arXiv research

Method scales up ML science by measuring multiple molecules at once.

Proposes using entity embedding vectors to improve Gaussian Process models for knowledge transfer across cell lines.

Improved scaffold generation for protein motifs using SE(3) flow matching.

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms or cell lines. Therefore, computational prediction methods are needed to address these is…

Active learning speeds up antibody affinity prediction.

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-o…

Bayesian optimization of antibodies learns from immune system evolution.

A new algorithm optimizes multiple molecular properties efficiently.

P3BO optimizes biological sequence design by combining multiple methods.

CoDrug uses KDE to create valid prediction sets for drug molecules under covariate shift.

PropEn uses matching to create a larger dataset for efficient design optimization.

PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI pr…

Efficiently optimize GPs by reusing candidate solutions multiple times.

This paper uses bandit theory and Thompson Sampling to optimize protein sequences.

The evolution of drug-resistant microbial species is one of the major challenges to global health. The development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemica…

A new method prioritizes and recycles experiences for better reinforcement learning.

This project combines recent advances in experience replay techniques, namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER), and Hindsight Experience Replay (HER). We show the results of combinations of these techniques with DDPG and DQN methods. CER always adds the most recent experience to th…

ReaPER improves learning efficiency by prioritizing reliable experiences.

Algorithm creates synthetic experiences to enhance Deep Reinforcement Learning.

This paper describes an improvement in Deep Q-learning called Reverse Experience Replay (also RER) that solves the problem of sparse rewards and helps to deal with reward maximizing tasks by sampling transitions successively in reverse order. On tasks with enough experience for training and enough Experience Replay mem…

Unified model optimizes experiment performance and reduces duration.

New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may requi…

Optimal tests developed for sequential experiments with asymptotic properties.

Bayesian optimization for long-term outcomes using fast and slow experiments.

Two methods estimate effect size for online experiments, improving accuracy and efficiency.

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule-based replay strategy, which may be sub-optimal. In this work, we consid…

Study finds real-world datasets contain natural experiments that can improve model performance.

We reduce variance in monetization metrics for ranking experiments.

AES improves policy gradient performance by adaptively selecting experience.

New framework for choosing optimal proxy metrics from past experiments.

Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more efficiently, researchers proposed prioritized experience replay (PER) which samples important transitions more frequently. In this paper, we propo…

LiDER refreshes past experiences in RL by dreaming about them.

We analyze the sample complexity of learning from multiple experiments where the experimenter has a total budget for obtaining samples. In this problem, the learner should choose a hypothesis that performs well with respect to multiple experiments, and their related data distributions. Each collected sample is associat…

The paper improves reinforcement learning stability and efficiency with a new theoretical framework.

Optimal adaptive experiment for choosing best treatment with binary outcomes.

We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of `covariates' and the associated `outcomes'. While similar experiments can be retrieved by comparing available `annotations', this approach ignores the valuable information ava…

Expands Bayesian experiment design framework to account for model discrepancies.

ER-GNN uses experience replay to prevent GNNs from forgetting previous tasks.

The paper addresses statistical inference issues in adaptive experiments.

Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gai…

New insights into experience replay in RL algorithms.

We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data. Since experiments are inherently noisy and…

Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.

The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves …

Estimates effect sizes and power from a pilot experiment.