Conference on Neural Information Processing Systems · 1874 papers
Optimizing Automatic Differentiation with Deep Reinforcement Learning
Jamie Lohoff (Peter Grünberg Institute Forschungszentrum Jülich), Emre Neftci (Peter Grünberg Institute Forschungszentrum Jülich)
CodeOptimizationTransformerReinforcement LearningGraphFinance Related
🎯 What it does: Utilizing deep reinforcement learning (a variant of AlphaZero) to find the optimal cross-country elimination order, significantly reducing the number of multiplications while preserving the exact Jacobian;
🎯 What it does: This paper proposes a method that treats the occupancy prediction task as a direct set prediction problem, and implements parallel regression of occupied locations and semantic categories in 3D space using a Transformer encoder-decoder architecture.
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Zhuopeng Xu (Central South University), Ning Gui (Central South University)
CodeScore-based ModelGraph
🎯 What it does: A unified causal discovery algorithm CaPS is proposed, capable of performing DAG structure learning on data with both linear and nonlinear causal relationships.
🎯 What it does: We propose OT4P, a temperature-controlled differentiable transformation that maps unconstrained vectors to the orthogonal group, thereby relaxing permutation matrices and enabling gradient-based optimization and stochastic optimization.
OTTER: Effortless Label Distribution Adaptation of Zero-shot Models
Changho Shin (University of Wisconsin Madison), Frederic Sala (University of Wisconsin Madison)
CodeClassificationDomain AdaptationTransformerLarge Language ModelContrastive LearningImageText
🎯 What it does: This paper proposes a method that adjusts the prediction distribution of zero-shot models (such as CLIP and BERT) through Optimal Transport during the inference phase to address the issue of mismatched label distributions in pre-training, while also being compatible with few-shot scenarios and correcting the selection bias of large language models.
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
Alvin Heng (National University of Singapore), Harold Soh (National University of Singapore)
CodeAnomaly DetectionDiffusion modelImage
🎯 What it does: Using a single unconditional diffusion model, the judgment of whether a sample belongs to the distribution (OOD detection) is achieved by analyzing the rate of change and curvature of the diffusion path from the sample to the standard normal distribution.
Out-Of-Distribution Detection with Diversification (Provably)
Haiyun Yao (Tianjin University), Changqing Zhang (Tianjin University)
CodeAnomaly DetectionConvolutional Neural NetworkMixture of ExpertsImage
🎯 What it does: This paper theoretically analyzes and experimentally verifies the impact of auxiliary outlier sample diversity on OOD detection performance, and proposes a diversity-inducing Mixup (diverseMix) method to enhance the model's generalization ability to unknown OOD.
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
Yu-Liang Zhan (Renmin University of China), Ze-Feng Gao (Renmin University of China)
CodeKnowledge DistillationTransformerImageText
🎯 What it does: This paper proposes a framework called OPDF for over-parameterization of the student model during the knowledge distillation training phase. It utilizes MPO (Matrix Product Operator) to decompose the weight matrix into high-order tensors and designs a tensor alignment loss based on this to improve the distillation effect.
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Jeremias Traub (German Cancer Research Center), Paul F Jaeger
CodeClassificationSegmentationImageBenchmark
🎯 What it does: This paper proposes and validates a new multi-threshold evaluation metric AUGRC, aimed at comprehensively assessing the performance of selective classification systems, overcoming the shortcomings of the existing AURC metric.
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Shengjie Niu (Hong Kong Polytechnic University), Chao Wang (Southern University of Science and Technology)
CodeClassificationContrastive LearningImage
🎯 What it does: An open-world semi-supervised learning framework called OwMatch is proposed, which utilizes conditional self-labeling and hierarchical thresholds to achieve simultaneous classification and clustering of known and unknown classes.
OxonFair: A Flexible Toolkit for Algorithmic Fairness
Eoin D. Delaney, Chris Russell (University of Oxford)
CodeClassificationOptimizationTextTabular
🎯 What it does: Developed OxonFair, a scalable fairness toolkit for binary classification tasks, supporting three types of data: tabular, NLP, and computer vision;
🎯 What it does: Proposes the P2C Net model, which efficiently predicts spatiotemporal PDE dynamics on coarse grids and with large time steps, supporting training with very little data;
PaCE: Parsimonious Concept Engineering for Large Language Models
Jinqi Luo (University of Pennsylvania), Rene Vidal
CodeTransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: A sparse coding-based activation engineering framework, PaCE, is proposed for aligning large language models without adjusting parameters.
🎯 What it does: A link prediction framework called PRB is proposed, which integrates the contextual bandit algorithm with PageRank, capable of balancing exploitation and exploration in both online and offline environments while utilizing graph structural information.
🎯 What it does: This paper proposes a three-stage training pipeline called PaGoDA, which first pre-trains a diffusion model at low resolution, then uses DDIM inversion for first-order generator distillation, and finally enhances the model to high resolution through a progressive upsampling network, significantly reducing training and inference costs.
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
Mishaal Kazmi (University of British Columbia), Mathias Lécuyer (University of British Columbia)
CodeGenerationSafty and PrivacyTransformerLarge Language ModelGenerative Adversarial NetworkImageText
🎯 What it does: A privacy auditing framework named PANORAMIA is proposed, which uses generated data to replace non-member samples for evaluating the privacy leakage of machine learning models;
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
Heeseung Kim (Seoul National University), Kang Min Yoo (NAVER)
CodeGenerationData SynthesisTransformerLarge Language ModelSupervised Fine-TuningMultimodalityAudio
🎯 What it does: A unified speech-text large language model (USDM) has been developed, achieving end-to-end natural speech dialogue generation, capable of producing speech responses with natural prosody without using explicit ASR/TTS.
Parallelizing Linear Transformers with the Delta Rule over Sequence Length
Songlin Yang (Massachusetts Institute of Technology), Yoon Kim (Massachusetts Institute of Technology)
CodeTransformerText
🎯 What it does: Parallel training of the DeltaNet linear transformer is conducted, proposing a memory-efficient reparameterization based on the general Householder transformation and WY representation, and implementing chunkwise forward/backward computation.
Guodong DU, Min Zhang (Harbin Institute of Technology)
CodeOptimizationTransformerLarge Language ModelText
🎯 What it does: A model merging technique called PCB-MERGING is proposed, which is a training-free and data-free approach that achieves model fusion for multiple tasks, domains, and training configurations by balancing competition among task vectors at the parameter level.
Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
Wenke Huang (Wuhan University), Bo Du (Wuhan University)
CodeFederated LearningImage
🎯 What it does: This paper addresses the issue of backdoor attacks in heterogeneous federated learning by proposing a parameter importance evaluation and rescaling method based on the Fisher Information Matrix (FDCR). It identifies malicious clients through clustering of client gradient differences and weights important parameters during global aggregation to enhance backdoor defense effectiveness.
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
Hang Guo (Tsinghua University), Shu-Tao Xia (Tsinghua University)
CodeRestorationTransformerMixture of ExpertsImage
🎯 What it does: A parameter-efficient adaptation method for image restoration, AdaptIR, is proposed, which adapts a frozen pre-trained restoration model through a small number of trainable modules.
🎯 What it does: This paper proposes a Parameter Inverted Image Pyramid Network (PIIP), which uses a multi-branch structure to assign high-resolution images to small models and low-resolution images to large models, achieving multi-scale feature fusion and significantly reducing the computational cost of traditional image pyramids.
Parametric model reduction of mean-field and stochastic systems via higher-order action matching
Jules Berman (Courant Institute of Mathematical Sciences New York University), Benjamin Peherstorfer (Courant Institute of Mathematical Sciences New York University)
🎯 What it does: Learn a minimum energy gradient field based on optimal transport to approximate the dynamics of a population under changes in physical parameters, and use this model to quickly generate complete sample trajectories during inference.
CodeGenerationData SynthesisDrug DiscoveryGraph Neural NetworkTransformerDiffusion modelGraphBiomedical Data
🎯 What it does: The PARD model is proposed, combining autoregressive block generation with discrete diffusion to achieve interchangeability and efficiency in graph generation.
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Jinliang Deng (Hong Kong Generative AI Research and Development Center), Hui Xiong (Hong Kong University of Science and Technology)
CodeConvolutional Neural NetworkTime Series
🎯 What it does: A long sequence time series prediction model based on a decomposition and selection mechanism (SSCNN) is proposed, which achieves precise modeling of complex patterns by decomposing the time series into long-term, seasonal, short-term, and spatial components.
Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics
William Qian (Harvard University), Cengiz Pehlevan (Harvard University)
CodeRecurrent Neural NetworkTime Series
🎯 What it does: This study investigates how data-driven models (RNNs) incorrectly infer the dynamical mechanisms of neural networks when only a subset of neurons is observed, focusing on the linear approximations of linear and nonlinear systems, low-rank networks, and feedforward chains.
🎯 What it does: This paper proposes a two-stage causal bandit algorithm: first, it efficiently learns the observable subgraph of the reward node's ancestors and the necessary potential confounding variables using randomized intervention experiments, constructing the Possible Optimal Intervention Set (POMIS); subsequently, standard bandit algorithms such as UCB are run on this set, ultimately achieving sublinear regret.
🎯 What it does: A Particle Variational Inference (PVI) method based on particle approximation is proposed, which optimizes directly in the mixed distribution space, thus eliminating the need for implicit distributions or explicit parameterization of mixed distributions.
🎯 What it does: This paper proposes a new conditional GAN posterior sampler, pcaGAN, which can generate diverse samples that conform to the posterior distribution in image restoration tasks, supporting uncertainty quantification and perceptual/distortion trade-offs.
🎯 What it does: The PCoTTA framework is proposed, achieving adaptive continuous testing for multi-task point cloud understanding, significantly enhancing the model's transfer performance in continuously changing target domains.
🎯 What it does: Proposes PCP-MAE, which enhances the semantic representation of the encoder by learning to predict masked point cloud centers instead of directly leaking the centers during self-supervised pre-training.
🎯 What it does: A post-processing confidence function learning framework called Colander for threshold-based automatic labeling (TBAL) is proposed, significantly improving the coverage of automatic labeling while keeping the error within a preset threshold.
🎯 What it does: The first pedestrian-vehicle collision posture dataset PVCP based on dashcam perspective has been constructed, and a dual-stage pedestrian pre-collision posture and shape estimation network PPSENet has been proposed to estimate 3D posture and SMPL mesh from collision videos.
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
Dingkang Yang (Fudan University), Lihua Zhang (Fudan University)
CodeTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: A pediatric instruction dataset, PedCorpus 300k, was constructed, and PediatricsGPT was trained through steps including continuous pre-training, full-parameter SFT, DFPO preference alignment, and LoRA parameter-efficient SFT, enhancing pediatric diagnosis and consultation capabilities.
🎯 What it does: A new bidirectional cross-attention Transformer architecture, BiXT, is proposed, which achieves linear time and memory complexity while maintaining the high performance of Transformers, suitable for long sequences and multimodal tasks.
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen (National University of Defense Technology), Dongsheng Li (National University of Defense Technology)
CodeTransformerLarge Language ModelTextRetrieval-Augmented Generation
🎯 What it does: This paper proposes using semi-open-ended QA to probe the knowledge boundaries of large language models (LLMs), constructing a dataset containing multi-answer questions and mining low-probability answers that the target model cannot cover through auxiliary models.
🎯 What it does: Proposes PeRFlow, a stepwise generation framework that segments the sampling trajectory of a pre-trained diffusion model and reconstructs within each segment;
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
Qiang Wu (Xidian University), Shuyuan Yang (Xidian University)
CodeClassificationAnomaly DetectionTransformerTime Series
🎯 What it does: A Transformer model named Peri-midFormer is proposed, which utilizes the multi-periodicity and hierarchical inclusion relationships of time series to decompose the original sequence into a periodic pyramid, and performs self-attention modeling on this structure to accomplish multi-task time series analysis.
🎯 What it does: The paper proposes a knowledge distillation method based on Persistence Homology (PsHD) for semi-supervised continual learning (SSCL) scenarios, aimed at reducing catastrophic forgetting and enhancing robustness to noise.
Persistent Homology for High-dimensional Data Based on Spectral Methods
Sebastian Damrich (Hertie Institute for AI in Brain Health), Dmitry Kobak (Hertie Institute for AI in Brain Health)
CodePoint CloudBiomedical Data
🎯 What it does: The study uses spectral distance (effective resistance, diffusion distance) combined with persistent homology to detect the topological structure of point clouds in high-dimensional noisy environments.
CodeFederated LearningComputational EfficiencyTransformerLarge Language ModelPrompt EngineeringTime SeriesSequential
🎯 What it does: A framework named LM-WEATHER has been developed, utilizing pre-trained language models (PLM) combined with lightweight personalized adapters to achieve localized modeling of meteorological variable sequences (prediction and missing value imputation) in a federated learning environment on distributed devices.
