72 citations · 151 across the 16 of their papers we have counts for
16 papers
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
Duncan Soiffer, Chandler Squires, Yuan Guan +2
The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-def…
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw +2
Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biase…
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Thomas Jiralerspong, Berton Earnshaw, Jason Hartford +2
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks. In this work, we aim to build inductiv…
Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne, Jason Hartford, Sébastien Lachapelle +2
Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of c…
Object-centric architectures enable efficient causal representation learning
Amin Mansouri, Jason Hartford, Yan Zhang +1
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence clas…
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Elisabeth Ailer, Jason Hartford, Niki Kilbertus
Instrumental variable (IV) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable. Instr…
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
Lazar Atanackovic, Alexander Tong, Bo Wang +3
One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and their products that control gene expressio…
GFlowNets for AI-Driven Scientific Discovery
Moksh Jain, Tristan Deleu, Jason Hartford +3
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While scien…
UNSAT Solver Synthesis via Monte Carlo Forest Search
Chris Cameron, Jason Hartford, Taylor Lundy +4
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversin…
Weakly Supervised Representation Learning with Sparse Perturbations
Kartik Ahuja, Jason Hartford, Yoshua Bengio
The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge or any source of supervision. Most prior…
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning
Kartik Ahuja, Jason Hartford, Yoshua Bengio
A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work that provably achieves this goal relies…
The Perils of Learning Before Optimizing
Chris Cameron, Jason Hartford, Taylor Lundy +1
Formulating real-world optimization problems often begins with making predictions from historical data (e.g., an optimizer that aims to recommend fast routes relies upon travel-tim…
Exemplar Guided Active Learning
Jason Hartford, Kevin Leyton-Brown, Hadas Raviv +3
We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. We are motivated by the NLP problem of word sense disambiguation. For…
Valid Causal Inference with (Some) Invalid Instruments
Jason Hartford, Victor Veitch, Dhanya Sridhar +1
Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reli…
Deep Models of Interactions Across Sets
Jason Hartford, Devon R Graham, Kevin Leyton-Brown +1
We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonic…
Counterfactual Prediction with Deep Instrumental Variables Networks
Jason Hartford, Greg Lewis, Kevin Leyton-Brown +1
We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: m…