34 citations · 150 across the 65 of their papers we have counts for
46 papers · 1 filter
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
Likelihood Hacking in Probabilistic Program Synthesis
Jacek Karwowski, Younesse Kaddar, Zihuiwen Ye +2
When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing program…
Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces
Arran Carter, Sanghyeok Choi, Kirill Tamogashev +2
Sampling from a distribution known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the…
A Comedy of Estimators: On KL Regularization in RL Training of LLMs
Vedant Shah, Johan Obando-Ceron, Vineet Jain +10
The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involve…
Forgetting is Everywhere
Ben Sanati, Thomas L. Lee, Trevor McInroe +4
A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data. Addressing this problem requires a principle…
Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, Víctor Elvira +2
This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between seque…
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
Oskar Kviman, Kirill Tamogashev, Nicola Branchini +3
Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajec…
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal +9
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time co…
Data-to-Energy Stochastic Dynamics
Kirill Tamogashev, Esmeralda S. Whitammer
The Schrödinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certain transportation cost. This proble…
Adaptive Destruction Processes for Diffusion Samplers
Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev +5
This paper explores the challenges and benefits of a trainable destruction process in diffusion samplers -- diffusion-based generative models trained to sample an unnormalised dens…
Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning
Marco Jiralerspong, Esther Derman, Danilo Vucetic +5
A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a small set of promising candidates with d…
On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning
Magdalena Proszewska, Nikolay Malkin, N. Siddharth
Diffusion autoencoders (DAs) are variants of diffusion generative models that use an input-dependent latent variable to capture representations alongside the diffusion process. The…
Solving Bayesian inverse problems with diffusion priors and off-policy RL
Luca Scimeca, Siddarth Venkatraman, Moksh Jain +14
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically sol…
Learning Decision Trees as Amortized Structure Inference
Mohammed Mahfoud, Ghait Boukachab, Michał Koziarski +4
Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translating to better performance, and generalizi…
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal, Yoshua Bengio, Nikolay Malkin +1
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random variables, incorporating priors and updating…
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman, Mohsin Hasan, Minsu Kim +5
Any well-behaved generative model over a variable can be expressed as a deterministic transformation of an exogenous ('outsourced') Gaussian noise variable $\mathbf{z}…
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
Julius Berner, Lorenz Richter, Marcin Sendera +2
We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing m…
Action abstractions for amortized sampling
Oussama Boussif, Léna Néhale Ezzine, Joseph D Viviano +6
As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challeng…
Adaptive teachers for amortized samplers
Minsu Kim, Sanghyeok Choi, Taeyoung Yun +7
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is in…
On Generalization for Generative Flow Networks
Anas Krichel, Nikolay Malkin, Salem Lahlou +1
Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an unnormalized probability distribution, ca…
Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman, Moksh Jain, Luca Scimeca +12
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable…
Discrete Probabilistic Inference as Control in Multi-path Environments
Tristan Deleu, Padideh Nouri, Nikolay Malkin +2
We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to find a stochastic policy such that objec…
V-STaR: Training Verifiers for Self-Taught Reasoners
Arian Hosseini, Xingdi Yuan, Nikolay Malkin +3
Common self-improvement approaches for large language models (LLMs), such as STaR, iteratively fine-tune LLMs on self-generated solutions to improve their problem-solving ability.…
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose +9
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem…