Author name cluster

Mohan S. Kankanhalli

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

22 papers

2 author rows

ICLR Conference 2025 Conference Paper

Strong Preferences Affect the Robustness of Preference Models and Value Alignment

Ziwei Xu 0001
Mohan S. Kankanhalli

Value alignment, which aims to ensure that large language models (LLMs) and other AI agents behave in accordance with human values, is critical for ensuring safety and trustworthiness of these systems. A key component of value alignment is the modeling of human preferences as a representation of human values. In this paper, we investigate the robustness of value alignment by examining the sensitivity of preference models. Specifically, we ask: how do changes in the probabilities of some preferences affect the predictions of these models for other preferences? To answer this question, we theoretically analyze the robustness of widely used preference models by examining their sensitivities to minor changes in preferences they model. Our findings reveal that, in the Bradley-Terry and the Placket-Luce model, the probability of a preference can change significantly as other preferences change, especially when these preferences are dominant (i.e., with probabilities near zero or one). We identify specific conditions where this sensitivity becomes significant for these models and discuss the practical implications for the robustness and safety of value alignment in AI systems.

ICLR Conference 2024 Conference Paper

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Xilie Xu
Keyi Kong
Ning Liu 0014
Lizhen Cui 0001
Di Wang 0015
Jingfeng Zhang
Mohan S. Kankanhalli

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM’s adversarial robustness. This paper proposes an efficient tool to audit the LLM’s adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions. Our source code is available at https://github.com/GodXuxilie/PromptAttack.

ICLR Conference 2024 Conference Paper

AutoLoRa: An Automated Robust Fine-Tuning Framework

Xilie Xu
Jingfeng Zhang
Mohan S. Kankanhalli

Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial robustness in downstream applications, without requiring a lot of computational resources and collecting significant amounts of data. This paper uncovers an issue with the existing RFT, where optimizing both adversarial and natural objectives through the feature extractor (FE) yields significantly divergent gradient directions. This divergence introduces instability in the optimization process, thereby hindering the attainment of adversarial robustness and rendering RFT highly sensitive to hyperparameters. To mitigate this issue, we propose a low-rank (LoRa) branch that disentangles RFT into two distinct components: optimizing natural objectives via the LoRa branch and adversarial objectives via the FE. Besides, we introduce heuristic strategies for automating the scheduling of the learning rate and the scalars of loss terms. Extensive empirical evaluations demonstrate that our proposed automated RFT disentangled via the LoRa branch (AutoLoRa) achieves new state-of-the-art results across a range of downstream tasks. AutoLoRa holds significant practical utility, as it automatically converts a pre-trained FE into an adversarially robust model for downstream tasks without the need for searching hyperparameters. Our source code is available at [the GitHub](https://github.com/GodXuxilie/RobustSSL_Benchmark/tree/main/Finetuning_Methods/AutoLoRa).

ICLR Conference 2024 Conference Paper

Finetuning Text-to-Image Diffusion Models for Fairness

Xudong Shen
Chao Du
Tianyu Pang
Min Lin
Yongkang Wong
Mohan S. Kankanhalli

The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases. Without interventions, these biases could propagate a skewed worldview and restrict opportunities for minority groups. In this work, we frame fairness as a distributional alignment problem. Our solution consists of two main technical contributions: (1) a distributional alignment loss that steers specific characteristics of the generated images towards a user-defined target distribution, and (2) adjusted direct finetuning of diffusion model's sampling process (adjusted DFT), which leverages an adjusted gradient to directly optimize losses defined on the generated images. Empirically, our method markedly reduces gender, racial, and their intersectional biases for occupational prompts. Gender bias is significantly reduced even when finetuning just five soft tokens. Crucially, our method supports diverse perspectives of fairness beyond absolute equality, which is demonstrated by controlling age to a 75% young and 25% old distribution while simultaneously debiasing gender and race. Finally, our method is scalable: it can debias multiple concepts at once by simply including these prompts in the finetuning data. We share code and various fair diffusion model adaptors at https://sail-sg.github.io/finetune-fair-diffusion/.

