Conference on Neural Information Processing Systems Β· 1376 papers
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Massil HIHAT, Simon Bussy (Califrais' Machine Learning Lab)
CodeOptimizationTabularTime Series
π― What it does: A general online inventory optimization framework (OIO) is proposed, along with the MaxCOSD algorithm, which achieves an unbounded O(βT) expected and high-probability fallback rate under non-i.i.d. demand, arbitrary losses, and various inventory dynamics (such as perishability, stockouts, etc.).
Dheeraj Baby (University of California Santa Barbara), Yu-Xiang Wang (University of California Santa Barbara)
CodeClassificationOptimizationImageText
π― What it does: This paper studies classifier adaptation under online label drift (both unsupervised and supervised), proposing an online regression-based label ratio estimation and reweighting method that achieves optimal dynamic regret.
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui (Emory University), Carl Yang (Emory University)
CodeObject DetectionGenerationRetrievalTransformerPrompt EngineeringVision Language ModelImageTextMultimodality
π― What it does: Proposes the OpenVik framework, completing the detection of open-source visual knowledge and the generation of unformatted knowledge, achieving knowledge extraction from images to free text.
π― What it does: This paper proposes an improved method based on Operation-Level Early Stopping (OLES) to enhance the robustness of traditional DARTS, alleviating performance collapse caused by the dominance of skip connections.
π― What it does: This paper studies optimal exploration in model-based reinforcement learning for nonlinear systems, aiming to quantify which system parameters are most important for learning good controllers, and proposes an efficient exploration algorithm to reduce the uncertainty of these parameters.
π― What it does: A post-training parameter and neuron pruning method called OPNP is proposed, which identifies and removes weights and neurons that are detrimental to OOD detection based on gradient sensitivity.
π― What it does: A new reinforcement learning algorithm based on Bayesian upper confidence bounds (EBUCB) is designed and analyzed, capable of handling the multi-armed bandit problem under the constraint of bounded approximate Bayesian inference error.
π― What it does: Introduce an optimal transport-based distributional robustness framework (OT-MDR) in the model space and extend it to three scenarios: single model, ensemble model, and Bayesian neural networks;
π― What it does: This paper proposes a conditionally score-based diffusion model guided by optimal transport (OTCS) for image translation using unpaired or partially paired training sets.
Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making
Ting Li (Shanghai University of Finance and Economics), Hongtu Zhu (University of North Carolina at Chapel Hill)
CodeOptimizationReinforcement LearningSequential
π― What it does: The study investigates how to design experimental allocation schemes in sequential decision-making to maximize the amount of information obtained from online experiments and accurately estimate treatment effects.
Optimistic Active Exploration of Dynamical Systems
Bhavya Sukhija (ETH ZΓΌrich), Andreas Krause (ETH ZΓΌrich)
CodeOptimizationReinforcement LearningTime Series
π― What it does: This paper proposes the OPAX algorithm for actively exploring unknown dynamical systems and learning globally consistent dynamical models, thereby achieving multi-task zero-shot planning.
π― What it does: This paper proposes an optimistic exploration framework based on symbolic models, utilizing an optimistic symbolic approximation model with online learning in conjunction with a diversified planner to guide RL agents in goal-oriented exploration within sparse reward environments, continuously updating the model through environmental feedback.
Optimization of Inter-group criteria for clustering with minimum size constraints
Eduardo Sany Laber, Lucas Murtinho (Pontifical Catholic University of Rio de Janeiro)
CodeOptimizationTabular
π― What it does: An optimization method for clustering under the minimum spacing (Min-Sp) and minimum spanning tree spacing (MST-Sp) is proposed, addressing the minimum size constraint that each cluster must contain at least L samples.
π― What it does: A learning ranking heuristic is proposed and validated based on a ranking loss function, which is used to learn heuristics that can be directly applied to A* and GBFS from solved instances;
Optimized Covariance Design for AB Test on Social Network under Interference
Qianyi Chen (Tsinghua University), Yong Wang (Tencent)
CodeOptimizationGraph
π― What it does: In A/B testing considering network interference in social networks, a covariance-based experimental randomization scheme is proposed, aiming to balance the bias and variance of the estimator and improve the estimation accuracy of the Global Average Treatment Effect (GATE).
π― What it does: This paper proposes a framework for optimization on trained Graph Neural Networks (GNNs), providing two types of symmetry-breaking constraints to address the symmetry issues caused by graph isomorphism, and designs a feasible graph indexing algorithm.
CodeGenerationOptimizationTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringImageText
π― What it does: This paper proposes an automated prompt optimization framework (Promptist) that first performs supervised fine-tuning on manually designed prompts and then further improves them using reinforcement learning to generate prompts that better align with the preferences of text-to-image models, thereby enhancing the aesthetic quality of generated images and their relevance to the original user intent.
π― What it does: This paper proposes an OOD detection method based on auxiliary tasks called ATOL, which utilizes fake OOD data generated by a generator to improve the OOD detection performance of the predictor and reduce misjudgments.
π― What it does: This paper proposes a robust RGW model for Gromov-Wasserstein distance, which can still approximate the original GW distance in the presence of outliers.
π― What it does: The study addresses the recursive bias problem of batch normalization (BN) in continual learning and proposes the AdaB2N method to achieve adaptive balancing and adaptation of BN statistics, enhancing continual learning performance.
π― What it does: A zero-shot text-to-speech (TTS) model named P-Flow is proposed, which utilizes short audio prompts for speaker adaptation and employs a flow-matching generator to achieve non-autoregressive, real-time speech synthesis.
π― What it does: This paper proposes a learning framework that utilizes PAC-Bayes theory to enhance the efficiency and generalization guarantees of Inductive Conformal Prediction (ICP), allowing for direct learning of model and score function parameters on calibration data without the need for an additional validation set, thus achieving dual guarantees of expected coverage and efficiency.
PaintSeg: Painting Pixels for Training-free Segmentation
Xiang Li (Carnegie Mellon University), Bhiksha Raj (Microsoft)
CodeSegmentationTransformerDiffusion modelImage
π― What it does: A completely unsupervised image segmentation method called PaintSeg is proposed, which utilizes contrastive painting (AMCP) to create a contrast between the original image and the generated painting image, thereby gradually improving the segmentation mask.
π― What it does: This paper proposes a method for parallelizing diffusion model sampling using Picard iteration, called ParaDiGMS, which significantly reduces the time delay for a single sample while maintaining sample quality.
π― What it does: This paper proposes a reset-free and parallelizable series of Parallel Spiking Neurons (PSN) to significantly enhance the simulation speed and long-term dependency learning capability of spiking neural networks.
π― What it does: A lightweight cross-modal transfer framework called Aurora is proposed, achieving efficient cross-modal task transfer by training only about 0.1M parameters on the frozen BLIP large model.
π― What it does: This paper proposes a continuous table semantic parsing method (C3) that combines Parameter-Efficient Fine-Tuning (PEFT) and In-Context Tuning (ICT). It captures and compresses contextual information in few-shot scenarios through a teacher-student framework, completely avoiding catastrophic forgetting.
π― What it does: A new meta reinforcement learning method SDVT is proposed, which achieves better generalization of task diversity by decomposing non-parametric tasks into shared sub-tasks and parameterizing them.
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Kalpesh Krishna (University of Massachusetts Amherst), Mohit Iyyer (University of Massachusetts Amherst)
CodeGenerationRetrievalTransformerLarge Language ModelTextRetrieval-Augmented Generation
π― What it does: This paper proposes a controllable long-text rewriting model called DIPPER, designed to rewrite AI-generated text to evade existing detection methods; it also presents a retrieval-based defense scheme that uses a generated text library to detect whether the text is machine-generated.
Parselπ: Algorithmic Reasoning with Language Models by Composing Decompositions
Eric Zelikman (Stanford University), Nick Haber (Stanford University)
CodeRobotic IntelligenceAI Code AssistantTransformerLarge Language ModelPrompt EngineeringText
π― What it does: The Parsel framework is proposed, which utilizes large language models to hierarchically decompose, implement, and verify complex algorithmic tasks, thereby achieving the automated generation of complex programs and robotic planning tasks.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk (Ludwig Maximilian University of Munich), Stefan Feuerriegel (Ludwig Maximilian University of Munich)
CodeFlow-based ModelTabular
π― What it does: This paper studies counterfactual inference for continuous outcomes in Markov structural causal models, proposing a Curvature-Sensitive Model (CSM) and implementing a Pseudo-Invertible Decoder (APID) based on Residual Regularization Flow and Variational Augmentation, achieving interval estimation for partial identification.
π― What it does: A partial label learning method based on adversarial priors, DPCLS, is proposed, which utilizes a label confidence matrix and its transposed second-order similarity matrix, as well as a semantic dissimilarity matrix constructed and propagated through a candidate label set to achieve candidate label contraction and label discrimination.
π― What it does: A particle variational inference framework based on generalized Wasserstein gradient flow (GWG) is designed, and an adaptive version Ada-GWG is proposed to improve sampling efficiency.
π― What it does: This paper proposes a Patch Diffusion training framework, which significantly accelerates the training of diffusion models and improves data efficiency by performing conditional score matching at the image patch level.
π― What it does: The ActionBench benchmark is proposed to quantify the action knowledge of video-language models, and the PAXION framework is designed to fill the gaps in the model's action understanding through a lightweight Knowledge Patcher and Knowledge Fuser while maintaining its original capabilities.
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Phillip Lippe (Microsoft Research AI4Science), Johannes Brandstetter (Microsoft Research AI4Science)
CodeDiffusion modelTime SeriesPhysics Related
π― What it does: This paper proposes a neural PDE solver called PDE-Refiner based on iterative refinement, addressing the issue of accuracy degradation caused by the loss of frequency information in long-term rolling predictions.
Soham Deshmukh (Microsoft), Huaming Wang (Microsoft)
CodeClassificationRecognitionGenerationTransformerLarge Language ModelVision Language ModelMultimodalityAudio
π― What it does: Designed and trained an audio language model called Pengi, which unifies all audio tasks into an input of audio + text prompts, outputting a free-text generation task that can complete open and closed audio tasks without additional fine-tuning.
Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
Ran Ran (North Carolina State University), Wujie Wen (North Carolina State University)
CodeOptimizationSafty and PrivacyComputational EfficiencyGraph Neural NetworkGraph
π― What it does: For encrypted inference of Graph Convolutional Networks (GCN), we propose Penguinβa method for homomorphic encryption ciphertext compression and operation optimization based on two-dimensional parallel packing and interleaved assembly.
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint
Dror Freirich (Technion Israel Institute of Technology), Ron Meir (Technion Israel Institute of Technology)
CodeRestorationOptimizationVideo
π― What it does: This paper proposes an online temporal signal reconstruction method that ensures the filter output is completely consistent with the original natural signal in terms of spatiotemporal distribution, thereby achieving the goal of 'perfect perceptual quality'.
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
Feiyang Kang (Virginia Tech), Ruoxi Jia (Virginia Tech)
CodeOptimizationData-Centric LearningImage
π― What it does: A framework called projektor is proposed for predicting model performance based on a small number of samples and selecting data sources in partially revealed data sources.
π― What it does: This paper proposes a neural network framework capable of handling 'weight space' data such as neural network weights and gradientsβPermutation Equivariant Neural Functionals (NFNs). It achieves effective encoding of the weight space by constructing an NF-Layer that is equivariant under neuron permutation symmetry.
π― What it does: This paper proposes to enhance the transferability of targeted adversarial attacks by utilizing perturbation directions aimed at high sample density regions (HSDR) of the target class.
π― What it does: This paper proposes a facial image restoration framework called PGDiff based on diffusion models, which achieves multi-task restoration by 'partially guiding' the attributes of high-quality images during the reverse diffusion process, without the need to pre-assume the degradation process.
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
Ruiyuan Kang (Bayanat AI), Dimitrios Kyritsis
CodeOptimizationGenerative Adversarial NetworkPhysics Related
π― What it does: A physics-driven machine learning error correction algorithm called GEESE is proposed to detect and correct failures in ML estimates for scientific and engineering inverse problems, significantly reducing the number of physical evaluation queries.
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Kim Andrea Nicoli, Shinichi Nakajima (Technische Universitaet Berlin)
CodeOptimizationPhysics Related
π― What it does: This paper proposes a Bayesian optimization method that combines physical priors for parameter search in the Variational Quantum Eigensolver (VQE).
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
Yuval Kirstain (Tel Aviv University), Omer Levy (Stability AI)
CodeGenerationRecommendation SystemTransformerReinforcement LearningPrompt EngineeringVision Language ModelImageText
π― What it does: By constructing a web application to collect real user preferences during the text-to-image generation process, a large-scale Pick-a-Pic dataset was generated, and based on this, the PickScore scoring model was trained to predict user preferences, evaluate models, and enhance generation quality.
π― What it does: A new method called PICProp is proposed for uncertainty quantification in physical information learning, particularly by propagating confidence intervals (CIs) from data locations to the entire domain.
π― What it does: Proposed PID-inspired historical encoders PIDE and GPIDE to improve the robustness of deep reinforcement learning in partially observable control tasks.
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
Hojoon Lee (Korea Advanced Institute of Science and Technology), Chulhee Yun (Korea Advanced Institute of Science and Technology)
CodeReinforcement LearningImage
π― What it does: This study addresses the 'plasticity loss' problem in offline reinforcement learning and proposes the PLASTIC algorithm to enhance sample efficiency by improving the plasticity of inputs and labels.
Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
Chaofei Fan (Stanford University), Francis R Willett
CodeRecurrent Neural NetworkLarge Language ModelText
π― What it does: This paper proposes a method for continuous online self-calibration of iBCI (CORP) using language models to generate pseudo-labels, and validates its long-term stability in writing-based BCI through over a year of clinical trials.
π― What it does: A self-regressive generative model called PoET is proposed and trained to generate new protein sequences from homologous sequences of a given protein family, and to predict the functional variants through conditional probabilities.
π― What it does: This paper proposes PointGPT, which achieves self-supervised pre-training of point clouds through autoregressive generation tasks, overcoming the challenges of unordered point clouds, low information density, and the gap between generation and downstream tasks.
π― What it does: A PSRO (PSD-PSRO) framework based on multi-strategy space diversity is proposed, which further approximates the Nash equilibrium of zero-sum non-transitive games by incorporating diversity regularization focused on convex hull expansion in the best response solving.
Tan Zhu (University of Connecticut), Jinbo Bi (University of Connecticut)
CodeRecommendation SystemTabularBiomedical Data
π― What it does: Designing the Polyhedron Attention Module (PAM) to achieve adaptive order feature interaction, improving the expressive power of traditional ReLU networks;
PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection
Zhizhang Yuan (Zhejiang University), Yafeng Li (Nuozhu Technology Co., Ltd.)
CodeClassificationDomain AdaptationAnomaly DetectionConvolutional Neural NetworkTransformerContrastive LearningTime SeriesBiomedical Data
π― What it does: A patient-independent SEEG seizure detection model called PPi is proposed, which is based on self-supervised pre-training and employs two techniques, channel background subtraction and brain region enhancement, to improve generalization ability during the detection phase.
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu (Columbia University), John Patrick Cunningham
CodeImage TranslationRestorationGenerationData SynthesisProtein Structure PredictionDiffusion modelScore-based ModelImageBiomedical Data
π― What it does: This paper proposes the Twisted Diffusion Sampler (TDS), a conditional sampling framework based on Sequential Monte Carlo (SMC) that allows for precise sampling from trained diffusion models without the need for task-specific training.
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang (Aalto University), Luigi Acerbi (University of Helsinki)
CodeOptimizationTime Series
π― What it does: A new Relational Conditional Neural Process (RCNP) model is proposed, which achieves intrinsic modeling of equivariance through relational encoding, suitable for high-dimensional inputs.
π― What it does: Utilizing the rich wild video data from the internet for unsupervised pre-training, a world model is constructed that achieves higher sample efficiency in visual control tasks.
π― What it does: This paper proposes a new PR-Divergence (D_Ξ»-PR) to explicitly optimize the precision-recall trade-off of generative models, and implements user-specified Ξ» values on GANs and Normalizing Flows.
π― What it does: The PRED framework is proposed for pre-training outdoor LiDAR point clouds based on image semantic rendering, addressing issues of incomplete point clouds and occlusion.
Predicting a Protein's Stability under a Million Mutations
Jeffrey Ouyang-Zhang (University of Texas at Austin), Philipp Kraehenbuehl
CodeProtein Structure PredictionSupervised Fine-TuningBiomedical Data
π― What it does: A model named Mutate Everything has been constructed, capable of parallel prediction of thermodynamic stability changes (ΞΞG) for single and multiple mutations (including double mutations) in a single forward pass.
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao (Hong Kong University of Science and Technology), Bernie Wang
CodeGenerationData SynthesisConvolutional Neural NetworkDiffusion modelMultimodalityTime Series
π― What it does: A two-stage forecasting framework called PreDiff is proposed, which generates multimodal forecasts using a latent diffusion model and introduces physical constraints through a knowledge alignment mechanism.
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shentao Yang (University of Texas), Mingyuan Zhou (University of Texas)
CodeGenerationReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningText
π― What it does: An alternating training process is proposed to improve the fine-tuning of language models by transforming sequence-level preferences into token-level guidance.
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Samuel Goldman (Massachusetts Institute of Technology), Connor W. Coley (Massachusetts Institute of Technology)
CodeGraph Neural NetworkTransformer
π― What it does: The SCARF method is proposed, which splits mass spectrometry prediction into two steps: sub-formula generation (SCARF-Thread) and intensity prediction (SCARF-Weave). It efficiently enumerates sub-formulas using a prefix tree, enabling fast and physically reasonable predictions from molecules to mass spectrometry.
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
Allan Raventos, Surya Ganguli (Stanford University)
CodeTransformerTabular
π― What it does: This study investigates the impact of task diversity on in-context learning (ICL) of pre-trained Transformers, particularly exploring the task diversity threshold in linear regression tasks.
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
Yingyi Chen (KU Leuven), Johan Suykens
CodeComputational EfficiencyTransformerReinforcement LearningImageTime Series
π― What it does: This paper proposes viewing the self-attention of the Transformer as a singular value decomposition (KSVD) of an asymmetric kernel matrix, and implements the Primal-Attention mechanism in the original representation, avoiding expensive kernel matrix computations.
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
Zhiqing Sun (Carnegie Mellon University), Chuang Gan (Massachusetts Institute of Technology)
CodeGenerationExplainability and InterpretabilityTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
π― What it does: This paper proposes the SELF-ALIGN method, which self-aligns large language models from scratch to build the Dromedary AI assistant.
π― What it does: A weight initialization method for Input Convex Neural Networks (ICNN) is proposed, which enables stable signal propagation under non-negative weight conditions, thereby enhancing learning speed and generalization performance.
π― What it does: This paper proposes a framework called pFedBreD that introduces personalized prior knowledge into federated learning. It utilizes Bayesian modeling, Bregman-Moreau envelopes, and Relaxed Mirror Descent (RMD) to achieve the injection and extraction of priors, and further personalizes local models through a meta-learning-based step size strategy.