Personalized Federated Learning via Feature Distribution Adaptation
Connor Mclaughlin, Lili Su (Northeastern University)
CodeFederated LearningImage
🎯 What it does: This study proposes a personalized federated learning framework named pFedFDA, which models feature distributions as generative (class-conditional Gaussian) to guide the training of shared feature extractors, and uses local-global interpolation to estimate feature distributions on the client side, thereby generating personalized classifiers for each client.
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Pouya M. Ghari (University of California Irvine), Yanning Shen (University of California Irvine)
CodeFederated LearningMixture of ExpertsImageTabular
🎯 What it does: A Fed-POE algorithm is proposed, which achieves personalized training for online prediction and model fine-tuning by dynamically combining local models, global models, and historical models periodically saved by the server within the federated learning framework.
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
Yuanpu Cao (Pennsylvania State University), Jinghui Chen (Pennsylvania State University)
CodeTransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: This paper proposes an algorithm based on Bidirectional Preference Optimization (BiPO) to learn more effective 'steering vectors' within a single Transformer layer, allowing for precise control of large language model behavior during inference through shifts in the activation space.
pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
Jiaqi Wang (Pennsylvania State University), Fenglong Ma (Pennsylvania State University)
CodeFederated LearningKnowledge DistillationImage
🎯 What it does: This paper proposes pFedClub, which can aggregate heterogeneous models in federated learning and generate personalized, controllable-sized teacher models.
🎯 What it does: This paper proposes a Phase Consistency Model (PCM), which addresses the shortcomings of traditional Latent Consistency Models (LCM) in terms of consistency, controllability, and efficiency by dividing continuous probability flow ODE trajectories into several sub-trajectories and learning self-consistency on each sub-trajectory. It supports deterministic sampling, allows for stronger CFG usage, and improves negative prompt perception.
Physically Compatible 3D Object Modeling from a Single Image
Minghao Guo (Massachusetts Institute of Technology), Wojciech Matusik (Massachusetts Institute of Technology)
CodeGenerationOptimizationImageMeshPhysics Related
🎯 What it does: A framework is proposed for reconstructing physically compatible 3D objects from a single image, separating mechanical properties, external forces, and the object's static shape, and optimizing through static equilibrium hard constraints to obtain models that conform to physical behavior.
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
Taiki Miyagawa (Independent Researcher), Takeru Yokota (Interdisciplinary Theoretical and Mathematical Sciences Program RIKEN Center for Quantum Computing RIKEN)
CodePhysics RelatedOrdinary Differential Equation
🎯 What it does: A learning framework combining cylindrical approximation and PINN is proposed for solving functional differential equations.
🎯 What it does: A unified physical information state space Gaussian process (PHYSS-GP) is proposed, achieving linear time complexity in the time domain through variational inference and capable of handling spatiotemporal partial differential equations.
Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
Michal Balcerak (University of Zurich), bjoern menze
CodeSegmentationOptimizationImageBiomedical DataMagnetic Resonance ImagingPositron Emission TomographyPhysics Related
🎯 What it does: By combining physical models with multimodal medical imaging, a flexible constraint physical information network is constructed to learn the distribution of brain tumor cells and the elasticity of brain tissue, thereby improving the radiotherapy plan for glioblastoma.
CodeTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: This paper proposes the PiSSA method, which initializes low-rank adapters using the main singular values/vectors after performing singular value decomposition on the weights of pre-trained models, while freezing the residual part, thereby achieving parameter-efficient fine-tuning and significantly accelerating convergence speed.
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
Liyi Chen (University of Science and Technology of China), Hui Xiong (Hong Kong University of Science and Technology)
CodeTransformerLarge Language ModelPrompt EngineeringTextGraph
🎯 What it does: This paper proposes Plan-on-Graph (PoG), a knowledge graph-based self-correcting adaptive planning framework for LLMs, which combines task decomposition, adaptive exploration of relationships/entities, memory updates, and reflection mechanisms to achieve efficient reasoning for KGQA tasks.
PLIP: Language-Image Pre-training for Person Representation Learning
Jialong Zuo (Huazhong University of Science and Technology), Jingdong Wang (Baidu Inc.)
CodeRecognitionRetrievalRepresentation LearningTransformerVision Language ModelContrastive LearningImageTextMultimodality
🎯 What it does: The PLIP framework is proposed, utilizing three pre-training tasks (text-guided image coloring, image-guided attribute prediction, identity-based cross-modal contrast) for language-image cross-modal learning, focusing on character attributes and identity.
🎯 What it does: This study investigates prompt learning for large-scale 3D models, enhancing downstream point cloud recognition performance while maintaining domain generalization capabilities, and proposes the Point-PRC adjustment framework.
CodeAnomaly DetectionPrompt EngineeringVision Language ModelMultimodalityPoint Cloud
🎯 What it does: The PointAD framework is proposed, achieving zero-shot 3D anomaly detection and pluggable multimodal detection, directly utilizing the general features of CLIP for anomaly segmentation of unknown point clouds.
🎯 What it does: A state space model PointMamba based on Mamba is designed, which serializes point clouds using space-filling curves and completes tasks such as point cloud classification and segmentation through a pure Mamba encoder.
🎯 What it does: A Poisson distribution-based variational autoencoder (P-VAE) is proposed, replacing the continuous latent variables in traditional VAEs with discrete neural spike counts, achieving more biologically relevant perceptual inference.
Parand A. Alamdari (University of Toronto), Ariel D. Procaccia (Harvard University)
CodeOptimizationReinforcement Learning
🎯 What it does: This paper proposes that in multi-agent Markov decision processes, the reward functions of each agent are viewed as 'preferences' in social choice problems. By using the occupancy polytope of state-action pairs to aggregate all possible strategies, a interpretable and fair collective strategy is obtained.
Polyhedral Complex Derivation from Piecewise Trilinear Networks
Jin-Hwa Kim (NAVER AI Lab & SNU AIIS)
CodeMesh
🎯 What it does: Theoretical analysis of neural implicit surface networks using trilinear interpolation (such as HashGrid) is conducted, and an analytical method for accurately extracting polygonal meshes from piecewise trilinear networks under the Eikonal constraint is proposed.
Maximilian Herde (ETH Zurich), Siddhartha Mishra (ETH Zurich)
CodeOptimizationComputational EfficiencyData-Centric LearningTransformerSupervised Fine-TuningTime SeriesBenchmarkPhysics Related
🎯 What it does: A PDE-based model named POSEIDON is proposed and trained, capable of learning and generating operators for various physical processes, supporting continuous time evaluation and multi-scale feature extraction.
🎯 What it does: The effects of post-processing transformations such as temperature scaling, ensembling, and stochastic weight averaging on model performance were studied, and the phenomenon of post-processing reversal was discovered for the first time.
🎯 What it does: This paper proposes a time neural network PPLN based on learnable piecewise linear functions to handle time series data generated by event cameras.
Practical Bayesian Algorithm Execution via Posterior Sampling
Chu Xin Cheng (California Institute of Technology), Yisong Yue (California Institute of Technology)
CodeOptimizationTabular
🎯 What it does: This paper proposes and implements a Bayesian algorithm execution framework based on posterior sampling, called PS-BAX, for efficiently estimating the outputs of base algorithms on expensive black-box functions.
Julius Kunze (University College London), James Townsend (University of Amsterdam)
CodeCompressionGraph
🎯 What it does: A general-purpose entropy coding method for unordered objects is proposed, combining incomplete shuffling coding and autoregressive shuffling coding, capable of compressing large unordered data structures (such as multisets and graphs) in one go and achieving high-speed compression.
Precipitation Downscaling with Spatiotemporal Video Diffusion
Prakhar Srivastava (University of California), Stephan Mandt (University of California)
CodeGenerationData SynthesisDiffusion modelVideoTime Series
🎯 What it does: Using video diffusion models to perform spatiotemporal downscaling of low-resolution precipitation sequences, refining them into high-resolution sequences.
🎯 What it does: This paper studies the prediction of future actions and events of deployed reinforcement learning agents, systematically comparing explicit planning, implicit planning, and non-planning agents, and proposes two prediction methods based on internal states and simulations.
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Marc Wanner (Chalmers University of Technology and University of Gothenburg), Alexandru Gheorghiu (Chalmers University of Technology and University of Gothenburg)
CodeTabularPhysics Related
🎯 What it does: Two machine learning algorithms are proposed to predict the ground state properties of quantum many-body systems with constant sample complexity.
