GenAI4ELab/papercli-papers huggingface dataset:
Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
Authors: Zohar Rimon; Elisei Shafer; Tal Tepper; Efrat Shimron; Aviv Tamar
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a **representation** from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces -- the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.
Contrastive Learning with Data Misalignment: Feature Purity, Training Dynamics and Theoretical Generalization Guarantees
Authors: Jiawei Sun; Shuai Zhang; Hongkang Li; Meng Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Contrastive learning is a powerful framework for learning discriminative representations from image-text pairs. 
Despite its success, its theoretical foundations, especially when the image-text pair exhibits misalignment, remain underexplored. 
This paper provides the first theoretical analysis of contrastive learning under data misalignment, proving how the ground-truth modality-paired features are amplified while spurious features are suppressed through the training dynamics analysis. Specifically, we study two nonlinear encoders trained jointly with a contrastive loss and demonstrate that noisy (or misaligned) data pairs result in mixed representations and degrade the model's generalization ability. In contrast, recaptioning and filtering improve the data alignment, which in turn purifies the features learned by neurons and subsequently enhances generalization. Our analysis identifies feature purity as a key factor in the success of contrastive learning and offers insights into how data quality and training procedures impact representation learning and downstream generalization. Theoretical insights are  supported by experiments on standard benchmarks.
Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage
Authors: Ziqi Yuan; Haoyang Zhang; Yirui Eric Zhou; Apoorve Mohan; I-Hsin Chung; Seetharami Seelam; Jian Huang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We present the design and implementation of a new lifetime-aware tensor offloading
framework for GPU memory expansion using low-cost PCIe-based solid-state
drives (SSDs). Our framework, TERAIO, is developed explicitly for large language
model (LLM) training with multiple GPUs and multiple SSDs. Its design is driven
by our observation that the active tensors take only a small fraction (1.7% on
average) of allocated GPU memory in each LLM training iteration, the inactive
tensors are usually large and will not be used for a long period of time, creating
ample opportunities for offloading/prefetching tensors to/from slow SSDs without
stalling the GPU training process. TERAIO accurately estimates the lifetime (active
period of time in GPU memory) of each tensor with the profiling of the first few
iterations in the training process. With the tensor lifetime analysis, TERAIO will
generate an optimized tensor offloading/prefetching plan and integrate it into the
compiled LLM program via PyTorch. TERAIO has a runtime tensor migration
engine to execute the offloading/prefetching plan via GPUDirect storage, which
allows direct tensor migration between GPUs and SSDs for alleviating the CPU
bottleneck and maximizing the SSD bandwidth utilization. In comparison with
state-of-the-art studies such as ZeRO-Offload and ZeRO-Infinity, we show that
TERAIO improves the training performance of various LLMs by 1.47× on average,
and achieves 80.7% of the ideal performance assuming unlimited GPU memory.
Cross-fluctuation phase transitions reveal sampling dynamics in diffusion models
Authors: Sai Niranjan Ramachandran; Manish Krishan Lal; Suvrit Sra
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using \emph{cross-fluctuations}, a centered-moment statistic from statistical physics. Specifically, we show that starting from an unbiased isotropic normal distribution, samples undergo sharp, discrete transitions, eventually forming distinct events of a desired distribution while progressively revealing finer structure. As this process is reversible, these transitions also occur in reverse, where intermediate states progressively merge, tracing a path back to the initial distribution.  We demonstrate that these transitions can be detected as discontinuities in $n^{\text{th}}$-order cross-fluctuations. For variance-preserving SDEs, we derive a closed-form for these cross-fluctuations that is efficiently computable for the reverse trajectory. %, thus tying cross-fluctuation dynamics to event formation within the desired distribution. 
We find that detecting these transitions directly boosts sampling efficiency, accelerates class-conditional and rare-class generation, and improves two zero-shot tasks--image classification and style transfer--without expensive grid search or retraining. We also show that this viewpoint unifies classical coupling and mixing from finite Markov chains with continuous dynamics while extending to stochastic SDEs and non Markovian samplers. Our framework therefore bridges discrete Markov chain theory, phase analysis, and modern generative modeling.
Smoothed Differentiation Efficiently Mitigates Shattered Gradients in Explanations
Authors: Adrian Hill; Neal McKee; Johannes Maeß; Stefan Bluecher; Klaus Robert Muller
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Explaining complex machine learning models is a fundamental challenge when developing safe and trustworthy deep learning applications. To date, a broad selection of explainable AI (XAI) algorithms exist. One popular choice is SmoothGrad, which has been conceived to alleviate the well-known shattered gradient problem by smoothing gradients through convolution. SmoothGrad proposes to solve this high-dimensional convolution integral by sampling -- typically approximating the convolution with limited precision. Higher numbers of samples would amount to higher precision in approximating the convolution but also to higher computing demand, therefore in practice only few samples are used in SmoothGrad. In this work we propose a well founded novel method _SmoothDiff_ to resolve this tradeoff yielding a _speedup of over two orders of magnitude_. Specifically, _SmoothDiff_ leverages automatic differentiation to decompose the expected values of Jacobians across a network architecture, directly targeting only the non-linearities responsible for shattered gradients and making it easy to implement. We demonstrate SmoothDiff's excellent speed and performance in a number of experiments and benchmarks. Thus, SmoothDiff greatly enhances the usability (quality and speed) of SmoothGrad -- a popular workhorse of XAI.
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-based Decoding
Authors: Xiner Li; Yulai Zhao; Chenyu Wang; Gabriele Scalia; Gökcen Eraslan; Surag Nair; Tommaso Biancalani; Shuiwang Ji; Aviv Regev; Sergey Levine; Masatoshi Uehara
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Existing methods for achieving this goal often require differentiable proxy models (e.g., classifier guidance or DPS) or involve computationally expensive fine-tuning of diffusion models (e.g., classifier-free guidance, RL-based fine-tuning). In our work, we propose a new method to address these challenges. Our algorithm is an iterative sampling method that integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future, into the standard inference procedure of pre-trained diffusion models. Notably, our approach avoids fine-tuning generative models and eliminates the need to construct differentiable models. This enables us to (1) directly utilize non-differentiable features/reward feedback, commonly used in many scientific domains, and (2) apply our method to recent discrete diffusion models in a principled way. Finally, we demonstrate the effectiveness of our algorithm across several domains, including image generation, molecule generation, and DNA/RNA sequence generation.
Diversity Is All You Need for Contrastive Learning: Spectral Bounds on Gradient Magnitudes
Authors: Peter Ochieng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Contrastive learning thrives—or fails—based on how we construct \emph{positive} and \emph{negative} pairs. In the absence of explicit labels, models must infer semantic structure from these proxy signals. Early work on Siamese networks \citep{chopra2005learning,hadsell2006dimensionality} already showed that pair construction directly shapes learned representations. In modern contrastive frameworks, poor pair selection remains a primary failure mode: it either causes collapse, where all embeddings converge to a point, or wastes the representational capacity of the space \citep{chen2020simple,tian2020makes,khosla2020supervised}.

Contemporary methods typically generate positives via semantic-preserving augmentations (crop, jitter, view transform), while negatives are drawn from other elements in the mini-batch under the assumption that different images are semantically dissimilar. But this assumption breaks down in fine-grained, low-diversity, or high-resolution settings \citep{kalantidis2020hard,robinson2020contrastive,chuang2020debiased}, motivating techniques such as hard-negative mining and debiased losses \citep{bachman2019learning,tian2020makes}.

\paragraph{Beyond pairs: batch-level diversity.} 
While most prior work focuses on \emph{which} individual negatives to select, we study the geometry of the entire batch. Our central observation is this: the overall \emph{diversity} of the batch embedding space strongly governs both training dynamics and representational quality. If diversity is too low, the model sees nearly identical negatives and gradients vanish—leading to collapse. If diversity is too high, negatives become almost orthogonal, but the resulting gradients shrink in magnitude, and learning slows. Optimal training thus occurs within a \emph{moderate diversity window}: high enough to avoid collapse, low enough to preserve update strength.
Sequentially Auditing Differential Privacy
Authors: Tomás González; Mateo Dulce Rubio; Aaditya Ramdas; Mónica Ribero
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We propose a practical sequential test for auditing differential privacy guarantees of black-box mechanisms. The test processes streams of mechanisms' outputs providing anytime-valid inference while controlling Type I error, overcoming the fixed sample size limitation of previous batch auditing methods. Experiments show this test detects violations with sample sizes that are orders of magnitude smaller than existing methods, reducing this number from 50K to a few hundred examples, across diverse realistic mechanisms. Notably, it identifies DP-SGD privacy violations in \textit{under} one training run, unlike prior methods needing full model training.
Stability and Oracle Inequalities for Optimal Transport Maps between General Distributions
Authors: Shubo Li; Yizhe Ding; Lingzhou Xue; Runze Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Optimal transport (OT) provides a powerful framework for comparing and transforming probability distributions, with wide applications in generative modeling, AI4Science and statistical inference. However, existing estimation theory typically requires stringent smoothness conditions on the underlying Brenier potentials and assumes bounded distribution supports, limiting practical applicability. In this paper, we introduce a unified theoretical framework for semi-dual OT map estimation that relaxes both of these restrictions. Building on sieved convex conjugate, our framework has two key contributions: (i) a new map stability bounds that holds without any second-order regularity assumptions on the true Brenier potentials, and (ii) an oracle inequality that cleanly decomposes the estimation error into statistical error, sieved bias, and approximation error. Specifically, our approximation error is measured in the $L^\infty$ norm rather than Sobolev norm in the existing results, aligning more naturally with classical approximation theory. Leveraging these tools, we provide statistical error of semi-dual estimators with mild and verifiable conditions on the true OT map. Moreover, we establish the first theoretical guarantee for deep neural network OT map estimator between general distributions, with Tanh network function class as an example.
Linear Attention for Efficient Bidirectional Sequence Modeling
Authors: Arshia Afzal; Elias Abad Rocamora; Leyla Naz Candogan; Pol Puigdemont; Francesco Tonin; Yongtao Wu; Mahsa Shoaran; Volkan Cevher
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Linear Transformers and State Space Models have emerged as efficient alternatives
to softmax Transformers for causal sequence modeling, enabling parallel training
via matrix multiplication and efficient RNN-style inference. However, despite their
success in causal tasks, no unified framework exists for applying Linear Transformers to bidirectional sequence modeling. We introduce LION, the first framework to
systematically extend Linear Transformers to the bidirectional setting. LION generalizes three core representations commonly used in the causal case—full Linear
Attention , bidirectional RNN, and chunkwise parallel form—to the bidirectional
setting. These forms are theoretically equivalent and enable models to exploit the
strengths of each during training and inference. We prove that a broad class of
Linear Transformers can be extended using LION and validate our framework via
three core examples based on the choice of decay type: LION-LIT, the bidirectional
extension of [25]; LION-D, based on [44]; and LION-S, a variant using selective
decay [34, 13]. Across standard bidirectional tasks, LION enables models to match
or exceed the performance of softmax Transformers, while offering significantly
faster training and more efficient inference than existing State Space Models.
ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
Authors: Timo Kaufmann; Yannick Metz; Daniel A. Keim; Eyke Hüllermeier
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the *direction* of a preference. A person may choose apples over oranges and bananas over grapes, but *which preference is stronger*? Strength is crucial for decision-making under uncertainty and generalization of preference models, but hard to measure reliably. Metadata such as response times and inter-annotator agreement can serve as proxies for strength, but are often noisy and confounded. We propose ResponseRank to address the challenge of learning from noisy strength signals. Our method uses relative differences in proxy signals to *rank responses to pairwise comparisons by their inferred preference strength*. To control for systemic variation, we compare signals only locally within carefully constructed strata. This enables robust learning of utility differences consistent with strength-derived rankings while making minimal assumptions about the strength signal. Our contributions are threefold: (1) ResponseRank, a novel method that robustly learns preference strength by leveraging locally valid relative strength signals; (2) empirical evidence of improved sample efficiency and robustness across diverse tasks: synthetic preference learning (with simulated response times), language modeling (with annotator agreement), and RL control tasks (with simulated episode returns); and (3) the *Pearson Distance Correlation (PDC)*, a novel metric that isolates cardinal utility learning from ordinal accuracy.
Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
Authors: Ezgi Korkmaz
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, agents have gained the ability to make sequential decisions in complex MDPs. Yet, reinforcement learning policies face exponentially growing state spaces in high dimensional MDPs resulting in a dichotomy between computational complexity and policy success. In our paper we focus on the agent’s interaction with the environment in a high-dimensional MDP during the learning phase and we introduce a theoretically-founded novel paradigm based on experiences obtained through counteractive actions. Our analysis and method provide a theoretical basis for efficient, effective, scalable and accelerated learning, and further comes with zero additional computational complexity while leading to significant acceleration in training. We conduct extensive experiments in the Arcade Learning Environment with high-dimensional state representation MDPs. The experimental results further verify our theoretical analysis, and our method achieves significant performance increase with substantial sample-efficiency in high-dimensional environments.
