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Found 26 papers, 18 papers with code
API-Net: Robust Generative Classifier via a Single Discriminator
On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure.
A Sample Efficient Conditional Independence Test in the Presence of Discretization
Motivated by this, this paper introduces a sample-efficient CI test that does not rely on the binarization process.
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
We model the user utterance strategies as causal factors, enabling system strategies to be treated as counterfactual actions.
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
Tailoring persuasive conversations to users leads to more effective persuasion.
Type Information-Assisted Self-Supervised Knowledge Graph Denoising
For the specific task of noise detection, the discrepancy between the reconstruction results and the input knowledge graph provides an opportunity for denoising, which is facilitated by the type consistency embedded in our method.
When Selection Meets Intervention: Additional Complexities in Causal Discovery
Even when recognized, the existing paradigm for interventional causal discovery still fails to address it.
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
Some approaches rely on sufficient changes on the distribution of latent variables indicated by auxiliary variables such as domain indices, but acquiring enough domains is often challenging.
Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery.
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Based on the theoretical result, we introduce a novel framework, Causal Temporal Representation Learning with Nonstationary Sparse Transition (CtrlNS), designed to leverage the constraints on transition sparsity and conditional independence to reliably identify both distribution shifts and latent factors.
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
To accommodate Gaussian sources, we develop an identifiability theory that relies on second-order statistics without imposing further preconditions on the distribution of sources, by introducing novel assumptions on the connective structure from sources to observed variables.
On the Parameter Identifiability of Partially Observed Linear Causal Models
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed.
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
To address these problems, we propose MAMA, a new approach to learning video-language representations by utilizing a contrastive objective with a subtractive angular margin to regularize cross-modal representations in their effort to reach perfect similarity.
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications.
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
Topic Modeling as Multi-Objective Contrastive Optimization
Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective.
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization.
DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query.
Temporally Disentangled Representation Learning under Unknown Nonstationarity
In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure.
Effective Neural Topic Modeling with Embedding Clustering Regularization
Topic models have been prevalent for decades with various applications.
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.
InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information.
Certified Robustness Against Natural Language Attacks by Causal Intervention
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
Towards Robustness Against Natural Language Word Substitutions
Robustness against word substitutions has a well-defined and widely acceptable form, i. e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing.
