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Christopher Beckham

Alumni

Publications

Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards
Luis Lara
Zhi Hao Luo
Aristides Milios
Aditya Sharma
Ge Ya Luo
Christopher Beckham
Florian Golemo
Christopher Pal
An AI system for professional floor plan design needs to be able to precisely control room dimensions and areas (quantitative constraints), … (voir plus)while also balancing functional considerations and design aesthetics. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans with numerical constraints. We introduce a text‑based floor plan generation approach that fine-tunes a large language model (LLM) on real plans and then applies reinforcement learning with verifiable rewards (RLVR) to enforce both numerical (areas, dimensions) and spatial (topological) constraints. Furthermore, we design a set of constraint adherence metrics to measure how generated floor plans align with user-defined constraints systematically. Our model generates floor plans that satisfy numerical constraints and outperforms existing methods on realism, compatibility, and diversity scores. Specifically, our approach leads to an up to 94\% reduction in compatibility score. Our results demonstrate that LLMs can effectively handle quantitative constraints in structured design tasks, suggesting broader applications for text-based generative modeling.
2025-09-29
NeurIPS.cc/2025/Workshop/UrbanAI (présentation orale)
openreview.net
openreview.net
Robust Guided Diffusion for Offline Black-Box Optimization
Can Chen
Christopher Beckham
Zixuan Liu
Xue Liu
Christopher Pal
Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two … (voir plus)main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.
2024-12-19
TMLR (accepté)
doi.org
openreview.net
Exploring validation metrics for offline model-based optimisation
Christopher Beckham
Alexandre Piché
David Vázquez
Christopher Pal
In offline model-based optimisation (MBO) we are interested in using machine learning to de-sign candidates that maximise some measure of d… (voir plus)esirability through an expensive but real-world scoring process. Offline MBO tries to approximate this expensive scoring function and use that to evaluate generated designs, however evaluation is non-exact because one approximation is being evaluated with another. Instead, we ask ourselves: if we did have the real world scoring function at hand, what cheap-to-compute validation metrics would correlate best with this? Since the real-world scoring function is available for simulated MBO datasets, insights obtained from this can be transferred over to real-world offline MBO tasks where the real-world scoring function is expensive to compute. To address this, we propose a conceptual evaluation framework that is amenable to measuring extrapolation, and apply this to conditional denoising diffusion models. Empirically, we find that two validation metrics – agreement and Frechet distance – correlate quite well with the ground truth. When there is high variability in conditional generation, feedback is required in the form of an approximated version of the real-world scoring function. Furthermore, we find that generating high-scoring samples may require heavily weighting the generative model in favour of sample quality, potentially at the cost of sample diversity.
2024-06-12
TMLR (accepté)
openreview.net
openreview.net
Parallel-mentoring for Offline Model-based Optimization
Can (Sam) Chen
Christopher Beckham
Zixuan Liu
Xue Liu
Christopher Pal
We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These desi… (voir plus)gns encompass a variety of domains, including materials, robots and DNA sequences. A common approach trains a proxy on the static dataset to approximate the black-box objective function and performs gradient ascent to obtain new designs. However, this often results in poor designs due to the proxy inaccuracies for out-of-distribution designs. Recent studies indicate that: (a) gradient ascent with a mean ensemble of proxies generally outperforms simple gradient ascent, and (b) a trained proxy provides weak ranking supervision signals for design selection. Motivated by (a) and (b), we propose \textit{parallel-mentoring} as an effective and novel method that facilitates mentoring among parallel proxies, creating a more robust ensemble to mitigate the out-of-distribution issue. We focus on the three-proxy case and our method consists of two modules. The first module, \textit{voting-based pairwise supervision}, operates on three parallel proxies and captures their ranking supervision signals as pairwise comparison labels. These labels are combined through majority voting to generate consensus labels, which incorporate ranking supervision signals from all proxies and enable mutual mentoring. However, label noise arises due to possible incorrect consensus. To alleviate this, we introduce an \textit{adaptive soft-labeling} module with soft-labels initialized as consensus labels. Based on bi-level optimization, this module fine-tunes proxies in the inner level and learns more accurate labels in the outer level to adaptively mentor proxies, resulting in a more robust ensemble. Experiments validate the effectiveness of our method. Our code is available here.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net
Conservative objective models are a special kind of contrastive divergence-based energy model
Christopher Beckham