ICML Conference 2024 Conference Paper

Improving Context Understanding in Multimodal Large Language Models via Multimodal Composition Learning

Wei Li
Hehe Fan
Yongkang Wong
Yi Yang 0001
Mohan S. Kankanhalli

Previous efforts using frozen Large Language Models (LLMs) for visual understanding, via image captioning or image-text retrieval tasks, face challenges when dealing with complex multimodal scenarios. In order to enhance the capabilities of Multimodal Large Language Models (MLLM) in comprehending the context of vision and language, we introduce Multimodal Composition Learning (MCL) for the purpose of mapping or aligning the vision and language input. In particular, we introduce two tasks: Multimodal-Context Captioning (MC-Cap) and Multimodal-Context Retrieval (MC-Ret) to guide a frozen LLM in comprehending the vision and language context. These specialized tasks are crafted to improve the LLM’s capacity for efficient processing and utilization of multimodal inputs, thereby enhancing its proficiency in generating more accurate text or visual representations. Extensive experiments on both retrieval tasks (i. e. , zero-shot composed image retrieval, visual storytelling image retrieval and visual dialog image retrieval) and text generation tasks (i. e. , visual question answering) demonstrate the effectiveness of the proposed method. The code is available at: https: //github. com/dhg-wei/MCL.

ICLR Conference 2023 Conference Paper

Continuous-Discrete Convolution for Geometry-Sequence Modeling in Proteins

Hehe Fan
Zhangyang Wang
Yi Yang 0001
Mohan S. Kankanhalli

The structure of proteins involves 3D geometry of amino acid coordinates and 1D sequence of peptide chains. The 3D structure exhibits irregularity because amino acids are distributed unevenly in Euclidean space and their coordinates are continuous variables. In contrast, the 1D structure is regular because amino acids are arranged uniformly in the chains and their sequential positions (orders) are discrete variables. Moreover, geometric coordinates and sequential orders are in two types of spaces and their units of length are incompatible. These inconsistencies make it challenging to capture the 3D and 1D structures while avoiding the impact of sequence and geometry modeling on each other. This paper proposes a Continuous-Discrete Convolution (CDConv) that uses irregular and regular approaches to model the geometry and sequence structures, respectively. Specifically, CDConv employs independent learnable weights for different regular sequential displacements but directly encodes geometric displacements due to their irregularity. In this way, CDConv significantly improves protein modeling by reducing the impact of geometric irregularity on sequence modeling. Extensive experiments on a range of tasks, including protein fold classification, enzyme reaction classification, gene ontology term prediction and enzyme commission number prediction, demonstrate the effectiveness of the proposed CDConv.

ICML Conference 2023 Conference Paper

Deep Regression Unlearning

Ayush Kumar Tarun
Vikram Singh Chundawat
Murari Mandal
Mohan S. Kankanhalli

With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models. In this work, we explore unlearning for the regression problem, particularly in deep learning models. Unlearning in classification and simple linear regression has been considerably investigated. However, unlearning in deep regression models largely remains an untouched problem till now. In this work, we introduce deep regression unlearning methods that generalize well and are robust to privacy attacks. We propose the Blindspot unlearning method which uses a novel weight optimization process. A randomly initialized model, partially exposed to the retain samples and a copy of the original model are used together to selectively imprint knowledge about the data that we wish to keep and scrub off the information of the data we wish to forget. We also propose a Gaussian fine tuning method for regression unlearning. The existing unlearning metrics for classification are not directly applicable to regression unlearning. Therefore, we adapt these metrics for the regression setting. We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications. Our methods show excellent performance for all these datasets across all the metrics. Source code: https: //github. com/ayu987/deep-regression-unlearning

NeurIPS Conference 2023 Conference Paper

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection

Xilie Xu
Jingfeng Zhang
Feng Liu
Masashi Sugiyama
Mohan S. Kankanhalli

Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks. However, ACL needs tremendous running time to generate the adversarial variants of all training data, which limits its scalability to large datasets. To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method. RCS does not require label information and searches for an informative subset that minimizes a representational divergence, which is the distance of the representation between natural data and their virtual adversarial variants. The vanilla solution of RCS via traversing all possible subsets is computationally prohibitive. Therefore, we theoretically transform RCS into a surrogate problem of submodular maximization, of which the greedy search is an efficient solution with an optimality guarantee for the original problem. Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the large-scale ImageNet-1K dataset to obtain an effective robust representation via RCS. Our source code is at https: //github. com/GodXuxilie/Efficient ACL via_RCS.

