Posters

AI learns to compute Gröbner bases, solving a notorious computational algebra problem efficiently via Transformers and novel algebraic techniques.

Learned Mixture Density Particle Smoother (MDPS) surpasses state-of-the-art for accurate, differentiable city-scale vehicle localization.

AI agents learn to balance helpfulness and self-preservation using empathy to gauge social relationships and guide reward sharing.

AI agents trained with Empowerment via Successor Representations (ESR) empower humans by maximizing their control over environmental outcomes, eliminating the need for human intention inference, unlik…

A novel Decomposition-based Policy Learning (DPPL) method optimally balances short-term and long-term rewards, even with interrelated objectives, by transforming the problem into intuitive subproblems…

Learning latent causal structures improves label noise modeling by accurately estimating noise transition matrices without relying on similarity-based assumptions, leading to state-of-the-art classifi…

Learn infinitesimal generators of stochastic diffusion processes efficiently via a novel energy-based risk functional, overcoming the unbounded nature of the generator and providing learning bounds in…

A novel Common-Points-Picking algorithm efficiently learns stable reward allocations (expected core) in strictly convex stochastic cooperative games with unknown reward distributions, achieving high p…

Learn data symmetries directly from data with flexible weight-sharing using learnable doubly stochastic tensors!

Learn deep Successor Features (SFs) directly from pixels, efficiently and without representation collapse, using a novel, simple method combining TD and reward prediction loss!

HypStructure boosts representation learning by embedding label hierarchies into hyperbolic space, improving accuracy and interpretability.

This research pioneers the integration of machine learning with the dependently-typed programming language Agda, introducing a novel dataset and neural architecture for faithful program representation…

ELSA: a new model that learns spatially aware language and audio embeddings, achieving state-of-the-art performance in semantic retrieval and 3D sound source localization.

Learn graph representations efficiently by identifying the minimal data needed to uniquely define a graph’s structure, achieving robust performance with fewer resources.

Self-supervised learning gets a boost: New objective function trains robust & predictive neural networks by straightening video trajectories, surpassing invariance methods for better spatiotemporal re…

CryptoANFNet accelerates solving cryptographic problems by 50x using a novel graph neural network and ANF representation, outperforming existing methods in accuracy.

Neural networks learn place cell-like representations and context-dependent remapping using a novel similarity-based objective function, providing insights into hippocampal encoding.

Learning hidden structures from sparse interactions in data is computationally hard but can be achieved with sufficient samples using gradient-based methods; This is shown by analyzing the gradient dy…

AI learns optimal taxes for congestion games, maximizing social welfare with limited feedback, via a novel algorithm.

Learned optimal lattice vector quantization (OLVQ) drastically boosts neural image compression efficiency by adapting quantizer structures to latent feature distributions, achieving significant rate-d…