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik (University of Freiburg), Frank Hutter (University of Freiburg)
CodeOptimizationHyperparameter SearchTabular
π― What it does: The PriorBand algorithm is proposed, which combines expert priors and low-cost proxy tasks to optimize hyperparameters of deep learning models within a multi-fidelity framework.
π― What it does: A new class of Probabilistic Exponential Integrators is proposed for efficient simulation and uncertainty quantification of stiff semi-linear ordinary differential equations.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Dominik Straub (Technische UniversitΓ€t Darmstadt), Constantin A. Rothkopf (Technische UniversitΓ€t Darmstadt)
CodeOptimizationRobotic IntelligenceReinforcement LearningTime Series
π― What it does: A probabilistic inverse optimal control method for partially observable nonlinear systems is proposed, which can infer the agent's cost function and perception/motion noise parameters from the observed state trajectories.
π― What it does: This paper proposes PWFN, a probabilistic framework that combines Bayesian networks with weight-sharing clustering for weight quantization and compression of large-scale neural networks.
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Sungik Choi (LG AI Research), Moontae Lee (LG AI Research)
CodeAnomaly DetectionDiffusion modelImage
π― What it does: Proposes the Projection Regret method, which maps any sample to a training distribution sample similar to the background through diffusion model projection, and detects anomalies using perceptual distance, further eliminating background bias through recursive projection;
π― What it does: This paper conducts a theoretical analysis and empirical evaluation of the Threshold-Based Automatic Labeling (TBAL) algorithm, providing sample complexity bounds between the amount of validation data and the quality of automatic labeling.
π― What it does: Pre-train a general soft prompt POMP to learn semantics on the large-scale categories of ImageNet-21K, and then directly perform zero-shot transfer to tasks such as image classification, semantic segmentation, and object detection.
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Siqiao Xue (Ant Group), JUN ZHOU
CodePrompt EngineeringSequential
π― What it does: This paper proposes a framework called PromptTPP for continual learning in streaming event sequences without replay buffers and task labels;
PromptIR: Prompting for All-in-One Image Restoration
Vaishnav Potlapalli (Mohamed bin Zayed University of AI), Fahad Khan
CodeRestorationTransformerPrompt EngineeringImage
π― What it does: This paper proposes PromptIR, a versatile blind image restoration framework that utilizes prompt learning to simultaneously restore various degraded images such as denoising, deraining, and defogging under unknown degradation conditions.
Nate Gruver (New York University), Andrew Gordon Wilson (New York University)
CodeOptimizationDrug DiscoveryReinforcement LearningDiffusion modelBiomedical Data
π― What it does: This paper proposes NOS (Diffusion-Optimized Sampling) and an improved version LaMBO-2, utilizing discrete diffusion models and gradient guidance for direct antibody design in the protein sequence space, ultimately achieving high expression rates and high binding rates in laboratory validation.
PROTES: Probabilistic Optimization with Tensor Sampling
Anastasia Batsheva (Skolkovo Institute of Science and Technology), Ivan Oseledets (Skolkovo Institute of Science and Technology)
CodeOptimization
π― What it does: A probability sampling optimization method called PROTES based on Tensor Train (TT) format is proposed to solve black-box multi-dimensional discrete optimization problems.
π― What it does: The ProtoDiff framework is proposed, which utilizes a task-oriented diffusion model during the meta-learning phase to gradually generate overfitting prototypes from naive prototypes, enhancing few-shot classification performance.
π― What it does: This study investigates a prototype-based framework for quantifying perceptual uncertainty, aimed at assessing data uncertainty and enhancing prediction credibility in cross-modal retrieval.
π― What it does: This paper proposes and theoretically proves that a curriculum learning strategy, which first trains on sparse samples (i.e., low Hamming weight) and then on complete samples, can significantly reduce the number of training steps and sample size required to learn k-th order singular functions (Parity) under a mixed sparse-dense input distribution.
π― What it does: This paper proposes a theoretical framework that combines the stabilization characteristics of low-level controllers with generative models to achieve provable behavior cloning of complex expert demonstrations.
π― What it does: This paper addresses the issue of uneven node training in Graph Contrastive Learning (GCL) by proposing a 'node compactness' metric to measure the degree to which each node adheres to the InfoNCE principle under all possible graph augmentations. Based on this, a provably optimal training (POT) regularization method is designed to enhance the quality of node embeddings.
π― What it does: An algorithm INVPROP is proposed for approximating the upper bound of neural network preimages, which can efficiently solve the feasible region of the input space under given output linear constraints.
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Hanlin Zhu (University of California Berkeley), Amy Zhang (University of Texas Austin)
CodeRobotic IntelligenceReinforcement Learning
π― What it does: This paper studies an algorithm for offline goal-oriented reinforcement learning called VP-learning. It provides a theoretical sample complexity and suboptimality analysis under the assumptions of general function approximation and single policy concentration, and validates its effectiveness on tasks such as robot grasping/pushing.
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
Youbang Sun (Northeastern University), Shahin Shahrampour (Northeastern University)
CodeOptimizationReinforcement Learning
π― What it does: This paper studies the convergence properties of the Independent Natural Policy Gradient (NPG) algorithm in Markov Potential Games, proving that under certain technical assumptions, its average NE-gap can converge to Ξ΅-Nash equilibrium at a rate of O(1/Ξ΅);
π― What it does: The concept of 'proximity bias' is proposed, studying the issue of deep models being overconfident in sparse regions, and its universality is validated on 504 ImageNet pre-trained models;
Andrey Kuzmin (Qualcomm AI Research), Tijmen Blankevoort (Qualcomm AI Research)
CodeCompressionLarge Language ModelSupervised Fine-TuningImageText
π― What it does: This paper provides a systematic comparison of two compression techniques: neural network pruning and quantization, covering theoretical error analysis, hierarchical error lower bounds, global solutions, and fine-tuning results of complete models.
Theo Gruner (Technical University of Darmstadt), Jan Peters (German Research Center for AI)
Code
π― What it does: A novel SBI method called Pseudo-Likelihood Inference (PLI) is proposed for inferring the posterior distribution of black-box simulators under multiple observation conditions.
PTQD: Accurate Post-Training Quantization for Diffusion Models
Yefei He (Zhejiang University), Bohan Zhuang (Monash University)
CodeGenerationCompressionDiffusion modelImage
π― What it does: This paper proposes the PTQD method for untrained low-bit quantization of diffusion models, significantly reducing model size and inference costs.
π― What it does: A heterogeneous graph representation learning framework POFD is proposed and implemented, which integrates the effects of public opinion fields and social circle influences for the analysis of popular topic diffusion.
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
Hyemi Jang (Seoul National University), Sungroh Yoon (Seoul National University)
CodeRestorationConvolutional Neural NetworkImage
π― What it does: A self-supervised image denoising model called PUCA based on J-invariant U-Net is proposed, which can train high-quality denoising results using only noisy images.
π― What it does: A PUe algorithm based on causal inference is proposed to address the imbalance of positive and negative labels in PU learning. By using normalized inverse probability weighting for positive samples, the loss function is corrected to improve classifier performance in scenarios with label bias.
Tim Dettmers (University of Washington), Luke Zettlemoyer (University of Washington)
CodeOptimizationComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningTextBenchmark
π― What it does: A QLORA method is proposed, which uses a 4-bit quantized model for efficient fine-tuning on a single GPU, maintaining the same performance as 16-bit fine-tuning.
π― What it does: This paper proposes QuantSR, a low-bit (2-4 bit) image super-resolution network that significantly reduces model size and computational load while maintaining high accuracy.