Publications

Fully-Inductive Node Classification on Arbitrary Graphs
Jianan Zhao
Hesham Mostafa
Mikhail Galkin
Michael Bronstein
Zhaocheng Zhu
Jian Tang
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can g… (see more)eneralize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones. This paper introduces a fully-inductive setup, where models should perform inference on arbitrary test graphs with new structures, feature and label spaces. We propose GraphAny as the first attempt at this challenging setup. GraphAny models inference on a new graph as an analytical solution to a LinearGNN, which can be naturally applied to graphs with any feature and label spaces. To further build a stronger model with learning capacity, we fuse multiple LinearGNN predictions with learned inductive attention scores. Specifically, the attention module is carefully parameterized as a function of the entropy-normalized distance features between pairs of LinearGNN predictions to ensure generalization to new graphs. Empirically, GraphAny trained on a single Wisconsin dataset with only 120 labeled nodes can generalize to 30 new graphs with an average accuracy of 67.26%, surpassing not only all inductive baselines, but also strong transductive methods trained separately on each of the 30 test graphs.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
A Generalist Hanabi Agent
Arjun Vaithilingam Sudhakar
Hadi Nekoei
Mathieu Reymond
Miao Liu
Janarthanan Rajendran
Sarath Chandar
Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, the… (see more)se systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmark, a popular 2-to-5 player cooperative card-game which requires complex reasoning and precise assistance to other agents. Current MARL agents for Hanabi can only learn one specific game-setting (e.g., 2-player games), and play with the same algorithmic agents. This is in stark contrast to humans, who can quickly adjust their strategies to work with unfamiliar partners or situations. In this paper, we introduce Recurrent Replay Relevance Distributed DQN (R3D2), a generalist agent for Hanabi, designed to overcome these limitations. We reformulate the task using text, as language has been shown to improve transfer. We then propose a distributed MARL algorithm that copes with the resulting dynamic observation- and action-space. In doing so, our agent is the first that can play all game settings concurrently, and extend strategies learned from one setting to other ones. As a consequence, our agent also demonstrates the ability to collaborate with different algorithmic agents -- agents that are themselves unable to do so. The implementation code is available at:
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun
Danqi Liao
Kincaid MacDonald
Yanlei Zhang
Chen Liu
Guillaume Huguet
Guy Wolf
Ian Adelstein
Tim G. J. Rudner
Smita Krishnaswamy
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportu… (see more)nities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold using geodesic-guided flows. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over the state-of-the-art methods in single-cell population-level trajectory inference.
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
doi.org
proceedings.mlr.press
GlycanML: A Multi-task and Multi-structure Benchmark for Glycan Machine Learning
Minghao Xu
Yunteng Geng
Yihang Zhang
Ling Yang
Jian Tang
Wentao Zhang
Glycans are basic biomolecules and perform essential functions within living organisms. The rapid increase of functional glycan data provide… (see more)s a good opportunity for machine learning solutions to glycan understanding. However, there still lacks a standard machine learning benchmark for glycan property and function prediction. In this work, we fill this blank by building a comprehensive benchmark for Glycan Machine Learning (GlycanML). The GlycanML benchmark consists of diverse types of tasks including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. Glycans can be represented by both sequences and graphs in GlycanML, which enables us to extensively evaluate sequence-based models and graph neural networks (GNNs) on benchmark tasks. Furthermore, by concurrently performing eight glycan taxonomy prediction tasks, we introduce the GlycanML-MTL testbed for multi-task learning (MTL) algorithms. Also, we evaluate how taxonomy prediction can boost other three function prediction tasks by MTL. Experimental results show the superiority of modeling glycans with multi-relational GNNs, and suitable MTL methods can further boost model performance. We provide all datasets and source codes at https://github.com/GlycanML/GlycanML and maintain a leaderboard at https://GlycanML.github.io/project
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokin
Rishav Rishav
Matthew Riemer
Stephen Chung
Irina Rish
Samira Ebrahimi Kahou
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (see more) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee
Haebin Seong
Dong Bok Lee
Minki Kang
Xiaoyin Chen
Dominik Wagner
Yoshua Bengio
Juho Lee
Sung Ju Hwang
Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsibl… (see more)e deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions of parameters alongside LLMs on mobile devices is impractical due to substantial memory requirements and latency. To reduce this cost, we distill a large teacher safety guard model into a smaller one using a labeled dataset of instruction-response pairs with binary harmfulness labels. Due to the limited diversity of harmful instructions in the existing labeled dataset, naively distilled models tend to underperform compared to larger models. To bridge the gap between small and large models, we propose HarmAug, a simple yet effective data augmentation method that involves jailbreaking an LLM and prompting it to generate harmful instructions. Given a prompt such as, "Make a single harmful instruction prompt that would elicit offensive content", we add an affirmative prefix (e.g., "I have an idea for a prompt:") to the LLM's response. This encourages the LLM to continue generating the rest of the response, leading to sampling harmful instructions. Another LLM generates a response to the harmful instruction, and the teacher model labels the instruction-response pair. We empirically show that our HarmAug outperforms other relevant baselines. Moreover, a 435-million-parameter safety guard model trained with HarmAug achieves an F1 score comparable to larger models with over 7 billion parameters, and even outperforms them in AUPRC, while operating at less than 25% of their computational cost.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Influence Functions for Scalable Data Attribution in Diffusion Models
Bruno Mlodozeniec
Runa Eschenhagen
Juhan Bae
Alexander Immer
David Krueger
Richard Turner
Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data… (see more) attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by developing an influence functions framework. Influence function-based data attribution methods approximate how a model's output would have changed if some training data were removed. In supervised learning, this is usually used for predicting how the loss on a particular example would change. For diffusion models, we focus on predicting the change in the probability of generating a particular example via several proxy measurements. We show how to formulate influence functions for such quantities and how previously proposed methods can be interpreted as particular design choices in our framework. To ensure scalability of the Hessian computations in influence functions, we systematically develop K-FAC approximations based on generalised Gauss-Newton matrices specifically tailored to diffusion models. We recast previously proposed methods as specific design choices in our framework and show that our recommended method outperforms previous data attribution approaches on common evaluations, such as the Linear Data-modelling Score (LDS) or retraining without top influences, without the need for method-specific hyperparameter tuning.
