Debugging genomic profiling experiments and predictive models with interpretation tools @SimonsInstituteTOC

Simons Institute | Debugging genomic profiling experiments and predictive models with interpretation tools @SimonsInstituteTOC | Uploaded 3 months ago | Updated 15 hours ago
Anshul Kundaje (Stanford University)
https://simons.berkeley.edu/talks/anshul-kundaje-stanford-university-2024-06-10
AI≡Science: Strengthening the Bond Between the Sciences and Artificial Intelligence

Deep learning models are typically very powerful predictors but also tend to learn latent biases present in training data. Using genomics as a case study, I will show how one can use interpretation frameworks to identify latent confounders, technical biases and artifacts present in experimental data that is learned by models trained naively on these data. I will also show how we can design models that can learn extremely accurate models of these technical biases and automatically correct them resulting in models that have high predictive performance, provide near optimal latent bias correction and the ability to reveal novel insights into fundamental mechanisms of gene regulation. And just for kicks ... our models are also very efficient and robust and we outperform state-of-the-art foundation models.

A Theory of Multi-objective Machine Learning

$Large Scale Private Learning on Data Streams, and the BLTs Abhradeep Guha Thakurta (Google DeepMind) https://simons.berkeley.edu/talks/abhradeep-guha-thakurta-google-deepmind-2024-06-18 Extroverted Sublinear Algorithms In recent years, Differentially Private Follow the Regularized Leader (DP-FTRL) style algorithms have become increasingly popular for training large models over data streams, e.g., in training DP models for Gboard next-word prediction (https://research.google/blog/federated-learning-with-formal-differentia… and https://arxiv.org/abs/2305.18465). At the heart of these algorithms is a correlated noise addition mechanism that factorizes a square lower triangular matrix of all ones (A=BC) and adds noise BZ (with Z ~ N(0,σ^2)^{training steps x dimensions}) to the prefix sum of the gradients observed during training. Prior work required generating and storing the noise matrix in advance, which is prohibitively expensive for large-scale model training. In this talk, we introduce a new algorithmic construction called the Buffered Linear Toeplitz operator (BLT) that allows generating the noise required for DP in a streaming fashion while requiring a buffer size of approximately log^2(training steps) x dimensions. Furthermore, compared to the class of all Lower Triangular Toeplitz (LTT) factorizations (i.e., B and C being LTT), BLTs are only off by an additive constant in terms of utility under standard error metrics. The talk will draw ideas from rational function approximations and constant recurrence sequences for the construction of BLTs. The talk will also demonstrate the practicality of this work through simulation experiments. The talk is primarily based on https://arxiv.org/pdf/2404.16706, and is a joint work with Krishnamurthy (Dj) Dvijotham, Brendan McMahan, Krishna Pillutla, and Thomas Steinke.$

Space is a latent sequence: A theory of hippocampus and PFC

Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree

O(log log n) Passes is Optimal for Semi-Streaming Maximal Independent Set

Towards Understanding Generalization Properties of Score-Based Losses

Prediction, Generalization, Complexity: Revisiting the Classical View from Statistics Part 1

From Simulated Subjectivity to Collective Consciousness in Large Language Models

Re-thinking Transformers: Searching for Efficient Linear Layers over a Continuous Space of...