Conditioning by adaptive sampling for robust design (1901.10060v9)

Abstract: We present a new method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest. For example, in protein design, one may wish to find the protein sequence that maximizes fluorescence. We assume access to one or more, potentially black box, stochastic "oracle" predictive functions, each of which maps from input (e.g., protein sequences) design space to a distribution over a property of interest (e.g. protein fluorescence). At first glance, this problem can be framed as one of optimizing the oracle(s) with respect to the input. However, many state-of-the-art predictive models, such as neural networks, are known to suffer from pathologies, especially for data far from the training distribution. Thus we need to modulate the optimization of the oracle inputs with prior knowledge about what makes `realistic' inputs (e.g., proteins that stably fold). Herein, we propose a new method to solve this problem, Conditioning by Adaptive Sampling, which yields state-of-the-art results on a protein fluorescence problem, as compared to other recently published approaches. Formally, our method achieves its success by using model-based adaptive sampling to estimate the conditional distribution of the input sequences given the desired properties.

• The paper introduces CbAS, a method that approximates conditional distributions using adaptive importance sampling to improve design robustness.
• It leverages a coherent statistical framework with generative models to counteract prediction biases in high-dimensional, sparse design spaces.
• Numerical tests in protein fluorescence optimization show that CbAS produces realistic sequences with improved stability compared to existing methods.

Conditioning by Adaptive Sampling for Robust Design

The paper "Conditioning by Adaptive Sampling for Robust Design" addresses the challenges associated with design optimization problems where the goal is to achieve desired properties, such as maximizing a specific attribute of proteins, using black box stochastic predictive functions known as oracles. The main contribution of this research is the development of a novel method—Conditioning by Adaptive Sampling (CbAS)—that leverages model-based adaptive sampling to condition the distribution over the design space to enhance the accuracy and reliability of the predictive models.

Summary of Key Contributions

The authors introduce CbAS as an approach grounded in a coherent statistical framework aimed at approximating a conditional distribution of a prior, leveraging generative models to mitigate the pathological behaviors of predictive models in extrapolation regimes far from training data. The paper posits that directly optimizing oracles may lead to unrealistic sequences, notably in protein design, where the oracle predictions can be unreliable due to biases induced by the distribution constraints inherent to the training data. CbAS counters this by deriving an approximation to the conditional distribution of design inputs on desired properties, thus modulating the optimization process.

Methodological Approach

CbAS employs an iterative scheme based on adaptive importance sampling to handle rare event conditioning, a typical challenge in this design framework due to the sparsity of desired outcomes in high-dimensional design spaces. The methodology draws on concepts from Cross-Entropy Methods (CEM), Evolutionary Distribution Algorithms (EDA), and Information Geometric Optimization (IGO) to ensure robustness. This involves selecting a relaxed conditioning event progressively leading to the desired one, thereby circumventing the inaccuracies that arise when predictive models venture beyond their trained regimes.

Numerical Insights and Results

The paper uses simulations on a toy example and real-world data involving protein fluorescence optimization to demonstrate the efficacy of CbAS relative to existing methods such as Reward Weighted Regression (RWR), Activation Maximization with a VAE prior (AM-VAE), and others. The results suggest that CbAS effectively balances between staying true to realistic input distributions and optimizing for high property values. The method outperforms other approaches by yielding sequences with accurate property measures and improved stability—highlighting the importance of incorporating prior information to regulate exploratory behaviors.

Practical and Theoretical Implications

From a practical perspective, CbAS presents a significant improvement for applications in molecular design and biotechnology, where fidelity to known biochemical structures is critical. The potential to replace expensive experimental cycles with computational oracle-based predictions could streamline processes like protein engineering and drug design.

Theoretically, the paper contributes to understanding how density estimation underlies robust adaptive sampling strategies. In environments where model predictions exhibit high variance in uncharted regions, the informed exploration facilitated by CbAS could influence future developments in optimization algorithms and their applications in AI-related fields.

Future Directions

The paper suggests further explorations into capturing prior uncertainty robustly, potentially by expanding the generative model capabilities to encode richer domain-specific knowledge. Future research could also explore the calibration of model-based density estimations and their nuanced interactions with oracle uncertainty—potentially refining the balance between exploration and exploitation in complex optimization scenarios.

In conclusion, by effectively addressing critical issues in model reliability and offering an innovative conditioning approach, "Conditioning by Adaptive Sampling for Robust Design" provides a meaningful contribution to the toolkit for design and optimization problems in artificial intelligence.