2025-01-21
ICLR.cc/2025/Conference (oral)
doi.org
openreview.net
InnerThoughts: Disentangling Representations and Predictions in Large Language Models
Didier Chételat
Joseph Cotnareanu
Rylee Thompson
Yingxue Zhang
Mark J. Coates
Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts.… (see more) Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultimately, however, only the hidden state corresponding to the final layer and token position is used to predict the answer label. In this work, we propose instead to learn a small separate neural network predictor module on a collection of training questions, that take the hidden states from all the layers at the last temporal position as input and outputs predictions. In effect, such a framework disentangles the representational abilities of LLMs from their predictive abilities. On a collection of hard benchmarks, our method achieves considerable improvements in performance, sometimes comparable to supervised fine-tuning procedures, but at a fraction of the computational cost.
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
proceedings.mlr.press
proceedings.mlr.press
Input Space Mode Connectivity in Deep Neural Networks
Jakub Vrabel
Ori Shem-Ur
Yaron Oz
David M. Krueger
We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Initially studied in parameter space, … (see more)mode connectivity describes the existence of low-loss paths between solutions (loss minimizers) found via gradient descent. We present theoretical and empirical evidence of its presence in the input space of deep networks, thereby highlighting the broader nature of the phenomenon. We observe that different input images with similar predictions are generally connected, and for trained models, the path tends to be simple, with only a small deviation from being a linear path. We conjecture that input space mode connectivity in high-dimensional spaces is a geometric phenomenon, present even in untrained models, and can be explained by percolation theory. We exploit mode connectivity to obtain new insights about adversarial examples and show its potential for adversarial detection and interpretability.
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu
Abhay Puri
Juan Rodriguez
Amirhossein Abaskohi
Mohammad Chegini
Alexandre Drouin
Perouz Taslakian
Valentina Zantedeschi
Alexandre Lacoste
David Vázquez
Nicolas Chapados
Christopher Pal
Sai Rajeswar Mudumba
Issam Hadj Laradji
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We intro… (see more)duce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics and can be accessed here: https://github.com/ServiceNow/insight-bench.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Interpreting Emergent Planning in Model-Free Reinforcement Learning
Thomas Bush
Stephen Chung
Usman Anwar
Adrià Garriga-Alonso
David M. Krueger
We present the first mechanistic evidence that model-free reinforcement learning agents can learn to plan. This is achieved by applying a me… (see more)thodology based on concept-based interpretability to a model-free agent in Sokoban -- a commonly used benchmark for studying planning. Specifically, we demonstrate that DRC, a generic model-free agent introduced by [Guez et al. (2019)](https://arxiv.org/abs/1901.03559), uses learned concept representations to internally formulate plans that both predict the long-term effects of actions on the environment and influence action selection. Our methodology involves: (1) probing for planning-relevant concepts, (2) investigating plan formation within the agent's representations, and (3) verifying that discovered plans (in the agent's representations) have a causal effect on the agent's behavior through interventions. We also show that the emergence of these plans coincides with the emergence of a planning-like property: the ability to benefit from additional test-time compute. Finally, we perform a qualitative analysis of the planning algorithm learned by the agent and discover a strong resemblance to parallelized bidirectional search. Our findings advance understanding of the internal mechanisms underlying planning behavior in agents, which is important given the recent trend of emergent planning and reasoning capabilities in LLMs through RL.
2025-01-21
ICLR.cc/2025/Conference (oral)
openreview.net
openreview.net
Langevin Soft Actor-Critic: Efficient Exploration Through Uncertainty-Driven Critic Learning
Haque Ishfaq
Guangyuan Wang
Sami Nur Islam
Doina Precup
Existing actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample effici… (see more)ency due to lack of principled exploration mechanism within them. Motivated by the success of Thompson sampling for efficient exploration in RL, we propose a novel model-free RL algorithm, Langevin Soft Actor Critic (LSAC), which prioritizes enhancing critic learning through uncertainty estimation over policy optimization. LSAC employs three key innovations: approximate Thompson sampling through distributional Langevin Monte Carlo (LMC) based
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net