Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userUser-Oriented Smart General AI System under Causal Inference
85
0
0.0
(
0
)
Added by
Huimin Peng
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Authors
Huimin Peng
Ask ChatGPT about the research
No Arabic abstract
General AI system solves a wide range of tasks with high performance in an automated fashion. The best general AI algorithm designed by one individual is different from that devised by another. The best performance records achieved by different users are also different. An inevitable component of general AI is tacit knowledge that depends upon user-specific comprehension of task information and individual model design preferences that are related to user technical experiences. Tacit knowledge affects model performance but cannot be automatically optimized in general AI algorithms. In this paper, we propose User-Oriented Smart General AI System under Causal Inference, abbreviated as UOGASuCI, where UOGAS represents User-Oriented General AI System and uCI means under the framework of causal inference. User characteristics that have a significant influence upon tacit knowledge can be extracted from observed model training experiences of many users in external memory modules. Under the framework of causal inference, we manage to identify the optimal value of user characteristics that are connected with the best model performance designed by users. We make suggestions to users about how different user characteristics can improve the best model performance achieved by users. By recommending updating user characteristics associated with individualized tacit knowledge comprehension and technical preferences, UOGAS helps users design models with better performance.
rate research
Read More
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.
Data scarcity is a tremendous challenge in causal effect estimation. In this paper, we propose to exploit additional data sources to facilitate estimating causal effects in the target population. Specifically, we leverage additional source datasets which share similar causal mechanisms with the target observations to help infer causal effects of the target population. We propose three levels of knowledge transfer, through modelling the outcomes, treatments, and confounders. To achieve consistent positive transfer, we introduce learnable parametric transfer factors to adaptively control the transfer strength, and thus achieving a fair and balanced knowledge transfer between the sources and the target. The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target. Experiments on both synthetic and real-world datasets show the effectiveness of the proposed method as compared with recent baselines.
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determining a single directed acyclic graph (DAG) or a Markov equivalence class thereof. However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty. For instance, planning interventions to find out more about the causal mechanisms that govern our data requires quantifying epistemic uncertainty over DAGs. While Bayesian causal inference allows to do so, the posterior over DAGs becomes intractable even for a small number of variables. Aiming to overcome this issue, we propose a form of variational inference over the graphs of Structural Causal Models (SCMs). To this end, we introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. Its number of parameters does not grow exponentially with the number of variables and can be tractably learned by maximising an Evidence Lower Bound (ELBO). In our experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive faulty observation by abrupt interferences such as black-out, frozen-screen, and adversarial perturbation. How to design a resilient DRL algorithm against these rare but mission-critical and safety-crucial scenarios is an important yet challenging task. In this paper, we consider a generative DRL framework training with an auxiliary task of observational interferences such as artificial noises. Under this framework, we discuss the importance of the causal relation and propose a causal inference based DRL algorithm called causal inference Q-network (CIQ). We evaluate the performance of CIQ in several benchmark DRL environments with different types of interferences as auxiliary labels. Our experimental results show that the proposed CIQ method could achieve higher performance and more resilience against observational interferences.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