NeurIPS Conference 2023 Conference Paper

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization

Xilie Xu
Jingfeng Zhang
Feng Liu
Masashi Sugiyama
Mohan S. Kankanhalli

Adversarial contrastive learning (ACL) is a technique that enhances standard contrastive learning (SCL) by incorporating adversarial data to learn a robust representation that can withstand adversarial attacks and common corruptions without requiring costly annotations. To improve transferability, the existing work introduced the standard invariant regularization (SIR) to impose style-independence property to SCL, which can exempt the impact of nuisance style factors in the standard representation. However, it is unclear how the style-independence property benefits ACL-learned robust representations. In this paper, we leverage the technique of causal reasoning to interpret the ACL and propose adversarial invariant regularization (AIR) to enforce independence from style factors. We regulate the ACL using both SIR and AIR to output the robust representation. Theoretically, we show that AIR implicitly encourages the representational distance between different views of natural data and their adversarial variants to be independent of style factors. Empirically, our experimental results show that invariant regularization significantly improves the performance of state-of-the-art ACL methods in terms of both standard generalization and robustness on downstream tasks. To the best of our knowledge, we are the first to apply causal reasoning to interpret ACL and develop AIR for enhancing ACL-learned robust representations. Our source code is at https: //github. com/GodXuxilie/Enhancing ACL via_AIR.

ICML Conference 2022 Conference Paper

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

Xilie Xu
Jingfeng Zhang
Feng Liu 0003
Masashi Sugiyama
Mohan S. Kankanhalli

Non-parametric two-sample tests (TSTs) that judge whether two sets of samples are drawn from the same distribution, have been widely used in the analysis of critical data. People tend to employ TSTs as trusted basic tools and rarely have any doubt about their reliability. This paper systematically uncovers the failure mode of non-parametric TSTs through adversarial attacks and then proposes corresponding defense strategies. First, we theoretically show that an adversary can upper-bound the distributional shift which guarantees the attack’s invisibility. Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST’s test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs. To enable TST-agnostic attacks, we propose an ensemble attack (EA) framework that jointly minimizes the different types of test criteria. Second, to robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels. Extensive experiments on both simulated and real-world datasets validate the adversarial vulnerabilities of non-parametric TSTs and the effectiveness of our proposed defense. Source code is available at https: //github. com/GodXuxilie/Robust-TST. git.

NeurIPS Conference 2022 Conference Paper

Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation

Ziwei Xu
Yogesh Rawat
Yongkang Wong
Mohan S. Kankanhalli
Mubarak Shah

We propose Differentiable Temporal Logic (DTL), a model-agnostic framework that introduces temporal constraints to deep networks. DTL treats the outputs of a network as a truth assignment of a temporal logic formula, and computes a temporal logic loss reflecting the consistency between the output and the constraints. We propose a comprehensive set of constraints, which are implicit in data annotations, and incorporate them with deep networks via DTL. We evaluate the effectiveness of DTL on the temporal action segmentation task and observe improved performance and reduced logical errors in the output of different task models. Furthermore, we provide an extensive analysis to visualize the desirable effects of DTL.

ICLR Conference 2021 Conference Paper

Effective Abstract Reasoning with Dual-Contrast Network

Tao Zhuo
Mohan S. Kankanhalli

As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven’s Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the ground truth answer of each question for model learning, aiming for an intelligent agent to have a strong learning capability with a small amount of supervision. Based on the RPM problem formulation, the correct answer filled into the missing entry of the third row/column has to best satisfy the same rules shared between the first two rows/columns.Thus we design a simple yet effective Dual-Contrast Network (DCNet) to exploit the inherent structure of RPM puzzles. Specifically, a rule contrast module is designed to compare the latent rules between the filled row/column and the first two rows/columns; a choice contrast module is designed to increase the relative differences between candidate choices. Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. Code is available at https://github.com/visiontao/dcnet.

ICLR Conference 2021 Conference Paper

Geometry-aware Instance-reweighted Adversarial Training

Jingfeng Zhang
Jianing Zhu
Gang Niu 0001
Bo Han 0003
Masashi Sugiyama
Mohan S. Kankanhalli

In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, whether we can keep the accuracy and improve the robustness, is conceptually and practically more interesting, since robust accuracy should be lower than standard accuracy for any model. In this paper, we show this direction is also promising. Firstly, we find even over-parameterized deep networks may still have insufficient model capacity, because adversarial training has an overwhelming smoothing effect. Secondly, given limited model capacity, we argue adversarial data should have unequal importance: geometrically speaking, a natural data point closer to/farther from the class boundary is less/more robust, and the corresponding adversarial data point should be assigned with larger/smaller weight. Finally, to implement the idea, we propose geometry-aware instance-reweighted adversarial training, where the weights are based on how difficult it is to attack a natural data point. Experiments show that our proposal boosts the robustness of standard adversarial training; combining two directions, we improve both robustness and accuracy of standard adversarial training.

