Thank you for your thoughtful feedback. We will first discuss common themes and then specific reviewer comments. Even though ExpO is "simple" (in that it connects existing concepts, albeit in a novel way), we believe We will add a discussion as outlined below. " by Qin et al does not consider interpretability at all. Several methods rely on domain knowledge: "Learning credible . . .

Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks.

Additional Feedback: This paper suggests a reduced-rank regression (RRR) estimator suitable for the high-dimensional n p setting. The estimator is very simple and consists of two steps: (1) reduce X with PCA to Z; (2) do SVD on cross-covariance between Z and Y. The paper claims that this procedure has good statistical guarantees and outpeforms all existing competitors. It develops a detailed mathematical treatment (mostly in the Appendix) to provide some statistical guarantees on the performance. That said, I am not convinced that this paper provides a contribution of NeurIPS level.

Approximate nearest neighbor (ANN) search is a key component in many modern machine learning pipelines; recent use cases include retrieval-augmented generation (RAG) and vector databases. Clustering-based ANN algorithms, that use score computation methods based on product quantization (PQ), are often used in industrial-scale applications due to their scalability and suitability for distributed and disk-based implementations. However, they have slower query times than the leading graph-based ANN algorithms. In this work, we propose a new supervised score computation method based on the observation that inner product approximation is a multivariate (multi-output) regression problem that can be solved efficiently by reduced-rank regression. Our experiments show that on modern high-dimensional data sets, the proposed reduced-rank regression (RRR) method is superior to PQ in both query latency and memory usage. We also introduce LoRANN, a clustering-based ANN library that leverages the proposed score computation method. LoRANN is competitive with the leading graph-based algorithms and outperforms the state-of-the-art GPU ANN methods on high-dimensional data sets.

S. S. Rajamouli was born in 1973, in the South Indian state of Karnataka, to a family from a dominant caste. He learned how to make movies from various odd jobs and apprenticeships, including a years-long stint working for his father, the successful screenwriter Koduri Viswa Vijayendra Prasad. In the past two decades, Rajamouli has earned a reputation among Indian moviegoers for a series of formally ambitious blockbusters, including the spectacular "Baahubali: The Beginning," from 2015, which inspired a new wave of Indian historic epics. But he has found a new level of global success with his latest film, the joyously over-the-top action-fantasy "RRR"--short for "Rise Roar Revolt"--which is among the highest-grossing Indian movies of all time. "RRR" was first released last March but caught on with American viewers over the summer, after an unusual U.S.-wide theatrical rerelease organized by the distributor Variance Films and the film consultant Josh Hurtado.

Multi-regional interaction among neuronal populations underlies the brain's processing of rich sensory information in our daily lives. Recent neuroscience and neuroimaging studies have increasingly used naturalistic stimuli and experimental design to identify such realistic sensory computation in the brain. However, existing methods for cross-areal interaction analysis with dimensionality reduction, such as reduced-rank regression and canonical correlation analysis, have limited applicability and interpretability in naturalistic settings because they usually do not appropriately 'demix' neural interactions into those associated with different types of task parameters or stimulus features (e.g., visual or audio). In this paper, we develop a new method for cross-areal interaction analysis that uses the rich task or stimulus parameters to reveal how and what types of information are shared by different neural populations. The proposed neural demixed shared component analysis combines existing dimensionality reduction methods with a practical neural network implementation of functional analysis of variance with latent variables, thereby efficiently demixing nonlinear effects of continuous and multimodal stimuli. We also propose a simplifying alternative under the assumptions of linear effects and unimodal stimuli. To demonstrate our methods, we analyzed two human neuroimaging datasets of participants watching naturalistic videos of movies and dance movements. The results demonstrate that our methods provide new insights into multi-regional interaction in the brain during naturalistic sensory inputs, which cannot be captured by conventional techniques.