Author name cluster

Vasileios Lioutas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

ICML Conference 2024 Conference Paper

Nearest Neighbour Score Estimators for Diffusion Generative Models

Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang 0001
Yunpeng Liu 0007
Ke Zhang

Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.

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NeurIPS Conference 2023 Conference Paper

A Diffusion-Model of Joint Interactive Navigation

Matthew Niedoba
Jonathan Lavington
Yunpeng Liu
Vasileios Lioutas
Justice Sefas
Xiaoxuan Liang
Dylan Green
Setareh Dabiri

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN -- a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.

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TMLR Journal 2023 Journal Article

Conditional Permutation Invariant Flows

Berend Zwartsenberg
Adam Scibior
Matthew Niedoba
Vasileios Lioutas
Justice Sefas
Yunpeng Liu
Setareh Dabiri
Jonathan Wilder Lavington

We present a conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation equivariant dynamics. These dynamics are driven by a learnable per-set-element term and pairwise interactions, both parametrized by deep neural networks. We illustrate the utility of this model via applications including (1) complex traffic scene generation conditioned on visually specified map information, and (2) object bounding box generation conditioned directly on images. We train our model by maximizing the expected likelihood of labeled conditional data under our flow, with the aid of a penalty that ensures the dynamics are smooth and hence efficiently solvable. Our method significantly outperforms non-permutation invariant baselines in terms of log likelihood and domain-specific metrics (offroad, collision, and combined infractions), yielding realistic samples that are difficult to distinguish from data.

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ICLR Conference 2023 Conference Paper

Critic Sequential Monte Carlo

Vasileios Lioutas
Jonathan Wilder Lavington
Justice Sefas
Matthew Niedoba
Yunpeng Liu 0007
Berend Zwartsenberg
Setareh Dabiri
Frank Wood

We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpful for planning in environments with hard constraints placed sparsely in time. Compared with previous work, we modify the placement of such heuristic factors, which allows us to cheaply propose and evaluate large numbers of putative action particles, greatly increasing inference and planning efficiency. CriticSMC is compatible with informative priors, whose density function need not be known, and can be used as a model-free control algorithm. Our experiments on collision avoidance in a high-dimensional simulated driving task show that CriticSMC significantly reduces collision rates at a low computational cost while maintaining realism and diversity of driving behaviors across vehicles and environment scenarios.

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TMLR Journal 2022 Journal Article

TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference

Vasileios Lioutas
Adam Scibior
Frank Wood

Models of human driving behavior have long been used for prediction in autonomous vehicles, but recently have also started being used to create non-playable characters for driving simulations. While such models are in many respects realistic, they tend to suffer from unacceptably high rates of driving infractions, such as collisions or off-road driving, particularly when deployed in map locations with road geometries dissimilar to the training dataset. In this paper we present a novel method for fine-tuning a foundation model of human driving behavior to novel locations where human demonstrations are not available which reduces the incidence of such infractions. The method relies on inference in the foundation model to generate infraction-free trajectories as well as additional penalties applied when fine-tuning the amortized inference behavioral model. We demonstrate this "titration" technique using the ITRA foundation behavior model trained on the INTERACTION dataset when transferring to CARLA map locations. We demonstrate a 76-86% reduction in infraction rate and provide evidence that further gains are possible with more computation or better inference algorithms.

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ICML Conference 2020 Conference Paper

Time-aware Large Kernel Convolutions

Vasileios Lioutas
Yuhong Guo

To date, most state-of-the-art sequence modeling architectures use attention to build generative models for language based tasks. Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$. Alternatively, they utilize depthwise convolutions with softmax normalized kernels of size $k$ acting as a limited-window self-attention, resulting in time complexity of $O(k{\cdot}n)$. In this paper, we introduce Time-aware Large Kernel (TaLK) Convolutions, a novel adaptive convolution operation that learns to predict the size of a summation kernel instead of using a fixed-sized kernel matrix. This method yields a time complexity of $O(n)$, effectively making the sequence encoding process linear to the number of tokens. We evaluate the proposed method on large-scale standard machine translation, abstractive summarization and language modeling datasets and show that TaLK Convolutions constitute an efficient improvement over other attention/convolution based approaches.

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