Prediction-Powered Ranking of Large Language Models
Ivi Chatzi (Max Planck Institute for Software Systems), Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)
CodeRecommendation SystemTransformerLarge Language ModelText
🎯 What it does: A statistical framework based on prediction-driven inference is proposed, which constructs a confidence set (rank-set) for model ranking using a small amount of human comparisons and a large amount of powerful LLM comparison data, thereby quantifying ranking uncertainty.
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Bei Li (Northeastern University), Xunliang Cai (Meituan Inc.)
CodeTransformerTextOrdinary Differential Equation
🎯 What it does: A PCformer model is proposed, utilizing a prediction-corrector framework combined with exponential moving average coefficient learning to improve the performance of Transformers in various NLP tasks.
CodeGenerationRecommendation SystemReinforcement Learning from Human FeedbackFlow-based ModelImageTextOrdinary Differential Equation
🎯 What it does: Proposes Preference Flow Matching (PFM), which directly learns a vector field from low-preference data to high-preference data through flow matching, achieving preference alignment in black-box models.
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice
Sebastiaan De Peuter (Aalto University), Samuel Kaski (Aalto University)
CodeRecommendation SystemOptimizationTabular
🎯 What it does: Two computable and inferable choice models, Computable Rationality with Cross-Feature Extension (CRCS and LC-CRCS), are proposed to learn latent utility functions from human preference data, and their utility is validated in multi-task cases (structural design, hydraulic networks, chemical synthesis planning).
🎯 What it does: Utilizing preference comparison/ranking information, the high-dimensional probability density of experts is learned using normalized flows.
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Xiaobin Li (Xidian University), Jing Liu (Xidian University)
CodeOptimizationTransformerTabular
🎯 What it does: A pre-trained population optimization model (POM) is proposed, which can directly solve new black-box optimization tasks with little or no tuning;
CodeGraph Neural NetworkTransformerSupervised Fine-TuningGraphPhysics Related
🎯 What it does: This paper proposes the Codomain Attention Neural Operator (CoDA‑NO) and conducts self-supervised pre-training to support rapid inference and few-shot fine-tuning of multi-physics coupled PDEs.
🎯 What it does: This paper pre-trains the network using random noise inputs and random labels without real data, and subsequently employs the feedback alignment (FA) algorithm during training with real data. It observes that pre-training significantly enhances learning speed and generalization performance.
🎯 What it does: This paper proposes adding orthogonal regularization to the encoder (including convolutional layers and linear layers) during the pre-training phase of self-supervised learning to prevent the collapse of the weight matrix, hidden features, and representation vectors.
🎯 What it does: This study investigates the model collapse issue of Deep Canonical Correlation Analysis (DCCA) in multi-view representation learning and proposes a Noise Regularization method (NR-DCCA) to prevent a sharp decline in performance during training.
Principled Bayesian Optimization in Collaboration with Human Experts
Wenjie Xu (Ecole Polytechnique Federale de Lausanne), Michael A Osborne
CodeOptimizationTabular
🎯 What it does: A collaborative Bayesian optimization framework is designed, utilizing binary labels from experts to guide sampling, and achieving two theoretical guarantees: 'harmless' and 'handover'.
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
Zeki Kazan (Duke University), Jerome Reiter
CodeSafty and PrivacyTabularBiomedical Data
🎯 What it does: This paper proposes a framework based on Bayesian posterior-prior risk (relative disclosure risk) to determine the value of the privacy budget ε in differential privacy (DP), and provides the corresponding closed-form solution and minimization problem.
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
Kristjan Greenewald (MIT-IBM Watson AI Lab), Kai Xu (MIT-IBM Watson AI Lab)
CodeData SynthesisDomain AdaptationSafty and PrivacyImageTabular
🎯 What it does: By adding noise to data projections and using the gradient-free smoothed-sliced f-divergence to train generative models, differential privacy synthetic data is achieved.
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe (ETH Zurich), Martin Vechev (ETH Zurich)
CodeSafty and PrivacyTransformerPrompt EngineeringVision Language ModelImageChain-of-Thought
🎯 What it does: This study investigates the privacy risks of visual-language models (VLM) in inferring personal attributes from non-human images and constructs the VIP dataset for evaluation.
