Combining query answering and data science workloads has become prevalent. An important class of such workloads is top-k queries with a scoring function implemented as an opaque UDF - a black box whose internal structure and scores on the search domain are unavailable. Some typical examples include costly calls to fuzzy classification and regression models. The models may also be changed in an ad-hoc manner. Since the algorithm does not know the scoring function's behavior on the input data, opaque top-k queries become expensive to evaluate exactly or speed up by indexing. Hence, we propose an approximation algorithm for opaque top-k query answering. Our proposed solution is a task-independent hierarchical index and a novel bandit algorithm. The index clusters elements by some cheap vector representation then builds a tree of the clusters. Our bandit is a diminishing returns submodular epsilon-greedy bandit algorithm that maximizes the sum of the solution set's scores. Our bandit models the distribution of scores in each arm using a histogram, then targets arms with fat tails. We prove that our bandit algorithm approaches a constant factor of the optimal algorithm. We evaluate our standalone library on large synthetic, image, and tabular datasets over a variety of scoring functions. Our method accelerates the time required to achieve nearly optimal scores by up to an order of magnitude compared to exhaustive scan while consistently outperforming baseline sampling algorithms.

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce C ooler S pace to facilitate physically correct and computationally efficient color programming. C ooler S pace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by C ooler S pace . The type system in C ooler S pace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, C ooler S pace also generates performance-optimized programs using equality saturation. C ooler S pace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. C ooler S pace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized C ooler S pace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.