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Found 14 papers, 7 papers with code
Propensity Score Alignment of Unpaired Multimodal Data
Multimodal representation learning techniques typically rely on paired samples to learn common representations, but paired samples are challenging to collect in fields such as biology where measurement devices often destroy the samples.
Object-centric architectures enable efficient causal representation learning
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence class).
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Instrumental variable (IV) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable.
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges.
GFlowNets for AI-Driven Scientific Discovery
However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline.
UNSAT Solver Synthesis via Monte Carlo Forest Search
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversing an exponential-sized tree.
Weakly Supervised Representation Learning with Sparse Perturbations
We show that if the perturbations are applied only on mutually exclusive blocks of latents, we identify the latents up to those blocks.
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning
These results suggest that by exploiting inductive biases on mechanisms, it is possible to design a range of new identifiable representation learning approaches.
The Perils of Learning Before Optimizing
Typically, learning the prediction model used to generate the optimization problem and solving that problem are performed in two separate stages.
Exemplar Guided Active Learning
The challenge is that we are not informed which labels are common and which are rare, and the true label distribution may exhibit extreme skew.
Valid Causal Inference with (Some) Invalid Instruments
The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently.
Deep Models of Interactions Across Sets
In experiments, our models achieved surprisingly good generalization performance on this matrix extrapolation task, both within domains (e. g., new users and new movies drawn from the same distribution used for training) and even across domains (e. g., predicting music ratings after training on movies).
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Deep IV: A Flexible Approach for Counterfactual Prediction
Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables.
Counterfactual Prediction with Deep Instrumental Variables Networks
We are in the middle of a remarkable rise in the use and capability of artificial intelligence.
