927 citations · 2.7k across the 88 of their papers we have counts for
90 papers
Lloyd's -Means Clustering Algorithm Is Frank-Wolfe in Disguise
Michael Pokojovy, J. Marcus Jobe, Simon Lacoste-Julien
Lloyd's -means algorithm, also known as naïve -means, is a widely used ad hoc optimization heuristic, designed to minimize the sum of squared errors (SSE) across all -part…
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Vitória Barin Pacela, Vitória Barin-Pacela, Shruti Joshi +3
The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under superposition, this encoding is a projecti…
Operationalizing Quantized Disentanglement
Vitoria Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien +1
Recent theoretical work established the unsupervised identifiability of quantized factors under any diffeomorphism. The theory assumes that quantization thresholds correspond to ax…
Tight Lower Bounds and Improved Convergence in Performative Prediction
Pedram Khorsandi, Rushil Gupta, Mehrnaz Mofakhami +2
Performative prediction is a framework accounting for the shift in the data distribution induced by the prediction of a model deployed in the real world. Ensuring rapid convergence…
Performative Prediction on Games and Mechanism Design
António Góis, Mehrnaz Mofakhami, Fernando P. Santos +2
Agents often have individual goals which depend on a group's actions. If agents trust a forecast of collective action and adapt strategically, such prediction can influence outcome…
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma +4
This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed…