π― What it does: ASPEN is proposed, a DNN inference framework based on fine-grained tile-level dynamic scheduling, eliminating traditional operator synchronization barriers to achieve opportunistic parallelism.
Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment
Tianhe Wu (Shenzhen International Graduate School Tsinghua University), Yujiu Yang (Shenzhen International Graduate School Tsinghua University)
CodeTransformerImage
π― What it does: This paper proposes a multi-sequence network called Assessor360 for blind quality assessment of panoramic images under no-reference conditions.
Assumption violations in causal discovery and the robustness of score matching
Francesco Montagna (University of Genoa), Francesco Locatello (Institute of Science and Technology Austria)
CodeScore-based ModelTabular
π― What it does: A systematic evaluation of eleven causal discovery methods under observational IID data was conducted, with a particular focus on scenarios of assumption violations;
π― What it does: This paper proposes an asynchronous collaborative perception framework called CoBEVFlow based on Bird's Eye View (BEV) flow, which can dynamically compensate for asynchronous information caused by communication delays and clock discrepancies in multi-agent systems such as vehicles and robots, thereby improving perception accuracy.
ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
BjΓΆrn Deiseroth (Aleph Alpha), Kristian Kersting (Technical University Darmstadt)
CodeExplainability and InterpretabilityComputational EfficiencyTransformerLarge Language ModelVision Language ModelTextMultimodality
π― What it does: This paper proposes ATMAN, a memory-efficient explanation method for generative Transformers, which generates a relevance map of input to output by utilizing scalar perturbations of attention scores.
π― What it does: This paper proposes a dual-layer adaptive framework ATTA for dense OOD detection in semantic segmentation under domain shift conditions.
Ben Chugg (Carnegie Mellon University), Aaditya Ramdas (Carnegie Mellon University)
CodeTabularFinance Related
π― What it does: A non-parametric sequential hypothesis testing method based on game theory has been developed for continuously monitoring the group fairness of deployed classification and regression models in real-time auditing.
Rohan Alur (Massachusetts Institute of Technology), Dennis Shung (Yale University)
CodeBiomedical DataElectronic Health Records
π― What it does: A statistical testing method based on conditional independence (ExpertTest) has been developed to detect whether human experts incorporate valuable information beyond observable features;
π― What it does: This paper proposes an augmented perception self-supervised discriminator that can predict augmentation parameters while simultaneously distinguishing between real and fake data, thereby improving the training efficiency of GANs in scenarios with limited data.
π― What it does: A dense contrastive knowledge distillation method Af-DCD has been developed for efficient semantic segmentation network distillation, without data augmentation and without memory buffering.
π― What it does: This paper proposes a framework based on a 3D self-decoder and a 3D diffusion model for learning and generating static and dynamic 3D assets from 2D images or monocular videos.
π― What it does: In this paper, the authors propose an automated computation graph optimization framework called AutoGO, which achieves the goal of significantly reducing FLOPs and hardware latency while maintaining or improving task performance by segmenting, mutating, and evaluating hardware friendliness of the low-level computation graph of neural networks.
π― What it does: An automated error classification framework is proposed for fine-grained classification of model errors on ImageNet and evaluation of error distribution across different models.
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Zhiqi Bu (Amazon Web Services), George Karypis (Amazon Web Services)
CodeOptimizationSafty and PrivacyImageTextTabular
π― What it does: A technique called Automatic Clipping is proposed, which eliminates the need to adjust the clipping threshold R in DP training. It directly implements gradient clipping using a normalization method for each sample's gradient (with a stability constant Ξ³), compatible with various DP optimizers.
Automatic Integration for Spatiotemporal Neural Point Processes
Zihao Zhou (University of California San Diego), Rose Yu (University of California San Diego)
CodePoint CloudTime Series
π― What it does: Proposes the AutoSTPP automatic integration framework, which utilizes a dual network to achieve precise likelihood estimation of three-dimensional space-time point processes.
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings
Pulkit Verma (Arizona State University), Siddharth Srivastava (Arizona State University)
CodeOptimizationExplainability and InterpretabilityRobotic IntelligenceReinforcement LearningAgentic AISequential
π― What it does: This paper proposes an interactive query-based algorithm QACE for learning interpretable probability transition models in black-box sequential decision systems, helping users assess the capabilities of AI systems.
BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
Bin Huang (Tsinghua University), Zhi Wang (Tsinghua University)
CodeObject TrackingAdversarial AttackVideo
π― What it does: This paper proposes a backdoor attack (BadTrack) for visual object tracking (VOT) models that only poisons the data during the training phase. By embedding a trigger in the background of video frames, the tracker maintains performance under normal inputs but loses tracking when the trigger appears.
π― What it does: This paper studies the application of Dynamic Sparse Training (DST) in Generative Adversarial Networks (GANs) and proposes the Balance Ratio (BR) metric to measure the balance between the generator and discriminator. Based on this, the ADAPT algorithm is designed to achieve balanced training of sparse GANs.
Mo Tiwari (Stanford University), Martin Jinye Zhang (Carnegie Mellon University)
CodeOptimizationComputational EfficiencyImageText
π― What it does: This paper presents BanditPAM++, an improved version of the k-medoids clustering algorithm that significantly enhances running efficiency while maintaining clustering quality comparable to BanditPAM and PAM.
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization
Shu Tew, Daniel F. Schmidt (Monash University)
CodeComputational EfficiencyHyperparameter SearchTabularTime Series
π― What it does: A Bayesian ridge regression-based Expectation-Maximization (EM) algorithm is proposed for quickly and accurately tuning the regularization hyperparameter Ξ» without the need for grid search, along with complete theoretical and implementation details.
Shuyi Li (Arizona State University), Shiwei Lan (Arizona State University)
CodeImageTime SeriesComputed TomographyFinance Related
π― What it does: Proposes the q-exponential process (Q-EP) as a Bayesian prior for Lq regularization, addressing the limitations of GP and Besov in high-dimensional function spaces;
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Frederik Rahbæk Warburg (Technical University of Denmark), Søren Hauberg (Technical University of Denmark)
CodeRetrievalContrastive LearningImage
π― What it does: A Bayesian metric learning method based on Laplace approximation (LAM) is proposed, treating the contrastive loss as the negative log-likelihood on the sphere and deriving a positive definite Hessian approximation;
π― What it does: A Bayesian nonparametric non-renewal process (NPNR) is proposed for modeling instantaneous fluctuations and dependencies in neural spike time series.
Bayesian Optimization with Cost-varying Variable Subsets
Sebastian Shenghong Tay (National University of Singapore), Bryan Kian Hsiang Low (National University of Singapore)
CodeOptimization
π― What it does: Proposed and solved the Bayesian optimization problem with controllable variable subsets that can be selected in each round of experiments, where each subset has different costs (BOCVS).
π― What it does: We propose and implement BayesTune, an automatic sparse fine-tuning framework based on Bayesian inference, which determines which base model parameters need to be updated using posterior scale values, reducing the need for manual design and additional modules.
Mingyuan Zhou (University of Texas at Austin), Huangjie Zheng (University of Texas at Austin)
CodeGenerationData SynthesisDiffusion modelImage
π― What it does: Beta Diffusion is proposed, utilizing a multiplicative Beta process for training and sampling generative models within the range of 0 to 1.
Beyond Confidence: Reliable Models Should Also Consider Atypicality
Mert Yuksekgonul (Stanford University), Carlos Guestrin
CodeClassificationAnomaly DetectionLarge Language ModelImageTextBiomedical Data
π― What it does: This paper proposes using the atypicality of samples and categories to supplement traditional confidence, in order to more comprehensively measure and enhance the reliability and uncertainty quantification of models.
π― What it does: This paper conducts a large-scale systematic evaluation of various Bayesian Deep Learning (BDL) algorithms on real distribution shift tasks, covering single-mode posterior, deep ensemble, multi-mode ensemble (MultiX), and fine-tuning of pre-trained models, with a focus on generalization, calibration, and posterior approximation quality.
π― What it does: A new decentralized learning topology, BASE-(k+1) graph, is proposed, which can achieve finite-time convergence under any number of nodes and maximum degree k, while balancing fast consensus rate and low communication cost.
π― What it does: A dynamic similarity analysis (DSA) method is proposed to compare the similarity of neural networks or neural circuits at the level of temporal dynamics.