Smoothed Agnostic Learning of Halfspaces over the Hypercube
Authors: Yiwen Kou; Raghu Meka
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Agnostic learning of Boolean halfspaces is a fundamental problem in computational learning theory, but it is known to be computationally hard even for weak learning. Recent work \citep{chandrasekaran2024smoothed} proposed smoothed analysis as a way to bypass such hardness, but existing frameworks rely on additive Gaussian perturbations, making them unsuitable for discrete domains. We introduce a new smoothed agnostic learning framework for Boolean inputs, where perturbations are modeled via random bit flips. This defines a natural discrete analogue of smoothed optimality generalizing the Gaussian case. Under strictly subexponential assumptions on the input distribution, we give an efficient algorithm for learning halfspaces in this model, with runtime and sample complexity $\tilde{O}(n^{\mathrm{poly}(\frac{1}{\sigma\epsilon})})$. Previously, such algorithms were known only with strong structural assumptions for the discrete hypercube—for example, independent coordinates or symmetric distributions. Our result provides the first computationally efficient guarantee for smoothed agnostic learning of halfspaces over the Boolean hypercube, bridging the gap between worst-case intractability and practical learnability in discrete settings.
Efficient Quadratic Corrections for Frank-Wolfe Algorithms
Authors: Jannis Halbey; Seta Rakotomandimby; Mathieu Besançon; Sébastien Designolle; Sebastian Pokutta
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including Blended Conditional Gradients, Blended Pairwise Conditional Gradients, and Fully-Corrective Frank-Wolfe.
For this, we prove tight convergence guarantees together with an optimal face identification property.
Furthermore, we propose two highly efficient corrective steps for convex quadratic objectives based on linear optimization or linear system solving, akin to Wolfe's Minimum-Norm Point algorithm, and prove finite-time convergence under suitable conditions.
Beyond optimization problems that are directly quadratic, we revisit two algorithms, Split Conditional Gradient and Second-Order Conditional Gradient Sliding, which can leverage quadratic corrections to accelerate the solution of their quadratic subproblems.
We show improved convergence rates for the first and prove broader applicability for the second.
Finally, we demonstrate substantial computational speedups for Frank-Wolfe-based algorithms with quadratic corrections across the considered problem classes.
LLM Unlearning via Neural Activation Redirection
Authors: William F. Shen; Xinchi Qiu; Meghdad Kurmanji; Alex Iacob; Lorenzo Sani; Yihong Chen; Nicola Cancedda; Nicholas D. Lane
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The ability to selectively remove knowledge from LLMs is highly desirable. However, existing methods often struggle with balancing unlearning efficacy and retain model utility, and lack controllability at inference time to emulate base model behavior as if it had never seen the unlearned data. In this paper, we propose LUNAR, a novel unlearning method grounded in the Linear Representation Hypothesis and operates by redirecting the representations of unlearned data to activation regions that expresses its inability to answer. We show that contrastive features are not a prerequisite for effective activation redirection, and LUNAR achieves state-of-the-art unlearning performance and superior controllability. Specifically, LUNAR achieves between 2.9x and 11.7x improvement in the combined unlearning efficacy and model utility score (Deviation Score) across various base models and generates coherent, contextually appropriate responses post-unlearning. Moreover, LUNAR effectively reduces parameter updates to a single down-projection matrix, a novel design that significantly enhances efficiency by 20x and robustness. Finally, we demonstrate that LUNAR is robust to white-box adversarial attacks and versatile in real-world scenarios, including handling sequential unlearning requests.
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
Authors: Antonios Valkanas; Soumyasundar Pal; Pavel Rumiantsev; Yingxue Zhang; Mark Coates
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promising solution is to use cascaded inference, where small, cheap models handle easy queries, and only the hardest examples are escalated to more powerful models. However, existing cascade methods typically rely on supervised training with labeled data, offer no theoretical generalization guarantees, and provide limited control over test-time computational cost.
We introduce **C3PO** (*Cost Controlled Cascaded Prediction Optimization*), a self-supervised framework for optimizing LLM cascades under probabilistic cost constraints. By focusing on minimizing regret with respect to the most powerful model (MPM), C3PO avoids the need for labeled data by constructing a cascade using only unlabeled model outputs. It leverages conformal prediction to bound the probability that inference cost exceeds a user-specified budget.
We provide theoretical guarantees on both cost control and generalization error, and show that our optimization procedure is effective even with small calibration sets. Empirically, C3PO achieves state-of-the-art performance across a diverse set of reasoning benchmarks including GSM8K, MATH-500, BigBench-Hard and AIME, outperforming strong LLM cascading baselines in both accuracy and cost-efficiency. Our results demonstrate that principled, label-free cascade optimization can enable scalable LLM deployment.