Publications

Learning Successor Features the Simple Way
Raymond Chua
Arna Ghosh
Christos Kaplanis
Blake Richards
Doina Precup
In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference … (voir plus)in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, wherein representations degenerate and fail to capture meaningful variations in the data. More recent methods for learning SFs can avoid representation collapse, but they often involve complex losses and multiple learning phases, reducing their efficiency. We introduce a novel, simple method for learning SFs directly from pixels. Our approach uses a combination of a Temporal-difference (TD) loss and a reward prediction loss, which together capture the basic mathematical definition of SFs. We show that our approach matches or outperforms existing SF learning techniques in both 2D (Minigrid), 3D (Miniworld) mazes and Mujoco, for both single and continual learning scenarios. As well, our technique is efficient, and can reach higher levels of performance in less time than other approaches. Our work provides a new, streamlined technique for learning SFs directly from pixel observations, with no pretraining required.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Listenable Maps for Zero-Shot Audio Classifiers
Francesco Paissan
Luca Della Libera
Mirco Ravanelli
Cem Subakan
Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthines… (voir plus)s of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context), which, to the best of our knowledge, is the first decoder-based post-hoc interpretation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes a novel loss function that maximizes the faithfulness to the original similarity between a given text-and-audio pair. We provide an extensive evaluation using the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions in a zero-shot classification context. Moreover, we qualitatively show that our method produces meaningful explanations that correlate well with different text prompts.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Do LLMs Build World Representations? Probing Through the Lens of State Abstraction
Zichao Li
Yanshuai Cao
Jackie Cheung
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Many-Shot In-Context Learning
Rishabh Agarwal
Avi Singh
Lei M Zhang
Bernd Bohnet
Luis Rosias
Stephanie C.Y. Chan
Ankesh Anand
Zaheer Abbas
Biao Zhang
Azade Nova
John D Co-Reyes
Eric Chu
Feryal Behbahani
Aleksandra Faust
Hugo Larochelle
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, w… (voir plus)ithout any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples – the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated outputs. To mitigate this limitation, we explore two new settings: (1) "Reinforced ICL" that uses model-generated chain-of-thought rationales in place of human rationales, and (2) "Unsupervised ICL" where we remove rationales from the prompt altogether, and prompts the model only with domain-specific inputs. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. We demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases, can learn high-dimensional functions with numerical inputs, and performs comparably to supervised fine-tuning. Finally, we reveal the limitations of next-token prediction loss as an indicator of downstream ICL performance.
2024-09-25
NeurIPS.cc/2024/Conference (spotlight)
doi.org
openreview.net
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
Aniket Rajiv Didolkar
Anirudh Goyal
Nan Rosemary Ke
Siyuan Guo
Michal Valko
Timothy P Lillicrap
Danilo Jimenez Rezende
Yoshua Bengio
Michael Curtis Mozer
Sanjeev Arora
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Multi-Scale Representation Learning for Protein Fitness Prediction
Zuobai Zhang
Pascal Notin
Yining Huang
Aurelie Lozano
Vijil Chenthamarakshan
Debora Susan Marks
Payel Das
Jian Tang
Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of func… (voir plus)tional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. While initial protein representation learning studies solely focused on either sequence or structural features, recent hybrid architectures have sought to merge these modalities to harness their respective strengths. However, these sequence-structure models have so far achieved only incremental improvements when compared to the leading sequence-only approaches, highlighting unresolved challenges effectively leveraging these modalities together. Moreover, the function of certain proteins is highly dependent on the granular aspects of their surface topology, which have been overlooked by prior models. To address these limitations, we introduce the Sequence-Structure-Surface Fitness (**S3F**) model — a novel multimodal representation learning framework that integrates protein features across several scales. Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology. The proposed method achieves state-of-the-art fitness prediction on the ProteinGym benchmark encompassing 217 substitution deep mutational scanning assays, and provides insights into the determinants of protein function. Our code is at https://github.com/DeepGraphLearning/S3F.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Normalization and effective learning rates in reinforcement learning
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
James Martens
Hado van Hasselt
Razvan Pascanu
Will Dabney
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Offline Multitask Representation Learning for Reinforcement Learning
Haque Ishfaq
Thanh Nguyen-Tang
Songtao Feng
Raman Arora
Mengdi Wang
Ming Yin
Doina Precup
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung
Lynn Cherif
Doina Precup
David Meger
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Periodic agent-state based Q-learning for POMDPs
Amit Sinha
Matthieu Geist
Aditya Mahajan
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Predicting Future Actions of Reinforcement Learning Agents
Stephen Chung
Scott Niekum
David Scott Krueger
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
QGFN: Controllable Greediness with Action Values
Elaine Lau
Stephen Zhewen Lu
Ling Pan
Doina Precup
Emmanuel Bengio
Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating … (voir plus)diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate,
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net