Learning Task Informed Abstractions

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by training two models that learn visual features via cooperative reconstruction, but one model is adversarially dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks where natural and unconstrained visual distractions pose a formidable challenge.

Consistent Accelerated Inference via Confident Adaptive Transformers

We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.

Few-shot Conformal Prediction with Auxiliary Tasks

We develop a novel approach to conformal prediction when the target task has limited data available for training. Conformal prediction identifies a small set of promising output candidates in place of a single prediction, with guarantees that the set contains the correct answer with high probability. When training data is limited, however, the predicted set can easily become unusably large. In this work, we obtain substantially tighter prediction sets while maintaining desirable marginal guarantees by casting conformal prediction as a meta-learning paradigm over exchangeable collections of auxiliary tasks. Our conformalization algorithm is simple, fast, and agnostic to the choice of underlying model, learning algorithm, or dataset. We demonstrate the effectiveness of this approach across a number of few-shot classification and regression tasks in natural language processing, computer vision, and computational chemistry for drug discovery.

Modeling Drug Combinations based on Molecular Structures and Biological Targets

Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Since validating drug combinations via direct screening is prohibitively expensive due to combinatorial explosion, recent approaches have applied machine learning to identify synergistic combinations for cancer. However, these approaches is not readily applicable to many diseases with limited combination data. Motivated by the fact that drug synergy is closely tied with biological targets, we propose a model that jointly learns drug-target interaction and drug synergy. The model, parametrized as a graph convolutional network, consists of two parts: a drug-target interaction and target-disease association module. These modules are trained together on drug combination screen as well as abundant drug-target interaction data. Our model is trained and evaluated on two SARS-CoV-2 drug combination screens and achieves 0.777 test AUC, which is 10% higher than the model trained without drug-target interaction.

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph-structured data. While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representational learning in many applications, the neighborhood aggregation paradigm exposes additional vulnerabilities to attackers seeking to extract node-level information about sensitive attributes. To counter this, we propose a minimax game between the desired GNN encoder and the worst-case attacker. The resulting adversarial training creates a strong defense against inference attacks, while only suffering a small loss in task performance. We analyze the effectiveness of our framework against a worst-case adversary, and characterize the trade-off between predictive accuracy and adversarial defense. Experiments across multiple datasets from recommender systems, knowledge graphs and quantum chemistry demonstrate that the proposed approach provides a robust defense across various graph structures and tasks, while producing competitive GNN encoders. Our code is available at https://github.com/liaopeiyuan/GAL.

Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels

Providing a small set of promising candidates in place of a single prediction is well-suited for many open-ended classification tasks. Conformal Prediction (CP) is a technique for creating classifiers that produce a valid set of predictions that contains the true answer with arbitrarily high probability. In practice, however, standard CP can suffer from both low predictive and computational efficiency during inference---i.e., the predicted set is both unusably large, and costly to obtain. This is particularly pervasive in the considered setting, where the correct answer is not unique and the number of total possible answers is high. In this work, we develop two simple and complementary techniques for improving both types of efficiencies. First, we relax CP validity to arbitrary criterions of success---allowing our framework to make more efficient predictions while remaining "equivalently correct." Second, we amortize cost by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---while still guaranteeing marginal coverage. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.

Domain Extrapolation via Regret Minimization

Many real prediction tasks such as molecular property prediction require ability to extrapolate to unseen domains. The success in these tasks typically hinges on finding a good representation. In this paper, we extend invariant risk minimization (IRM) by recasting the simultaneous optimality condition in terms of regret, finding instead a representation that enables the predictor to be optimal against an oracle with hindsight access on held-out environments. The change refocuses the principle on generalization and doesn't collapse even with strong predictors that can perfectly fit all the training data. Our regret minimization (RGM) approach can be further combined with adaptive domain perturbations to handle combinatorially defined environments. We evaluate our method on two real-world applications: molecule property prediction and protein homology detection and show that RGM significantly outperforms previous state-of-the-art domain generalization techniques.

Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-based generative model for molecule to cell image synthesis. To generate cell features at different resolutions and scale to high-resolution images, we develop a novel multi-scale flow architecture based on a Haar wavelet image pyramid. To maximize the mutual information between the generated images and the molecular interventions, we devise a training strategy based on contrastive learning. To evaluate our model, we propose a new set of metrics for biological image generation that are robust, interpretable, and relevant to practitioners. We show quantitatively that our method learns a meaningful embedding of the molecular intervention, which is translated into an image representation reflecting the biological effects of the intervention.