpha$-smoothing locally within each cluster, and (3) combines the resulting predictions into a mixture distribution. By interpreting the smoothing distribution as a mixture of $\alpha$-smoothers, we derive a lower bound on the probability that the smoothed prediction lies within a union of compact regions corresponding to distinct modes. We empirically evaluate our framework on two benchmarks, demonstrating substantial improvements over state-of-the-art methods. In stochastic trajectory prediction on a driving simulator dataset, our approach achieves, on average, a 33\% lower Wasserstein distance to the ground-truth distribution compared to $\alpha$-smoothing. In quadrotor control, where modes correspond to distinct feasible paths to a target, our method reduces the collision rate by 81\% relative to the state-of-the-art randomized smoothing.

Offline RL methods often regularize toward the empirical occupancy of the dataset, but this reference can mismatch the deployed policy. In partially observed or decentralized settings, data may be pooled from hidden modes, histories, or conventions that are unavailable at execution time. The pooled occupancy can then require incompatible action laws for the same deployed input, or joint correlations that a decentralized policy class cannot represent, so regularization toward it can favor a low return projection. We formalize this failure through *reference coherence*: a reference is coherent when it does not rely on information hidden from the deployed policy, and when its action law is representable by that policy. We propose *Closest Slice DICE (CS-DICE)*, a DICE variant that regularizes toward a coherent slice of the training data rather than the pooled occupancy. A hard or soft selector chooses the slice used as the reference term, while the deployed observations, actor class, and factorization constraints remain unchanged. Most of the results focus on the soft reverse-$KL$ case, where the selector only changes the reference used by an otherwise standard DICE update. Controlled examples show that incoherent pooled references collapse to suboptimal behavior, while CS-DICE recovers high return policies when the selected or inferred slice is coherent with the deployed interface. We also identify a boundary case where pooling is benign because the relevant signal is revealed before the conflict. Finally, a CS-CoMA-DICE study on MaMuJoCo shows that selected references can be integrated into a neural cooperative MARL pipeline while preserving decentralized execution.

Owing to enhanced capabilities to capture a broad range of higher-order interactions, simplicial neural networks (SNNs) have emerged as a new powerful methodology for graph learning. However, prevailing SNNs are often limited in efficient exploration of heterogeneous graphs, thereby restricting the SNN utility on downstream tasks. To address this challenge, we introduce the concept of L\'{e}vy flights over simplices, offering a new efficient alternative to learning higher-order graph (sub)structures under heterogeneous scenarios. Specifically, we develop a new Fractional Hodge Laplacian Simplicial Neural Network (FHL-SNN), a novel approach leveraging fractional powers of the Hodge-Laplacian and allowing for a more efficient exploration of the underlying higher-order graph organization in conjunction with the link prediction. We establish a theoretical connection between a fractional diffusion process and graph exploration over simplices. In particular, we prove that the L\'{e}vy flight over simplices yields a lower mixing time than that of its non-fractional counterpart. Our extensive experiments on both directed and undirected link prediction tasks illuminate the power of non-local search of graph space via fractional dynamics, yielding relative gains of up to 9\%. Finally, we show that the idea of L\'{e}vy flights over simplices is versatile and that the integration of fractional dynamics to existing SNNs has the potential to further boost the model performance.

Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as it can model holistic 3D spaces by encoding geometric occupancy along with semantic categories. However, existing occupancy prediction methods struggle to meet practical deployment requirements, such as adapting to varying computing budgets, sensor setups, and observation views. In this paper, we propose a point-based Adaptive 3D Occupancy Prediction method, called AdaOcc, tailored for embodied scenarios. To accommodate heterogeneous sensor inputs, AdaOcc uses an adaptive geometry-guided dual-branch encoder that can support RGB images in various numbers of views with (estimated) depth maps or LiDAR scans. AdaOcc represents occupied regions via sparse semantic points trained with a progressive query learning strategy, allowing the prediction computational budget to be flexibly adjusted through query point numbers and decoder layers. To facilitate high-fidelity geometric modeling for lightweight point-based occupancy learning, we further propose a novel containment loss that regularizes predicted points to reside within valid occupied regions. Extensive experiments show that our method achieves a new state-of-the-art on Occ-ScanNet with considerable performance improvements over previous methods. Moreover, our framework demonstrates strong practical applicability as an adaptive 3D perception module in real-world embodied systems.

Predictive models have the potential to transform drug development by increasing success rates across all stages of development pipelines. Evaluating this potential needs to include the whole pipeline, from initial screens of thousands of compounds to the expensive assays, such as toxicity assessment, focused on a few compounds in the later stages. Genetic medicines, such as antisense oligonucleotides (ASOs), could benefit millions affected by individually rare genetic disorders, if economic limitations imposed by the need to screen hundreds of sequences at considerable expense can be overcome. To enable systematic analysis of this problem, we present ASO Atlas 2.0, the largest public multi-endpoint ASO preclinical dataset, spanning 295,007 data points, including both in vitro efficacy and in vivo toxicology, across 430. Using this resource, we introduce a cost-based evaluation framework that translates per-stage classifier enrichment into projected cost savings under a representative preclinical pipeline model. We find that incorporating model predictions across all stages reduces projected pipeline cost by 71%. This saving is largely attributable to modest enrichment at costly late-stage in vivo studies rather than strong enrichment at lower-cost in vitro assays, reversing the model ranking suggested by accuracy alone and highlighting the need for improved toxicology modelling.

Multivariate conformal prediction requires nonconformity scores that compress residual vectors into scalars while preserving certain implicit geometric structure of the residual distribution. We introduce a Multivariate Kernel Score (MKS) that produces prediction regions that explicitly adapt to this geometry. We show that the proposed score resembles the Gaussian process posterior variance, unifying Bayesian uncertainty quantification with the coverage guarantees of frequentist-type. Moreover, the MKS can be decomposed into an anisotropic Maximum Mean Discrepancy (MMD) that interpolates between kernel density estimation and covariance-weighted distance. We prove finite-sample coverage guarantees and establish convergence rates that depend on the effective rank of the kernel-based covariance operator rather than the ambient dimension, enabling dimension-free adaptation. On regression tasks, the MKS reduces the volume of prediction regions significantly, compared to ellipsoidal baselines while maintaining nominal coverage, with larger gains at higher dimensions and tighter coverage levels.