Chenze Shao (Chinese Academy of Sciences), Yang Feng (Chinese Academy of Sciences)
CodeGenerationOptimizationTransformerLarge Language ModelSupervised Fine-TuningText
π― What it does: Proposes a training objective based on convex functions, allowing text generation models to learn sharper probability distributions, covering autoregressive, non-autoregressive models, and large models;
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
Wang Xinrui, Songcan Chen (Nanjing University of Aeronautics and Astronautics)
CodeClassificationAnomaly DetectionImageTabularAlzheimer's DiseaseFinance Related
π― What it does: This paper proposes a positive-unlabeled learning method based on the trend of prediction scores for positive and negative samples, utilizing positive sample resampling, time point process analysis, and trend score segmentation to automatically label unlabeled data and complete binary classification.
Beyond Normal: On the Evaluation of Mutual Information Estimators
PaweΕ CzyΕΌ (ETH Zurich), Alexander Marx (ETH Zurich)
CodeMultimodalityBenchmark
π― What it does: This paper proposes a method for constructing diverse distributions and provides a language-independent benchmarking platform for mutual information estimators, systematically evaluating the performance of commonly used estimators.
π― What it does: Proposes a Noiseless Image Modeling (NIM) framework and implements a tunable denoising adversarial defense method De3 based on a pre-trained decoder.
Beyond probability partitions: Calibrating neural networks with semantic aware grouping
Jia-Qi Yang (Nanjing University), Le Gan (Nanjing University)
CodeOptimizationConvolutional Neural NetworkImage
π― What it does: This study investigates the problem of probability calibration in deep networks, proposing a Partitioned Calibration Error (PCE) framework. It divides the input space into different subsets through a learned semantic-aware grouping function, applying temperature scaling within each subset to achieve better calibration.
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Zhang-Wei Hong (Massachusetts Institute of Technology), Pulkit Agrawal
CodeReinforcement Learning
π― What it does: This study investigates the conservativeness issue of offline reinforcement learning in the context of imbalanced datasets, proposing a resampling method that learns state-action density ratio weights, focusing only on high-reward samples, and integrating it as a plug-in module with existing offline RL algorithms.
Lukas Eisenmann (Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University), Daniel Durstewitz (Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University)
CodeRecurrent Neural NetworkTime Series
π― What it does: This study investigates the bifurcation phenomenon in training processes of ReLU-based recurrent neural networks (RNNs) and the resulting loss jumps. It proposes a search algorithm (SCYFI) that can accurately locate fixed points and periodic solutions, and uses bifurcation theory to mathematically prove gradient explosion/vanishing.
π― What it does: A novel 1-bit binarization scheme (Binarized Neural Machine Translation, BMT) is implemented in the Transformer machine translation model, binarizing weights and activations while maintaining translation quality.
π― What it does: This paper studies a hyperspectral image recovery method in spectral compressed imaging (SCI) based on Binarized Neural Networks (BNN), proposing the BiSRNet model that can be deployed on resource-constrained devices.
π― What it does: The BIOT model is proposed to achieve cross-data learning, capable of handling multi-channel, varying lengths, and missing values of biological signals.
Alberto Bietti (Flatiron Institute), Leon Bottou (Facebook AI Research Meta)
CodeTransformerText
π― What it does: This paper constructs a synthetic dataset based on a binary model to study how Transformers learn global and contextual knowledge during training, revealing the 'induction head' mechanism formed by two layers of Transformers.
Black-box Backdoor Defense via Zero-shot Image Purification
Yucheng Shi (University of Georgia), Ninghao Liu (University of Georgia)
CodeAdversarial AttackDiffusion modelImage
π― What it does: A black-box backdoor defense framework named ZIP (Zero-shot Image Purification) is proposed, which first disrupts the trigger through linear transformations (such as blurring) and then utilizes a pre-trained diffusion model to restore semantic information, resulting in purified images.
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing
Dongxu Li (Salesforce AI Research), Steven Hoi
CodeGenerationData SynthesisTransformerVision Language ModelDiffusion modelImageTextMultimodality
π― What it does: This paper proposes the BLIP-Diffusion model, which integrates the BLIP-2 visual-language encoder with a latent diffusion model to achieve controllable text-to-image generation and editing based on pre-trained topic representations.
