2k citations · 2.7k across the 55 of their papers we have counts for
29 papers · 1 filter
Training Fair Tabular Foundation Models
Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini +2
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.…
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
Xuemin Yu, Ankur Garg, Samira Ebrahimi Kahou +1
Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information these internal representations capture…
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Ankur Garg, Xuemin Yu, Hassan Sajjad +1
Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer…
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Transformer-based tabular foundation models have recently demonstrated promising in-context learning (ICL) performance on structured data, emerging as competitive alternatives to g…
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokhin, Rishav Rishav, Matthew Riemer +3
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, th…
Adaptive Group Robust Ensemble Knowledge Distillation
Patrik Kenfack, Ulrich Aïvodji, Samira Ebrahimi Kahou
Neural networks can learn spurious correlations in the data, often leading to performance degradation for underrepresented subgroups. Studies have demonstrated that the disparity i…
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
Riadh Azzaz, Valentin Hurel, Patrice Menard +3
The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts th…
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir +3
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the…
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
Jithendaraa Subramanian, Shivakanth Sujit, Niloy Irtisam +4
Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery. Prior approaches have resorted to evolutionary strateg…
Learning to Play Atari in a World of Tokens
Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou
Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate wo…
Spectral Temporal Contrastive Learning
Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou +1
Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, particularly contrastive learning (CL), ha…
Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth, Samira Ebrahimi Kahou
The increasing use of neural networks in various applications has lead to increasing apprehensions, underscoring the necessity to understand their operations beyond mere final pred…
Transformers in Reinforcement Learning: A Survey
Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles +2
Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve performance compared to other neural networks.…
Fairness Under Demographic Scarce Regime
Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available bec…
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning
Nikunj Gupta, Somjit Nath, Samira Ebrahimi Kahou
Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and utilizing a notion of a guar…
Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak +4
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dat…
Discovering Object-Centric Generalized Value Functions From Pixels
Somjit Nath, Gopeshh Raaj Subbaraj, Khimya Khetarpal +1
Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo re…
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein +1
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challen…
Learning Latent Structural Causal Models
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth +5
Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data.…
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi +3
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-eff…
Locally Constrained Representations in Reinforcement Learning
Somjit Nath, Rushiv Arora, Samira Ebrahimi Kahou
The success of Reinforcement Learning (RL) heavily relies on the ability to learn robust representations from the observations of the environment. In most cases, the representation…
Latent Variable Models for Bayesian Causal Discovery
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth +3
Learning predictors that do not rely on spurious correlations involves building causal representations. However, learning such a representation is very challenging. We, therefore,…
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga +1
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in the past. Not all samples carry the sa…
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi +14
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variati…