NeurIPS Conference 2021 Conference Paper

Learning to Predict Trustworthiness with Steep Slope Loss

Yan Luo
Yongkang Wong
Mohan S. Kankanhalli
Qi Zhao

Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. Prior efforts have been proven to be reliable on small-scale datasets. In this work, we study the problem of predicting trustworthiness on real-world large-scale datasets, where the task is more challenging due to high-dimensional features, diverse visual concepts, and a large number of samples. In such a setting, we observe that the trustworthiness predictors trained with prior-art loss functions, i. e. , the cross entropy loss, focal loss, and true class probability confidence loss, are prone to view both correct predictions and incorrect predictions to be trustworthy. The reasons are two-fold. Firstly, correct predictions are generally dominant over incorrect predictions. Secondly, due to the data complexity, it is challenging to differentiate the incorrect predictions from the correct ones on real-world large-scale datasets. To improve the generalizability of trustworthiness predictors, we propose a novel steep slope loss to separate the features w. r. t. correct predictions from the ones w. r. t. incorrect predictions by two slide-like curves that oppose each other. The proposed loss is evaluated with two representative deep learning models, i. e. , Vision Transformer and ResNet, as trustworthiness predictors. We conduct comprehensive experiments and analyses on ImageNet, which show that the proposed loss effectively improves the generalizability of trustworthiness predictors. The code and pre-trained trustworthiness predictors for reproducibility are available at \url{https: //github. com/luoyan407/predict_trustworthiness}.

ICLR Conference 2021 Conference Paper

PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences

Hehe Fan
Xin Yu 0002
Yuhang Ding
Yi Yang 0001
Mohan S. Kankanhalli

Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, we propose a point spatio-temporal (PST) convolution to achieve informative representations of point cloud sequences. The proposed PST convolution first disentangles space and time in point cloud sequences. Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension. Furthermore, we incorporate the proposed PST convolution into a deep network, namely PSTNet, to extract features of point cloud sequences in a hierarchical manner. Extensive experiments on widely-used 3D action recognition and 4D semantic segmentation datasets demonstrate the effectiveness of PSTNet to model point cloud sequences.

NeurIPS Conference 2021 Conference Paper

Unsupervised Motion Representation Learning with Capsule Autoencoders

Ziwei Xu
Xudong Shen
Yongkang Wong
Mohan S. Kankanhalli

We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance. MCAE models motion in a two-level hierarchy. In the lower level, a spatio-temporal motion signal is divided into short, local, and semantic-agnostic snippets. In the higher level, the snippets are aggregated to form full-length semantic-aware segments. For both levels, we represent motion with a set of learned transformation invariant templates and the corresponding geometric transformations by using capsule autoencoders of a novel design. This leads to a robust and efficient encoding of viewpoint changes. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets. Notably, it achieves better results than baselines on Trajectory20 with considerably fewer parameters and state-of-the-art performance on the unsupervised skeleton-based action recognition task.

ICML Conference 2020 Conference Paper

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Jingfeng Zhang
Xilie Xu
Bo Han 0003
Gang Niu 0001
Lizhen Cui
Masashi Sugiyama
Mohan S. Kankanhalli

Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. In this paper, we raise a fundamental question{—}do we have to trade off natural generalization for adversarial robustness? We argue that adversarial training is to employ confident adversarial data for updating the current model. We propose a novel formulation of friendly adversarial training (FAT): rather than employing most adversarial data maximizing the loss, we search for least adversarial data (i. e. , friendly adversarial data) minimizing the loss, among the adversarial data that are confidently misclassified. Our novel formulation is easy to implement by just stopping the most adversarial data searching algorithms such as PGD (projected gradient descent) early, which we call early-stopped PGD. Theoretically, FAT is justified by an upper bound of the adversarial risk. Empirically, early-stopped PGD allows us to answer the earlier question negatively{—}adversarial robustness can indeed be achieved without compromising the natural generalization.

ICML Conference 2020 Conference Paper

Inferring DQN structure for high-dimensional continuous control

Andrey Sakryukin
Chedy Raïssi
Mohan S. Kankanhalli

Despite recent advancements in the field of Deep Reinforcement Learning, Deep Q-network (DQN) models still show lackluster performance on problems with high-dimensional action spaces. The problem is even more pronounced for cases with high-dimensional continuous action spaces due to a combinatorial increase in the number of the outputs. Recent works approach the problem by dividing the network into multiple parallel or sequential (action) modules responsible for different discretized actions. However, there are drawbacks to both the parallel and the sequential approaches. Parallel module architectures lack coordination between action modules, leading to extra complexity in the task, while a sequential structure can result in the vanishing gradients problem and exploding parameter space. In this work, we show that the compositional structure of the action modules has a significant impact on model performance. We propose a novel approach to infer the network structure for DQN models operating with high-dimensional continuous actions. Our method is based on the uncertainty estimation techniques introduced in the paper. Our approach achieves state-of-the-art performance on MuJoCo environments with high-dimensional continuous action spaces. Furthermore, we demonstrate the improvement of the introduced approach on a realistic AAA sailing simulator game.

ICML Conference 2019 Conference Paper

Sublinear Time Nearest Neighbor Search over Generalized Weighted Space

Yifan Lei
Qiang Huang
Mohan S. Kankanhalli
Anthony K. H. Tung

Nearest Neighbor Search (NNS) over generalized weighted space is a fundamental problem which has many applications in various fields. However, to the best of our knowledge, there is no sublinear time solution to this problem. Based on the idea of Asymmetric Locality-Sensitive Hashing (ALSH), we introduce a novel spherical asymmetric transformation and propose the first two novel weight-oblivious hashing schemes SL-ALSH and S2-ALSH accordingly. We further show that both schemes enjoy a quality guarantee and can answer the NNS queries in sublinear time. Evaluations over three real datasets demonstrate the superior performance of the two proposed schemes.

TIST Journal 2015 Journal Article

Multi-Keyword Multi-Click Advertisement Option Contracts for Sponsored Search

Bowei Chen
Jun Wang
Ingemar J. Cox
Mohan S. Kankanhalli

In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine’s revenue; and the weak loyalty between advertiser and search engine. In this article, we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to purchase among the prespecified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers’ loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.

ECAI Conference 2014 Conference Paper

No One is Left "Unwatched": Fairness in Observation of Crowds of Mobile Targets in Active Camera Surveillance

Prabhu Natarajan
Bryan Kian Hsiang Low
Mohan S. Kankanhalli

Central to the problem of active multi-camera surveillance is the fundamental issue of fairness in the observation of crowds of targets such that no target is "starved" of observation by the cameras for a long time. This paper presents a principled decision-theoretic multi-camera coordination and control (MC2) algorithm called fair-MC2that can coordinate and control the active cameras to achieve max-min fairness in the observation of crowds of targets moving stochastically. Our fair-MC2algorithm is novel in demonstrating how (a) the uncertainty in the locations, directions, speeds, and observation times of the targets arising from the stochasticity of their motion can be modeled probabilistically, (b) the notion of fairness in observing targets can be formally realized in the domain of multi-camera surveillance for the first time by exploiting the max-min fairness metric to formalize our surveillance objective, that is, to maximize the expected minimum observation time over all targets while guaranteeing a predefined image resolution of observing them, and (c) a structural assumption in the state transition dynamics of a surveillance environment can be exploited to improve its scalability to linear time in the number of targets to be observed during surveillance. Empirical evaluation through extensive simulations in realistic surveillance environments shows that fair-MC2outperforms the state-of-the-art and baseline MC2algorithms. We have also demonstrated the feasibility of deploying our fair-MC2algorithm on real AXIS 214 PTZ cameras.

ICML Conference 2014 Conference Paper

Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes

Trong Nghia Hoang
Bryan Kian Hsiang Low
Patrick Jaillet
Mohan S. Kankanhalli

A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic ε-Bayes-optimal active learning (ε-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily developed myopic/greedy algorithms or performed exploration and exploitation separately. To perform active learning in real time, we then propose an anytime algorithm based on ε-BAL with performance guarantee and empirically demonstrate using synthetic and real-world datasets that, with limited budget, it outperforms the state-of-the-art algorithms.