Jonathan Pilault (Google DeepMind), Ross Goroshin (Google DeepMind)
CodeTransformerLarge Language ModelTextSequential
π― What it does: Proposes the Block-State Transformer (BST), which integrates State Space Models with Block Transformers for long sequence language modeling, achieving a fully parallelized layer structure.
π― What it does: Proposes Blockwise Parallel Transformers (BPT), which significantly reduces the memory consumption of Transformers by employing block-level parallel computation on self-attention and feedforward networks, supporting longer input sequences;
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Zhijin Ge (Xidian University), Yuanyuan Liu (Xidian University)
CodeAdversarial AttackImage
π― What it does: This paper studies how to enhance the cross-model transferability of adversarial attacks, proposing the PGN attack method that penalizes the gradient norm to position adversarial samples at flat local maxima.
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
Hee-Youl Kwak (University of Ulsan), Jong-Seon No (Seoul National University)
CodeSupervised Fine-Tuning
π― What it does: This paper addresses the error floor phenomenon of LDPC codes and proposes a three-stage training framework: first, using a benchmark decoder to learn the sponge effect, and then training a post-decoder with uncorrected codewords; it employs block-level training with backpropagation to alleviate gradient vanishing; and designs weight sharing using only two types of weights (satisfying/not satisfying check nodes) to significantly reduce the number of parameters.
π― What it does: This paper proposes a non-interpolative framework that utilizes kernel correction and self-expressive affinity learning methods to directly construct a high-quality similarity matrix on missing data, thereby enhancing the performance of spectral clustering.
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Minsu Kim (Korea Advanced Institute of Science and Technology), Jinkyoo Park (Korea Advanced Institute of Science and Technology)
CodeGenerationOptimizationDrug DiscoveryRecurrent Neural NetworkReinforcement LearningScore-based ModelSequentialBiomedical Data
π― What it does: A score-conditioned generator (BOOTGEN) based on bootstrapped training is proposed for offline optimization of biological sequences (DNA, RNA, proteins) to maximize a given black-box scoring function.
CodeGenerationRetrievalTransformerLarge Language ModelPrompt EngineeringVision Language ModelContrastive LearningImageTextMultimodality
π― What it does: A new training framework is proposed: first, train the Prompt-Former (P-Former) using single-modal text data to generate ideal soft prompts, and then align visual features with these prompts during VL pre-training, thereby improving visual-language pre-training based on frozen large language models.
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Leonard Papenmeier (Lund University), Matthias Poloczek (Amazon)
CodeOptimizationTabular
π― What it does: This study investigates the reliability issues of high-dimensional Bayesian optimization in combinatorial and mixed spaces, and proposes the Bounce algorithm, which can adaptively bin, nest embeddings, and dynamically manage trust regions.
π― What it does: Theoretical and empirical analysis of the high-dimensional latent space of pre-trained unsupervised diffusion models is conducted, proposing the BoundaryDiffusion method for learning free single-step semantic control, which directly utilizes frozen DDMs to perform image editing, text-driven editing, and unconditional semantic control.
Break It Down: Evidence for Structural Compositionality in Neural Networks
Michael A. Lepori (Brown University), Ellie Pavlick (Brown University)
CodeTransformerImageText
π― What it does: This paper explores whether neural networks can automatically decompose complex tasks into subprograms without explicit symbolic mechanisms, and implement these subprograms within sub-networks, referred to as structural compositionality.
π― What it does: Theoretical research on gradient estimation methods for discrete latent variables is conducted, proving that Straight-Through (ST) is a first-order Euler approximation. Based on this, a new second-order accurate estimator, ReinMax, is proposed and its effectiveness is validated across various tasks.
Bridging RL Theory and Practice with the Effective Horizon
Cassidy Laidlaw (University of California), Anca Dragan (University of California)
CodeReinforcement LearningTabular
π― What it does: This study constructs the BRIDGE dataset (155 deterministic MDPs) and proposes a new complexity measureβeffective horizonβto explain and predict the sample complexity of deep reinforcement learning algorithms (such as PPO and DQN); it also designs a GREedy Over Random Policy (GORP) algorithm based on stochastic rollout as a theoretical tool.