🎯 What it does: A probabilistic shape distortion (PCD) method is proposed to enhance the robustness of multimodal models in the absence of certain modalities;
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Yenho Chen (Georgia Institute of Technology), Christopher John Rozell
CodeAnomaly DetectionRepresentation LearningTime SeriesSequentialBiomedical Data
🎯 What it does: This paper proposes a probabilistic decomposition linear dynamical system (p-dLDS) and introduces time-varying bias terms and probabilistic sparsity structures to discover interpretable latent dynamics in high-dimensional neural signals.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson (Linköping University), Fredrik Lindsten (Linköping University)
CodeGraph Neural NetworkGraphTime Series
🎯 What it does: The Graph-EFM model is proposed, which achieves efficient probabilistic weather forecasting by introducing hierarchical grids and latent variables into graph neural networks.
Probablistic Emulation of a Global Climate Model with Spherical DYffusion
Salva Rühling Cachay (University of California San Diego), Rose Yu (University of California San Diego)
CodeDiffusion modelTime Series
🎯 What it does: A conditional generative model named Spherical DYffusion has been developed to realistically simulate global climate sequences from 10 to 100 years while maintaining low computational costs.
Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach
Lei Ding (University of Alberta), Bei Jiang (University of Alberta)
CodeGenerationTransformerLarge Language ModelText
🎯 What it does: Through experimental design, this study utilizes ChatGPT to generate job application materials, combined with the PRISM algorithm to assess gender bias, exploring the impact of AI-generated text on gender inequality in the labor market.
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao (University of California), Aditya Grover (University of California)
CodeClassificationOptimizationTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
🎯 What it does: By visualizing the decision boundaries of binary classification tasks, this paper analyzes the contextual learning ability of large language models and proposes a smoothing method.
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
Kun yuan, Nicolas Padoy (University of Strasbourg)
CodeRecognitionRetrievalConvolutional Neural NetworkLarge Language ModelContrastive LearningVideoTextMultimodality
🎯 What it does: A scheme for video-language pretraining in surgical procedures is proposed, which includes hierarchical knowledge enhancement and the Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining (PeskaVLP) framework.
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
Cong Wan (Xi'an Jiaotong University), Yihong Gong (Xi'an Jiaotong University)
CodeGenerationSafty and PrivacyAdversarial AttackDiffusion modelImage
🎯 What it does: In response to the privacy and copyright risks of customized text-to-image diffusion models, a Prompt-agnostic Adversarial Perturbation (PAP) method is proposed, which adds subtle perturbations to images, preventing the model from generating high-quality images of specific individuals or styles under any prompt, thereby protecting personal data and artistic creations.
🎯 What it does: A prospective learning framework is proposed, along with the Prospective ERM algorithm; experimental validation is conducted on synthetic data and periodic/hidden Markov tasks from MNIST and CIFAR-10.
🎯 What it does: A prospective representation learning framework is proposed, which reserves space through embedding compression constraints in the benchmark phase, and utilizes prototype-guided representation updates in the incremental phase to achieve separation of old and new classes in the latent space, thereby reducing forgetting and conflicts in zero-shot class incremental learning.
ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
Mingchen Li (Shanghai Jiao Tong University), Liang Hong (Shanghai Jiao Tong University)
CodeProtein Structure PredictionTransformerLarge Language ModelBiomedical Data
🎯 What it does: ProSST has been developed, a Transformer language model that combines protein sequences and three-dimensional structures, and learns rich contextual representations through pre-training.
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach
Yarin Bar (Technion Israel Institute of Technology), Yaniv Romano (Technion Israel Institute of Technology)
CodeDomain AdaptationImage
🎯 What it does: This paper proposes a testing-time adaptive method called POEM based on online entropy matching, which improves inference accuracy under distribution shifts by detecting entropy distribution drift and adaptively updating model parameters.
Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
Tinglin Huang (Yale University), Wengong Jin (Northeastern University)
CodeProtein Structure PredictionTransformerGraphBiomedical Data
🎯 What it does: An unsupervised protein-nucleic acid complex contact map prediction model FAFormer was developed, and the predicted contact maps were used for unsupervised aptamer screening.
ProTransformer: Robustify Transformers via Plug-and-Play Paradigm
Zhichao Hou (North Carolina State University), Xiaorui Liu (North Carolina State University)
CodeOptimizationAdversarial AttackTransformerLarge Language ModelImageTextGraph
🎯 What it does: A pluggable robust attention mechanism called ProAttention is proposed, which enhances the attack resistance of Transformer models without the need for additional training or fine-tuning.