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Found 14 papers, 3 papers with code
Deconfounding Reinforcement Learning in Observational Settings
Using this benchmark, we demonstrate that the proposed algorithms are superior to traditional RL methods in confounded environments with observational data.
ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by leveraging the power of off-the-shelf text-to-image generation methods (e. g., Stable Diffusion).
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
For the first time, the human-level performance in face verification (97. 53%) on LFW is surpassed.
Flexible Spatio-Temporal Networks for Video Prediction
We describe a modular framework for video frame prediction.
Interpreting Spatially Infinite Generative Models
In this paper we provide a firm theoretical interpretation for infinite spatial generation, by drawing connections to spatial stochastic processes.
Invariant Causal Representation Learning
As an alternative, we propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution generalization in the nonlinear setting (i. e., nonlinear representations and nonlinear classifiers).
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
We propose counterfactual RL algorithms to learn both population-level and individual-level policies.
Nonlinear Invariant Risk Minimization: A Causal Approach
Finally, in the discussion, we further explore the aforementioned assumption and propose a more general hypothesis, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes.
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods.
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Distribution-consistency Structural Causal Models
In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stand as the predominant frameworks.
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Within our framework, we devise a causal graph to elucidate the predictions of MLLMs on VQA problems, and assess the causal effect of biases through an in-depth causal analysis.