π― What it does: By utilizing various foundational models (such as SAM, Grounding DINO, BLIP, CLIP, etc.) to generate semantic masks, image captions, and textual information, a self-supervised pre-training of 3D point clouds is conducted, forming the Bridge3D framework.
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
Jan DubiΕski (Warsaw University of Technology), Adam Dziedzic (CISPA Helmholtz Center for Information Security)
CodeSafty and PrivacyRepresentation LearningAdversarial AttackContrastive LearningImage
π― What it does: A proactive defense framework named B4B is proposed, which can real-time prevent model theft attacks on the public Encoder API, with almost no impact on the representation quality for legitimate users.
CodeReinforcement LearningGaussian SplattingTime Series
π― What it does: A neural decoding method that does not rely on spike sorting is proposed, which directly utilizes spike localization and waveform features extracted from high-density multi-electrode probes for behavioral decoding.
π― What it does: This paper proposes a training-time calibration method for Transformer-based object detectors called Cal-DETR. It estimates uncertainty by calculating variance between decoder layers, modulates the class logits using this uncertainty, and performs mixup-style blending in the logit space as a regularization technique to enhance the confidence calibration and detection performance of the detector.
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
Dingmin Wang (University of Oxford), Yeyuan Chen (University of Michigan)
CodeClassificationGraph Neural NetworkGraphTime Series
π― What it does: This study investigates the logical expressiveness of R2-GNN on multi-relational graphs and temporal graphs, proving that its original form cannot cover all FOC2 formulas, and proposes a linear-time graph transformation F to enhance expressiveness; it also establishes a hierarchy of expressiveness for multi-relational and temporal graphs, conducting node classification experiments on synthetic and real data.
π― What it does: This paper proposes a differentiable coverage probability regularization method for calibrating the posterior distribution of neural networks in simulation-based inference (SBI) to avoid overconfidence.
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
Charles Thomas Marx (Stanford University), Stefano Ermon (Stanford University)
CodeOptimizationTabularAgriculture Related
π― What it does: A trainable calibration metric based on Maximum Mean Discrepancy (MMD) is proposed, unifying various calibration forms and using it as a regularization term to train probability prediction models.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Bernard Ghanem (King Abdullah University of Science and Technology)
CodeTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextChain-of-Thought
π― What it does: A role-playing (Role-Playing) framework is proposed, utilizing autonomous communication agents to complete tasks with minimal human input, and generating large-scale dialogue data for studying multi-agent collaboration and LLM capabilities through this framework.
π― What it does: This paper proposes the CaMP (Causal Multi-Policy Planning) framework, which combines multi-policy and causal inference to address the interactive navigation problem in multi-room scenarios.
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang (Xi'an Jiaotong University), Yulia Tsvetkov (University of Washington)
CodeGraph Neural NetworkLarge Language ModelPrompt EngineeringTextGraphBenchmarkChain-of-Thought
π― What it does: The NLGraph benchmark is proposed to evaluate the reasoning ability of large language models on graph problems described in natural language, and two instruction-based prompting methods, Build-a-Graph and Algorithmic Prompting, are introduced for graph reasoning.
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization
Swarnadeep Saha (University of North Carolina), Mohit Bansal (University of North Carolina)
CodeTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
π― What it does: This paper studies how to enable a large language model (Teacher) to teach a weaker language model (Student) to complete reasoning tasks through natural language explanations, exploring when, how, and whether it can improve student performance, and verifying that misleading teachers may lead to performance degradation.
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data
Boris van Breugel (University of Cambridge), Mihaela van der Schaar (University of Cambridge)
CodeData SynthesisDomain AdaptationAnomaly DetectionOptimizationGenerative Adversarial NetworkTabularSequentialBiomedical DataFinance Related
π― What it does: Proposes the 3S Testing framework, which evaluates the performance of models under small subgroups and distribution shifts by generating synthetic test sets.
π― What it does: We propose Canonical Manifold Flow (CMF), which incorporates L1 regularization on the off-diagonal elements of the metric tensor in flow models to enforce the learning of sparse and approximately orthogonal eigenbases, thereby achieving more compact latent representations.
