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Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang · Bo Dai · Negar Kiyavash · Niao He

Wed Dec 07:45 AM -- 09:45 AM PST @ Room 210 #6

Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable. Sampling-based ABC algorithms such as rejection- and K2-ABC are inefficient when the parameters have high dimensions, while the regression-based algorithms such as K- and DR-ABC are hard to scale. In this paper, we introduce an optimization-based ABC framework that addresses these deficiencies. Leveraging a generative model for posterior and joint distribution matching, we show that ABC can be framed as saddle point problems, whose objectives can be accessed directly with samples. We present the predictive ABC algorithm (P-ABC), and provide a probabilistically approximately correct (PAC) bound that guarantees its learning consistency. Numerical experiment shows that P-ABC outperforms both K2- and DR-ABC significantly.

Author Information

Yingxiang Yang (University of Illinois at Urbana Champaign)

PhD student at ECE/UIUC.

Bo Dai (Google Brain)
Negar Kiyavash (Georgia Tech)
Niao He (UIUC)

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