6 citations · 37 across the 16 of their papers we have counts for
12 papers · 1 filter
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
Alvin Heng, Harold Soh
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.…
Generative Modeling with Flow-Guided Density Ratio Learning
Alvin Heng, Abdul Fatir Ansari, Harold Soh
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradie…
Safety-Constrained Policy Transfer with Successor Features
Zeyu Feng, Bowen Zhang, Jianxin Bi +1
In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints.…
Observed Adversaries in Deep Reinforcement Learning
Eugene Lim, Harold Soh
In this work, we point out the problem of observed adversaries for deep policies. Specifically, recent work has shown that deep reinforcement learning is susceptible to adversarial…
SCALES: From Fairness Principles to Constrained Decision-Making
Sreejith Balakrishnan, Jianxin Bi, Harold Soh
This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (C…
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle +5
Many complex time series can be effectively subdivided into distinct regimes that exhibit persistent dynamics. Discovering the switching behavior and the statistical patterns in th…