Christopher Pal
In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind o… (voir plus)f contrastive divergence-based energy model, one where the energy function represents both the unconditional probability of the input and the conditional probability of the reward variable. While the initial formulation only samples modes from its learned distribution, we propose a simple fix that replaces its gradient ascent sampler with a Langevin MCMC sampler. This gives rise to a special probabilistic model where the probability of sampling an input is proportional to its predicted reward. Lastly, we show that better samples can be obtained if the model is decoupled so that the unconditional and conditional probabilities are modelled separately.
2023-04-06
ArXiv (prépublication)
doi.org
arxiv.org
Score-based Diffusion Models in Function Space
Jae Hyun Lim
Nikola B. Kovachki
R. Baptista
Christopher Beckham
Kamyar Azizzadenesheli
Jean Kossaifi
Vikram Voleti
Jiaming Song
Karsten Kreis
Jan Kautz
Christopher Pal
Arash Vahdat
Animashree Anandkumar
2023-02-13
ArXiv (prépublication)
doi.org
arxiv.org
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
Christopher Beckham
Martin Weiss
Florian Golemo
Sina Honari
Derek Nowrouzezahrai
Christopher Pal
Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Under… (voir plus)standing what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. We explore a controlled setting whereby questions are posed about the properties of a scene if that scene was observed from another viewpoint. To do this we have created a new version of the CLEVR dataset that we call CLEVR Mental Rotation Tests (CLEVR-MRT). Using CLEVR-MRT we examine standard methods, show how they fall short, then explore novel neural architectures that involve inferring volumetric representations of a scene. These volumes can be manipulated via camera-conditioned transformations to answer the question. We examine the efficacy of different model variants through rigorous ablations and demonstrate the efficacy of volumetric representations.
2022-11-29
Pattern Recognition (inconnu)
doi.org
openreview.net
Overcoming Challenges in Leveraging GANs for Few-Shot Data Augmentation
Christopher Beckham
Issam Laradji
Pau Rodríguez
David Vázquez
Derek Nowrouzezahrai
Christopher Pal
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We per… (voir plus)form an exploration into how a GAN can be fine-tuned for such a task (one of which is in a class-incremental manner), as well as a rigorous empirical investigation into how well these models can perform to improve few-shot classification. We identify issues related to the difficulty of training such generative models under a purely supervised regime with very few examples, as well as issues regarding the evaluation protocols of existing works. We also find that in this regime, classification accuracy is highly sensitive to how the classes of the dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning approach as a more pragmatic way forward to address these problems.
2021-12-31
CoLLAs (publié)
doi.org
proceedings.mlr.press
Towards good validation metrics for generative models in offline model-based optimisation
Christopher Beckham
Alexandre Piché
David Vázquez
Christopher Pal
In this work we propose a principled evaluation framework for model-based optimisation to measure how well a generative model can extrapolat… (voir plus)e. We achieve this by interpreting the training and validation splits as draws from their respective ‘truncated’ ground truth distributions, where examples in the validation set contain scores much larger than those in the training set. Model selection is performed on the validation set for some prescribed validation metric. A major research question however is in determining what validation metric correlates best with the expected value of generated candidates with respect to the ground truth oracle; work towards answering this question can translate to large economic gains since it is expensive to evaluate the ground truth oracle in the real world. We compare various validation metrics for generative adversarial networks using our framework. We also discuss limitations with our framework with respect to existing datasets and how progress can be made to mitigate them. 1
2021-12-31
arXiv.org (prépublication)
doi.org
Deep Learning for Detecting Extreme Weather Patterns
Mayur Mudigonda
Mayur Mudigonda, Prabhat Ram
Prabhat Ram
Karthik Kashinath
Evan Racah
Ankur Mahesh
Yunjie Liu
Christopher Beckham
Jim Biard
Thorsten Kurth
Sookyung Kim
S Ebrahimi Kahou
Tegan Maharaj
Burlen Loring
Christopher Pal
Travis O'Brien
K. Kunkel
Kenneth E. Kunkel
M. Wehner
Michael F. Wehner … (voir 2 de plus)
W. Collins
William D. Collins
2021-08-19
Deep Learning for the Earth Sciences (publié)
doi.org
On Adversarial Mixup Resynthesis
Christopher Beckham
Sina Honari
Alex Lamb
Vikas Verma
Farnoosh Ghadiri
R Devon Hjelm
Yoshua Bengio
Christopher Pal
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore mo… (voir plus)dels that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
2018-12-31
NeurIPS (publié)
dblp.uni-trier.de
Adversarial Mixup Resynthesizers
Christopher Beckham
Sina Honari
Alex Lamb
Vikas Verma
Farnoosh Ghadiri
R Devon Hjelm
Christopher Pal
In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore mod… (voir plus)els that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
2018-12-31
DGS@ICLR (publié)
openreview.net
openreview.net