Simons Institute | Sublinear-Time Algorithms in Learning @SimonsInstituteTOC | Uploaded 2 months ago | Updated 15 hours ago
Ronitt Rubinfeld (Massachusetts Institute of Technology)
https://simons.berkeley.edu/talks/ronitt-rubinfeld-massachusetts-institute-technology-2024-06-18
Extroverted Sublinear Algorithms

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...

Specification-guided Reinforcement learning

$Improved Bounds for Fully Dynamic Matching via Ordered Ruzsa-Szemeredi Graphs Sepehr Assadi (University of Waterloo) https://simons.berkeley.edu/talks/sepehr-assadi-university-waterloo-2024-07-30 Sublinear Graph Simplification We follow the recent breakthrough of Behnezhad and Ghafari [FOCS24] in designing fully dynamic algorithms for the maximum matching problem whose runtimes is parameterized by the density of the so-called Ordered Ruzsa-Szemeredi (ORS) Graphs. These are graphs whose edges can be partitioned into an ordered set of linear-size matchings such that each matching is induced among the edges of the matchings that appear after it in this ordering. Behnezhad and Ghafari developed an algorithm for (1+eps)-approximation of matchings for any constant eps with update time roughly sqrt{n * ORS(n)}, where ORS(n) denotes the largest number of matchings possible in an n-vertex ORS graph. The parameter ORS(n) is still quite poorly understood and we effectively only know that it is between n^{o(1)} and o(n). Under the plausible scenario that the current lower bounds for ORS(n) are almost optimal, the BG algorithm will run in n^{1/2+o(1)} update time, which will be a major achievement in this area. In this talk, we will cover our recent improvements to the BG-algorithm that improve the update time to only n^{o(1)} * ORS(n). This means that under the plausible scenario above, this algorithm simply runs in n^{o(1)} update time. In its current stage also, this fully reduces the algorithmic problem of designing dynamic matching algorithms to a purely combinatorial problem of upper bounding ORS(n) with no algorithmic considerations.$

Error Embraced: Making Trustworthy Scientific Decisions with Imperfect Predictions

Stochastic Minimum Vertex Cover with Few Queries: a 3/2-approximation

Sublinear-Time Algorithms in Learning @SimonsInstituteTOC