π― What it does: A weight correlation-based unstructured pruning method called CAP is proposed for high-precision vision models (such as ViT, ConvNext, etc.), along with an efficient fine-tuning process.
CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
Yulei Qin (Tencent), Rongrong Ji (Xiamen University)
CodeDomain AdaptationRepresentation LearningTransformerLarge Language ModelContrastive LearningImageTextMultimodality
π― What it does: This paper proposes the CAPro framework, which utilizes cross-modal (image and text) aligned prototypes for unsupervised visual representation learning of web data, and enhances model robustness through noise removal and collective bootstrapping.
CAST: Cross-Attention in Space and Time for Video Action Recognition
Dongho Lee (Kyung Hee University), Jinwoo Choi (Kyung Hee University)
CodeRecognitionTransformerVideo
π― What it does: This paper proposes a two-stream video action recognition framework named CAST (Cross-Attention in Space and Time), which utilizes frozen spatial experts (CLIP) and temporal experts (VideoMAE) to exchange information through cross-attention in a bottleneck adapter, achieving a balanced spatiotemporal representation of videos and making collaborative predictions.
Wendong Liang (Max Planck Institute for Intelligent Systems), Bernhard SchΓΆlkopf (Max Planck Institute for Intelligent Systems)
CodeFlow-based Model
π― What it does: This paper studies the Causal Component Analysis (CauCA) framework, exploring the identifiability of latent variables and mixed functions using multiple sets of intervention data under the premise of a known causal graph.
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
Jacy Reese Anthis (University of Chicago), Victor Veitch (University of Chicago)
CodeTabular
π― What it does: This paper establishes a bridge between counterfactual fairness and robust prediction, group fairness through causal context, proposing that under specific causal structures, counterfactual fair predictors can achieve the best accuracy for an unbiased target distribution while corresponding to different group fairness metrics.
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Siyuan Guo (University of Cambridge), Ferenc HuszΓ‘r (University of Cambridge)
CodeTabular
π― What it does: A method for causal structure identification based on exchangeable data is proposed, and the statistical validity of the ICM (Independent Causal Mechanism) assumption is proven through the Causal de Finetti theorem.
Causal discovery from observational and interventional data across multiple environments
Adam Li (Columbia University), Elias Bareinboim (Columbia University)
CodeBiomedical Data
π― What it does: This paper proposes a method for learning causal structures by simultaneously utilizing observational and interventional data in a multi-domain environment, defining concepts such as S-Markov properties and S-PAG, and providing an implementable S-FCI algorithm.
π― What it does: A non-parametric causal discovery algorithm based on proxy variables is proposed, capable of fully identifying causal structures under subsampled time series (where the observation frequency is lower than the causal influence frequency).
Shanyun Gao (Purdue University), Murat Kocaoglu (Purdue University)
CodeTime Series
π― What it does: A non-parametric constrained causal discovery algorithm PCMCI β¦ is proposed for semi-stationary time series data, capable of identifying causal graphs under periodically changing causal mechanisms.
CodeRecommendation SystemExplainability and InterpretabilityTransformerText
π― What it does: This paper views the self-attention mechanism of the Transformer as the total effect matrix of a linear Gaussian structural causal model (SCM), proposing an ABCD method based on the attention matrix to achieve zero-shot causal structure learning for a single input sequence, and further developing the CLEANN algorithm to provide causal explanations for model predictions from the learned causal graph.
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Xiangyu Sun (Simon Fraser University), Oliver Schulte (Simon Fraser University)
CodeFlow-based ModelTabular
π― What it does: This study investigates the robustness of the maximum likelihood (ML) method and the independence test (IT) method in causal inference under the location-scale noise model (LSNMs). It proposes an IT method based on affine flow and validates its superior performance on various synthetic and real datasets under noise distribution misjudgment and conditional variance misguidance.
π― What it does: This study investigates the numerical discrepancies in inference results when using the same trained CNN model and identical inputs across multiple platforms (75 CPUs and GPUs) and their causes;
π― What it does: A general context imitation learning framework CEIL is proposed, which can approximate expert behavior in various imitation learning scenarios.
