Taemin Park

Hyuck Lee, Taemin Park, Heeyoung Kim

Various algorithms have been proposed to address the challenges posed by class-imbalanced learning from real-world data with long-tailed distributions. While these algorithms reduce prediction bias through rebalancing techniques, they often introduce increased prediction variance as a trade-off. Several multi-expert learning algorithms aim to address this variance but involve complex procedures. We propose a new multi-expert learning algorithm, called the dual-axis multi-expert learning (DAMEL), which reduces both bias and variance of predictions by using multiple experts along both representation and time axes. Along the representation axis, DAMEL concatenates the representations of multiple experts and trains an auxiliary balanced classifier simultaneously with the concatenated representations. Along the time axis, DAMEL aggregates network weights across training epochs, employing these aggregated weights during testing. Experimental results demonstrate that DAMEL reduces both bias and variance of predictions, highlighting its effectiveness in class-imbalanced learning.

Julian Lettner, Dokyung Song, Taemin Park et al.

Sanitizers can detect security vulnerabilities in C/C++ code that elude static analysis. Current practice is to continuously fuzz and sanitize internal pre-release builds. Sanitization-enabled builds are rarely released publicly. This is in large part due to the high memory and processing requirements of sanitizers. We present PartiSan, a run-time partitioning technique that speeds up sanitizers and allows them to be used in a more flexible manner. Our core idea is to partition the execution into sanitized slices that incur a run-time overhead, and unsanitized slices running at full speed. With PartiSan, sanitization is no longer an all-or-nothing proposition. A single build can be distributed to every user regardless of their willingness to enable sanitization and the capabilities of their host system. PartiSan can automatically adjust the amount of sanitization to fit within a performance budget or disable sanitization if the host lacks sufficient resources. The flexibility afforded by run-time partitioning also means that we can alternate between different types of sanitizers dynamically; today, developers have to pick a single type of sanitizer ahead of time. Finally, we show that run-time partitioning can speed up fuzzing by running the sanitized partition only when the fuzzer discovers an input that causes a crash or uncovers new execution paths.