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
TaeHo Yoon (Seoul National University), Ernest K. Ryu (Seoul National University)
CodeGenerationData SynthesisReinforcement Learning from Human FeedbackDiffusion modelImage
π― What it does: Conducting review sampling on pre-trained diffusion models, utilizing minimal human feedback to train a reward model for suppressing image generation.
π― What it does: This paper proposes the first framework for achieving provable robustness in Graph Contrastive Learning (GCL) - Randomized Edgedrop Smoothing (RES), which obtains provable robustness against graph structure attacks by randomly dropping edges during training and inference.
π― What it does: This paper proposes a differentiable regularization method to explicitly maximize the safety margin of deep classifiers in the input space, thereby enhancing robustness against adversarial perturbations.
π― What it does: This work proposes Chanakya, a learning-based execution framework that automatically determines runtime parameters such as input resolution, model, and inference step length in a real-time perception pipeline, maximizing detection accuracy and minimizing latency while maintaining real-time performance (streaming perception paradigm).
Zhongjie Yu (TU Darmstadt), Kristian Kersting (TU Darmstadt)
CodeTabular
π― What it does: This paper proposes and implements a new probabilistic circuit modelβCharacteristic Circuits (CCs)βto unify the modeling of the joint distribution of discrete and continuous heterogeneous data in the spectral domain (characteristic function) and provides learnable structures and parameters.
Characterization and Learning of Causal Graphs with Small Conditioning Sets
Murat Kocaoglu (Purdue University)
CodeGraphTabular
π― What it does: In the case of limited samples, this study investigates the use of conditional independence tests that only include condition sets of size no greater than k to learn causal graphs, and proposes k-Markov equivalence, k-closure graphs, and the corresponding k-PC learning algorithm.
Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu (Carnegie Mellon University), Katia P. Sycara
CodeDomain AdaptationOptimizationImage
π― What it does: This paper proposes a label-free out-of-distribution (OOD) performance prediction method based on optimal transport, called COT, along with its threshold variant COTT. It provides a conservative and more accurate estimate of the model's error on external distributions under the assumption of unchanged label distribution.
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Zhimeng Jiang (Texas A&M University), Xia Hu (Texas A&M University)
CodeDomain AdaptationOptimizationTabular
π― What it does: This paper proposes a robust fair regularization method (RFR) based on model weight perturbation, achieving fairness transfer under distribution shift conditions.
Chatting Makes Perfect: Chat-based Image Retrieval
Matan Levy (Hebrew University of Jerusalem), Dani Lischinski (Hebrew University of Jerusalem)
CodeRetrievalTransformerLarge Language ModelVision Language ModelContrastive LearningImageTextMultimodality
π― What it does: ChatIR is proposed, a system that continuously refines retrieval queries through interaction with users to ultimately retrieve target images.
CodeComputational EfficiencyTransformerLarge Language ModelTextBenchmark
π― What it does: This paper proposes a cost model and idealized runtime metrics for inference with autoregressive Transformer models, demonstrating how to efficiently estimate inference efficiency and costs through a small number of benchmarks, enabling comparable analysis across different models and APIs.
Jialv Zou (Horizon Robotics), Chang Huang (Horizon Robotics)
CodeTransformerPoint Cloud
π― What it does: Abstracts the components in circuit design as point clouds, directly encoding the original node coordinates using Transformer to output multi-scale grid features for congestion and DRC violation prediction;
CLadder: Assessing Causal Reasoning in Language Models
Zhijing Jin (ETH Zurich), Bernhard SchΓΆlkopf (Max Planck Institute for Intelligent Systems)
CodeTransformerLarge Language ModelPrompt EngineeringTextBenchmarkChain-of-Thought
π― What it does: This paper presents the CLADDER dataset and the CAUSALCOT prompting strategy for evaluating the formal causal reasoning capabilities of large language models.
π― What it does: A class distribution-aware pseudo-labeling method (CAP) is proposed for semi-supervised multi-label learning, which generates pseudo-labels by setting thresholds for each class and estimating class distributions using labeled data, thereby making full use of unlabeled samples.
π― What it does: A continuous learning method CLeAR is proposed for abstract logical reasoning tasks, addressing issues such as input-task decoupling, dynamic dimensions, and OOD generalization.